Secret agents - TU Delft Repositories

Secret agentsAn exploratory study to the added value of

Agent-Based modeling to the Strategic

Geopolitical Intelligence process of NATO

state intelligence services

S. H

. C

hri

sti

aan

Men

kveld

1312472

1312472

Challenge the future

On the cover: Protesters in Egypt - 29 January 2011 - image adopted from the Huffington Post.

Colophon

Report details

Report title Secret agentsSub title An exploratory study to the added value of Agent-Based modeling

to the Strategic Geopolitical Intelligence processReport type Msc. Thesis

Author Christiaan MenkveldStudent no. 1312472

E-mail [email protected]

Programme Systems Engineering, Policy Analysis and Management (SEPAM)Section Safety Science GroupFaculty Technology, Policy and Management (TPM)

University Delft University of Technology (TU Delft)

Graduation committee

Chair Prof.dr. B.J.M. Ale - TU Delft, Safety Science GroupFirst supervisor Dr.ir. C. van Gulijk - TU Delft, Safety Science Group

Second supervisor Dr.ir. I. Nikolic - TU Delft, Energy & Industry section

Part I

Preamble

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Preface

This thesis is the result of a 6 month research performed for my master thesis graduation. It isalso a symbol of the knowledge I have obtained throughout my bachelor and master study onSystems Engineering, Policy Analysis and Management at the Delft University of Technology.This explicitly includes my semester on International Relations and Security at the NationalUniversity of Singapore, and implicitly includes my experiences with the DUT Racing team,Goud.Mijn, the Toercie and all other great experiences of being a student surrounded by greatfriends.

I am dedicating this thesis to my parents who made all the above possible. Bedankt pap enmam! Hereby I would also like to thank my sister who truly understands the meaning ofbrother-sisterhood and has always been there for me.

In addition I would like to thank my supervision committee who allowed me to wander off intoa whole new area of science and also because they did not mind to read through the next 250pages. Especially I would like to thank Dr.ir. Coen van Gulijk who, as the first supervisor,made sure I did not got lost wondering into the jungle I call my brain. Furthermore I am verythankful for all the experts who contributed to my thesis via interviews, e-mails and the survey.This interaction, especially with the intelligence service experts, brought my project more intouch with reality and therefore very interesting to do.

Christiaan Menkveld

Delft, July 2013

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Executive Summary

The events of the Arab spring reminds us how individual citizens have the power to overthrowpowerful regimes and destabilize complete regions. The dynamics of such bottom-up instigatedrevolutions seem to be in line with the (Social) Constructivism paradigm. The problem though,is that this paradigm lacks a powerful forecasting method that could have identified the Arabspring as a possible future event on beforehand. However the similar Complex Adaptive Systemsparadigm does have a powerful forecasting tool; Agent-Based modeling. After an exploratoryliterature study we discovered that it is unlikely that Agent-Based modeling is applied by NATOstate intelligence services for their forecasts on geopolitical dynamics on a strategic level. Thisdespite our expectations that Agent-Based modeling would be a lot more powerful in analyzingstrategic geopolitical dynamics than the current methods of NATO state intelligence services. Toconfirm our expectations we performed an exploratory study to; How Agent-Based modeling canadd value to the Strategic Geopolitical Intelligence process of NATO state intelligence services?

Based on a literature study and interviews with experts we have first identified;

• What makes Agent-Based modeling suitable for the Strategic Geopolitical Intelligenceprocess, compared to other methods;

• How currently the Strategic Geopolitical Intelligence process is executed by NATO stateintelligence services; and

• Which criteria should be used to compare the value of different Strategic GeopoliticalIntelligence processes.

The criteria were used to identify limitations of the current Strategic Geopolitical Intelligenceprocess. For each of these limitations we checked whether Agent-Based modeling would besuitable to overcome (partly) the limitations. This resulted in the following list of how Agent-Based modeling can add value to the Strategic Geopolitical Intelligence process:

• Risk scenarios can be built in line with the Complex Adaptive Systems paradigm, therebyincreasing the external validity of analyses of volatile states;

• A high number of risk scenarios can be taken into account, thereby decreasing the likelihoodof unexpected events and allowing the development of more detailed scenarios;

• Sensitivity analyses can be executed to assess intelligence, to take into account the un-certainty of intelligence, identify critical unknowns, determine de-warning indicators fornegative warnings, prioritize indicators in the intelligence collection plan, provide band-width values for relevant critical unknowns and develop more accurate conditional conclu-sions. This allows for more intelligence to be used, providing more focus in the intelligencegathering process and be explicit about the uncertainty of the conclusions;

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• The intelligence analyst can gain a better understanding of the dynamics of the systemunder study, thereby increasing the transferability of the analysis as well as the externalvalidity in the long run.

• The Strategic Geopolitical Intelligence process will become more quantitative, therebymaking the process more objective and increase internal validity, reliability and confirma-bility;

• Explicit integration between near term- and mid/long term assessments can be realized,due to same detail the Agent-Based model will provide for both types of scenarios. Therebymaking the process less costly, increase internal validity, reliability and confirmability; and

• (Adaptive) Policies can be deducted and developed based on the risk scenario set, therebymaking more effective and pro-active policies.

In order to evaluate our own findings and provide a starting point for NATO to introduce Agent-Based modeling into their doctrine, we have designed a new Strategic Geopolitical Intelligenceprocess that utilizes all these benefits of Agent-Based modeling. Based on literature and ourown experience with executing a large part of this process on an actual case, we are confidentthat it is possible to implement our new Strategic Geopolitical Intelligence process design intopractice. However we have also discovered that integrating Agent-Based modeling into theStrategic Geopolitical Intelligence process can significantly extend the duration of the analysis.Therefore it depends on the case and the available time for the analysis, whether it is sensibleto apply the new Agent-Based modeling driven Strategic Geopolitical Intelligence process or thetraditional Strategic Geopolitical Intelligence process. We especially recommend that based onour thesis, some research will be done to;

• Increase the validity of our findings;

• Identify under which circumstances it is still practical to use Agent-Based modeling in theStrategic Geopolitical Intelligence process; and

• Iteratively improve in practice our new Strategic Geopolitical Intelligence process.

Keywords: Agent-Based modeling - Complex Adaptive Systems - Methodology -Strategic Geopolitical Intelligence process - Volatile states

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Contents

I Preamble 5

II Main Thesis 15

1 Main introduction of the thesis 161.1 The volatile world and the opportunity to improve intelligence analysis . . . . . . 161.2 State-of-art description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171.3 The goals of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181.4 The research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181.5 Social and scientific relevance of this thesis . . . . . . . . . . . . . . . . . . . . . 191.6 Reading guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2 Methods 212.1 Introducing the methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212.2 Chosen methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212.3 Description of methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3 Results on sub question 1:What makes Agent-Based modeling suitable for Strategic Geopolitical Intelli-gence compared to other analysis methods? 283.1 (Node 1) Qualitative methods versus Quantitative methods . . . . . . . . . . . . 293.2 (Node 2) Replication versus Extrapolation . . . . . . . . . . . . . . . . . . . . . . 303.3 (Node 3) Open-form models versus Closed-form models . . . . . . . . . . . . . . 303.4 (Node 4) Deductive (top-down) approach versus Inductive (bottom-up) approach 323.5 (Node 5) Solving a problem versus Studying a problem versus Studying individual

agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323.6 Validation of the aspects that makes Agent-Based modeling suitable for Strategic

Geopolitical Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333.7 Notes on Agent-Based modeling for the potential user / policymaker . . . . . . . 33

4 Results on sub question 2:What is the current Strategic Geopolitical Intelligence process? 364.1 (A0) Strategic Geopolitical Intelligence . . . . . . . . . . . . . . . . . . . . . . . . 374.2 (A1-A3) Near term assessment processes . . . . . . . . . . . . . . . . . . . . . . . 374.3 (B1-B3) Mid/long term assessment processes . . . . . . . . . . . . . . . . . . . . 394.4 Verification of described intelligence processes . . . . . . . . . . . . . . . . . . . . 39

5 Results on sub question 3:

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Which criteria should be used to compare the value of different StrategicGeopolitical Intelligence processes? 405.1 (Blue) Input ⇒ Process ⇒ Output . . . . . . . . . . . . . . . . . . . . . . . . . . 425.2 (Red) The Iron triangle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425.3 (Green) Final decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425.4 (Orange) Validation of results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

6 Results on the main research question:How can Agent-Based modeling add value to Strategic Geopolitical Intelli-gence of NATO state intelligence services? 446.1 Assessments are practically completely qualitative . . . . . . . . . . . . . . . . . 446.2 Lacking integration of analysis methods . . . . . . . . . . . . . . . . . . . . . . . 456.3 Low number of scenarios and hypotheses . . . . . . . . . . . . . . . . . . . . . . . 466.4 Evaluation of input intelligence is qualitatively . . . . . . . . . . . . . . . . . . . 466.5 No explicit integration with the policy maker . . . . . . . . . . . . . . . . . . . . 476.6 Additional identified limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486.7 Validation of the relevance of the suggested improvements and its suggested im-

provements on the value criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

7 Discussion 517.1 How Agent-Based modeling can add value to the Strategic Geopolitical Intelli-

gence process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517.2 Practical implications of Agent-Based modeling . . . . . . . . . . . . . . . . . . . 547.3 Discussing the validity of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 55

8 Conclusion 578.1 Main conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 578.2 Summarizing the added value of this thesis . . . . . . . . . . . . . . . . . . . . . 58

9 Recommendations 609.1 Recommendations to improve the validity of our study . . . . . . . . . . . . . . . 609.2 Recommendations to improve the new Strategic Geopolitical Intelligence process,

as suggested in the next part of this document . . . . . . . . . . . . . . . . . . . 60

III Suggestion to NATO 63

10 Suggested Strategic Geopolitical Intelligence process 6410.1 Introduction to the discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6410.2 Guidelines for the new Strategic Intelligence process . . . . . . . . . . . . . . . . 6410.3 Proposed Strategic Geopolitical Intelligence process with Agent-Based modeling 65

11 Strategic Geopolitical Intelligence towards a formal model on Mali 8311.1 (α1) Gathering intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8311.2 (α2) Defining intelligence problem . . . . . . . . . . . . . . . . . . . . . . . . . . 8411.3 (α3) Develop Agent-Based model . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

12 Evaluation overview of the new Strategic Geopolitical Intelligence process 9412.1 Practical implications of executing the new Strategic Geopolitical Intelligence

process on the Mali case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

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12.2 Potential and drawbacks of the proposed Strategic Geopolitical Intelligence process 95

IV Bibliography 106

V Appendices 114

A Assumptions, research decisions and definitions used 115A.1 Overview of subjective decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . 115A.2 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116A.3 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

B State-of-art description 122B.1 State-of-art of Strategic Geopolitical Science . . . . . . . . . . . . . . . . . . . . . 122B.2 State-of-art in Strategic Geopolitical Intelligence . . . . . . . . . . . . . . . . . . 124B.3 Striking the golden mean with Agent-Based modeling . . . . . . . . . . . . . . . 124

C The basics of understanding how an Agent-Based model works 126C.1 Anatomy of an Agent-Based model . . . . . . . . . . . . . . . . . . . . . . . . . . 126C.2 Agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127C.3 State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128C.4 Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128C.5 Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128C.6 Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128C.7 Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

D The basics of understanding IDEF0 schematics 130D.1 The process boxes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130D.2 Inputs and outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130D.3 Controls and mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131D.4 Decomposition of diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

E Full description of the Strategic Geopolitical Intelligence process 132E.1 Main IDEF0 diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132E.2 (A0) Strategic Geopolitical Intelligence . . . . . . . . . . . . . . . . . . . . . . . . 133E.3 (A1-A3) Near term assessment processes . . . . . . . . . . . . . . . . . . . . . . . 133E.4 (A1) Setting up near term assessment . . . . . . . . . . . . . . . . . . . . . . . . 135E.5 (A2) Intelligence gathering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137E.6 (A3) Intelligence assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139E.7 (B1-B3) Mid/long term assessment processes . . . . . . . . . . . . . . . . . . . . 141E.8 (B1) Developing n number of plausible hypotheses . . . . . . . . . . . . . . . . . 142E.9 (B11) Generating hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143E.10 (B12) Selecting plausible hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . 145E.11 (B13) Develop hypotheses based on critical unknowns . . . . . . . . . . . . . . . 145E.12 (B2) Gathering intelligence for mid/long term assessment . . . . . . . . . . . . . 146E.13 (B3) Analysis of competing hypotheses . . . . . . . . . . . . . . . . . . . . . . . . 146E.14 Verification of described intelligence processes . . . . . . . . . . . . . . . . . . . . 147

F Value criteria explanation 149

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F.1 Operation costs of intelligence gathering . . . . . . . . . . . . . . . . . . . . . . . 150F.2 Failure costs of intelligence gathering . . . . . . . . . . . . . . . . . . . . . . . . . 150F.3 Duration of intelligence gathering . . . . . . . . . . . . . . . . . . . . . . . . . . . 150F.4 Required integrity of the gathered intelligence . . . . . . . . . . . . . . . . . . . . 150F.5 Operation costs of an analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150F.6 Failure costs of an analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151F.7 Duration of an analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151F.8 Internal validity of an analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151F.9 External validity of an analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151F.10 Reliability of an analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151F.11 Objectivity of an analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151F.12 Operation costs of reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152F.13 Failure costs of reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152F.14 Duration of reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152F.15 Relevance of a report to intelligence task . . . . . . . . . . . . . . . . . . . . . . . 152F.16 Credibility of a report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152F.17 Transferability of a report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152F.18 Dependability of a report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153F.19 Confirmability of a report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

G Computational actions of the formal model 154G.1 Govern controlled regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155G.2 Govern own mil. forces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156G.3 Govern terrorists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158G.4 Diplomacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158G.5 Terrorist attack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159G.6 Migration decision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162G.7 Internal citizen dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162G.8 Wage war with other mil. force . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164G.9 Manage security forces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168G.10 Request provisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170G.11 Evaluate relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171G.12 Desertion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171G.13 Evaluate region attribute values . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172G.14 Migration network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173G.15 Migrating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173G.16 End time step . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173

H Interview reports 174H.1 Interview reports structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174H.2 Interview with US Col. Frederic Borch III (Ret.) . . . . . . . . . . . . . . . . . . 175H.3 Interview with Dr. Niels van Willigen . . . . . . . . . . . . . . . . . . . . . . . . 180H.4 Interview with Prof.dr. Rudesindo Nunez Queija

& NL Capt. Onno Goldbach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195H.5 Interview with Dr. Giliam de Valk and Mr.drs. Willemijn Aerdts pt.1 . . . . . . 209H.6 Interview with Dr. Giliam de Valk pt.2 . . . . . . . . . . . . . . . . . . . . . . . 214H.7 Interview with Dr. Giliam de Valk pt.3 . . . . . . . . . . . . . . . . . . . . . . . 220H.8 Interview with Dr. Giliam de Valk pt.4 . . . . . . . . . . . . . . . . . . . . . . . 225

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I E-mail logs 229I.1 Overview of relevant e-mails . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229I.2 E-mail of Prof.dr. Cederman of ETH Zurich . . . . . . . . . . . . . . . . . . . . . 230I.3 E-mail of Dr. Andrea Ruggeri of the University of Amsterdam . . . . . . . . . . 231I.4 E-mail of Dr. De Valk of Ad de Jonge Centre of Intelligence Studies 1/2 . . . . . 233I.5 E-mail of Dr. De Valk of Ad de Jonge Centre of Intelligence Studies 2/2 . . . . . 234I.6 E-mail of Captain Onno Goldbach of the Dutch Intelligence and Security Institute

(DIVI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235I.7 E-mail of Ir. Gerben Bas of

the Delft University of Technology . . . . . . . . . . . . . . . . . . . . . . . . . . 236

J Survey 237J.1 The respondents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237J.2 Survey results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239

K Literature search log 253K.1 Introducing the Literature search log . . . . . . . . . . . . . . . . . . . . . . . . . 253K.2 Literature search log of literature exploration . . . . . . . . . . . . . . . . . . . . 254K.3 Literature search log of main thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 257K.4 Literature log of provided/suggested sources . . . . . . . . . . . . . . . . . . . . . 261K.5 Literature log of news / topicality articles . . . . . . . . . . . . . . . . . . . . . . 265

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Part II

Main Thesis

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Chapter 1

Main introduction of the thesis

1.1 The volatile world and the opportunity to improve intelli-gence analysis

The recent events of the Arab spring proved the high volatility of states that had been longassumed to be relatively stable by Western policy makers. [104, p.33-34]. In hindsight, we havelearned that the ideas of seemingly unorganized individuals can, via social media, erupt intomajor uprisings [114, p.4-5]. We1 have seen that during these idea-inflamed uprisings, existingactors dissolved where other actors emerged. This seemingly chaotic dynamic fits the (Social)Constructivism view within International Relations studies [91, p.24]. However, most systemengineers would probably recognize these dynamics as being emerging events within a ComplexAdaptive System2. Both theories are very similar but the Complex Adaptive Systems theoryalso provides a strong quantitative analytical tool, namely, Agent-Based modeling [38, p.53] (seeappendix C on the basics of this modeling method).

Our hypothesis is that the introduction of Agent-Based modeling to NATO intelligence servicescan add value to their Strategic Geopolitical Intelligence3 process. To substantiate our hypothesiswe will provide an overview of the state-of-art of Agent-Based modeling in Strategic GeopoliticalAnalyses4 in science and in intelligence in the next section. In section 1.3 we will highlightthe goals of this thesis which are based on the knowledge gap we identified in the state-of-art.Section 1.4 presents all the research questions, section 1.5 will indicate the relevance of thisthesis to society and science. Finally section 1.6 presents a reading guide presented to guide thereader through the rest of this thesis.

1It is in our eyes undesirable to avoid first-person pronouns and use: “verbose (and imprecise) statementssuch as It was found that in preference to the short, unambiguous I found. Young scientists should renounce thefalse modesty of their predecessors. [They] should not be afraid to name the agent of the action in a sentence,even when it is I or we.” [39, p.193-194]. Furthermore we choose we with respect to the supervision committee.

2Definition of a Complex Adaptive System: “an adaptive network exhibiting aggregate properties that emergefrom the local interaction among many agents mutually constituting their own environment”[24, p.51]. An elabo-ration of this definition can be found in A.3.1.

3Definition of Strategic Geopolitical Intelligence: a study of geographical and political dynamics in order toform policy and/or military plans at the national and international level. An elaboration of this definition can befound in A.3.2.

4Definition of a Strategic Geopolitical Analysis: a study of geographical and political dynamics in at thenational and international level. An elaboration of this definition can be found in A.3.2.

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1.2 State-of-art description

This section will show the state-of-art of Agent-Based modeling as a geopolitical analysis method.In this thesis we make a distinction between Strategic Geopolitical Analyses used in internationalrelations science (Strategic Geopolitical Science) and Strategic Geopolitical Analyses used byintelligence services (Strategic Geopolitical Intelligence)5.

1.2.1 State-of-art of Agent-Based modeling in Strategic GeopoliticalScience

In scientific literature, we have seen quite some promising developments where Agent-Basedmodeling is applied for Strategic Geopolitical Science. Examples include developed models toanalyze the general transnational dynamics of civil war [32] or models analyzing the generaleffects of ethnic segregation and its effects on civil violence and insurgencies [25] [26]. Mostof such studies simulate fictional cases to validate existing International Relations theories.However, recently there have been some models which were for existing cases to analyze ethnicand civil violence in, Baghdad [120], Afghanistan [10, 118] and Bosnia [119, 56]. In these real casemodels, scientists also introduce the integration of Agent-Based modeling with data generatedin geographic information systems.

Although there has been quite some scientific development for Strategic Geopolitical Analy-ses, we have seen more Agent-Based modeling studies used for more Tactical- and OperationalGeopolitical Science. Examples include, models to evaluate tactics to dismantle terrorist net-works [66], models to assess the risk of a certain area being attacked by terrorists [19] or tosimulate scenarios for unmanned military convoys [100]. These more tactical and operationalAgent-Based modeling studies are also more applied to specific cases, which also indicates ahigher maturity of Agent-Based modeling at these levels than at the strategic level.

1.2.2 State-of-art of Agent-Based modeling in Strategic GeopoliticalIntelligence

Since we are only able to access public literature and interviews, which are not so rich on in-telligence practices, we are unable to conclude with absolute certainty the exact state-of-art ofStrategic Geopolitical Intelligence. However, able to assumme that at present Agent-Based mod-eling studies have rarely been implemented by the intelligence services to support their StrategicGeopolitical Intelligence6. Based on the detail and level of application in the scientific literatureon Agent-Based models for Tactical- and Operational- Geopolitical Analyses we do believe thatsome intelligence services do use Agent-Based modeling for Tactical-, and Operational Geopo-litical Analyses. This belief is supported by findings in our overall state-of-art study on all typesof geopolitical analysis methods, where we found that for Tactical- and Operational GeopoliticalIntelligence more quantitative methods are applied compared to Strategic Geopolitical Intelli-gence. Our complete state-of-art description, including other geopolitical analysis methods, ispresented in appendix B.

5This distinction is based on the distinction between objective and normative geopolitical analyses that wasmade by Dr. van Willigen [H.3.2.1.1][H.3.2.1.2].

6Funded by expert judgements of all the interviewees, for more details we refer to the overview of Assumptionsin appendix A.2

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1.3 The goals of the thesis

Based on the state-of-art of applying Agent-Based modeling for Strategic Geopolitical Analyseswe can identify a knowledge gap in both science and practice. In science there seems to be a lackof Agent-Based modeling studies applied on real cases that can validate Agent-Based modelingas a scientific method in this field. Despite that there are examples of studies with real cases,the scientific community desires more real world applications of Strategic Geopolitical Analysesusing Agent-Based modeling (see the e-mails in appendices I.2 and I.3).

The knowledge gap within practice is even larger as it appears that Agent-Based modeling ishardly applied at all as a tool for Strategic Geopolitical Intelligence. This is despite the promiseof Agent-Based modeling to handle the high complexity of volatile states as suggested by anincreasing amount of International Relations scientists. Therefore, our hypothesis, as introducedin the first section, is as follows: The introduction of Agent-Based modeling to NATO intelligenceservices can add value to their Strategic Geopolitical Intelligence process. The primary goal ofour thesis is to explore the validity of the above stated hypothesis.

Note that from now on when we refer to the: “Strategic Geopolitical Intelligence” we alwaysmean the Strategic Geopolitical Intelligence of NATO intelligence services.

1.4 The research questions

This thesis explores the validity of our hypothesis by answering the research questions describedin figure 1.1. As can be seen in the figure, this thesis mainly seeks to address how Agent-Basedmodeling can add value to the Strategic Geopolitical Intelligence process. This is being exploredby answering the sub questions, which will result in the following products:

Product of sub question 1: Overview of abilities that distinct Agent-Based modeling from otherpossible Strategic Geopolitical Analysis methods.

Product of sub question 2: Overview of how currently the Strategic Geopolitical Intelligenceprocess is executed.

Product of sub question 3: Overview of criteria that can be used to compare the value of differ-ent Strategic Geopolitical Intelligence processes.

When using the criteria of sub question 3 to evaluate the current Strategic Geopolitical Intelli-gence process, it is possible to identify the limitations of the current process. If these limitationscan be overcome by the abilities of Agent-Based modeling, as identified in sub question 1, thenwe have identified opportunities on how Agent-Based modeling can add value to the StrategicGeopolitical Intelligence process. In order to bridge the gap between science and practice, wehave set a secondary goal to this thesis, as can be seen in figure 1.1. Based on the findingsof our research, we will provide a suggestion on how Agent-Based modeling can be integratedinto the Strategic Geopolitical Intelligence process. In order to test our new structure we willuse it to perform a Strategic Geopolitical Intelligence study on an actual case. The gatheredpractical insights can then be used to iteratively improve the suggested process and provide amore comprehensive answer to the main research question.

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What makes Agent-Based

modeling suitable for the Strategic

Geopolitical Intelligence

process compared to other analysis

methods?

What is the current Strategic


process?

Which criteria should be used to compare the value

of different Strategic

Geopolitical Intelligence processes?

How can Agent-Based modeling add value to the Strategic Geopolitical Intelligence process of NATO state intelligence services?

Sub question 1

Main research question

Sub question 2 Sub question 3

A suggestion on how Agent-Based modeling can be integrated in the Strategic Geopolitical Intelligence process

Demonstration on how the suggested Strategic Geopolitical Intelligence process could be applied to an actual case

Suggestion

DemonstrationEvaluation

Evaluation

Prim

ary goal:

Main

the

sisSe

con

dary go

al:Su

ggestio

n to

NA

TO

Figure 1.1: Structure of research

1.5 Social and scientific relevance of this thesis

If Agent-Based modeling is indeed able to add value to Strategic Geopolitical Intelligence then,by fulfilling the primary- and the secondary goal, this thesis can become a stepping stone tothe implementation of Agent-Based modeling into Strategic Geopolitical Intelligence. This im-plementation is important to a society that demands an ever increasing level of internationalsecurity. Furthermore, if this thesis is able to contribute to the implementation of Agent-Basedmodeling for Strategic Geopolitical Intelligence and if, these services are willing to cooperateand share older cases with science, it will fulfill the needs of the scientific community for moreof these studies applied to real-world cases.

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1.6 Reading guide

The next chapter will present the methods that are used to answer the research questions and toprovide a suggestion to NATO. Subsequently, there are four results chapters, one for each sub-question (chapters 3, 4 and 5) and one for the main research question (chapter 6). Finally, thethesis will include a discussion, conclusion and a recommendation chapter, respectively chapters7, 8 and 9.

After these three chapters, part III presents the suggested Strategic Geopolitical Intelligenceprocess, the demonstration of it on an actual case and an evaluation of the suggested StrategicGeopolitical Intelligence process. This is presented in respectively chapters 10, 11 and 12. Afterthis, part IV presents the bibliography of this thesis. This document concludes by providing allthe appendices in part V.

Furthermore note that there are two types of sources used to support our reasoning throughoutthe thesis; literature sources and expert sources. The literature sources are indicated with anumber between brackets e.g. [1], or sometimes to indicate an exact page e.g. [1, p.1], whichrefers to the bibliography. The expert sources are indicated by a letter and a number betweenbrackets, referring to the appendix that shows the report of that specific expert contact e.g.[H1].

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Chapter 2

Methods

2.1 Introducing the methods

This chapter will explain how we have answered all the research questions. First will be explainedfor the sub questions, the main research question and the secondary goal, which methods wehave used to provide results. The names of the methods are written in italic and correspondto paragraphs in the second section, which described what these methods are and how they areused.

2.2 Chosen methods

2.2.1 Chosen methods for sub question 1

Based on a Literature study we have identified what Agent-Based modeling can and cannot dofor Strategic Geopolitical Intelligence compared to other types of analysis methods. We havevalidated our reasoning by having some Expert contact to check our most critical arguments.


By gathering information using a combination of a Literature study and Expert contact withintelligence experts we have identified the current Strategic Geopolitical Intelligence process.Using IDEF0 diagrams we have created a structured overview of the entire Strategic GeopoliticalIntelligence process, which we can use to structurally answer the main research question andcommunicate our findings. Preferably we would have validated our findings with NATO orrelated intelligence services but due to the closed way of operating of intelligence services, thevalidity of the answer fully depends on the reliability of the sources used. We did however verifiedour formulated findings with the intelligence experts who helped us determine the current wayStrategic Geopolitical Intelligence is executed.

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We will decompose the general term Value of a Strategic Geopolitical Intelligence process intomeasurable criteria using a Literature study and Expert contact. This is schematized using anObjective-tree. Since the level of value is subjected to the eye of the beholder, we tried to assumethe position of NATO as possible.

2.2.4 Chosen methods for the main research question

To answer the main research question, we have evaluated all the processes of Strategic Geopoliti-cal Intelligence on the value criteria and compared these results with the abilities of Agent-Basedmodeling. This is done structurally per process using the IDEF0 diagrams we have developedfor sub question 2.

2.2.5 Chosen methods for the development of a new Strategic GeopoliticalIntelligence process with Agent-Based modeling

Based on the results of the main thesis and our own insights on which we reflected using someExpert contact, we have developed a new structure of Strategic Geopolitical Intelligence processesin line with existing NATO doctrine. We described this process also in IDEF0 diagrams andwe have validated the feasibility of the design through Expert contact. After the finalizingthe design we have qualitatively evaluated the value of the suggested Strategic GeopoliticalIntelligence process on the criteria identified in sub question 3. These insights are used in thediscussion of the main thesis.

2.2.6 Chosen methods for the demonstration of the new Strategic Geopolit-ical Intelligence process

We have used the suggested Strategic Geopolitical Intelligence process on an Instrumental case.The input information of the case is based on a Literature study and Expert contact. Howeverexecuting an accurate Strategic Geopolitical Intelligence process on the case was not feasiblewithin the time and input information limitations of our study1. It would be possible to createa fact-free model, a model without real input information and thus without a useful assessment,in order to provide a more tangible example of how an Agent-Based modeling study could com-plement the Strategic Geopolitical Intelligence process. However the benefits of implementinga fact-free model in software does not outweigh the large amount of extra time required todevelop the complete Agent-Based model2. Therefore we have only developed a formal Agent-Based model, which is basically the architecture of a model before it is programmed.

A formal model shows what the Agent-Based model would look like and it shows the typesof results it will produce. It helped us experience the practical implications of developing anAgent-Based modeling for Strategic Geopolitical Intelligence. Throughout the demonstrationwe used the learned lessons to improve the suggested Strategic Geopolitical Intelligence process

1See paragraph 2.3.4 on why the limitation of available information could not be overcome by picking up anhistoric case

2This notion was made by the second supervisor of this thesis, Dr.ir. Igor Nikolic and carried by the rest ofthe committee supervising this study.

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iteratively with the demonstration. In the end we have provided an overview of these learnedlessons, which are relevant for the discussion of the main thesis.

2.3 Description of methods

2.3.1 Literature study

We used the literature study method for answering sub questions 1 and 2, but also for determin-ing the state-of-art. In each of the situations the method we applied was the same. Based onthe sub question, a certain set of questions were determined we wanted to “ask the literature”.After getting an answer or an incomplete answer, other questions to “ask the literature” logicallyarised. Based on the question of the search we determined a set of search words and used eitherthe search engine of www.scopus.com or www.google.com. As a student at the TU Delft we haveaccess to the former, which is a search engine especially for scientific literature.

For the Instrumental case study we also followed a set of news sources and selected relevantarticles based on pre-determined key words (Dutch equivalents are used in Dutch sources).All accessed and read literature is logged in appendix K, however literature we have accessedbefore this study or literature that was used exclusively to understand or find other literatureis not included. Examples of the latter are dictionary searches on words, or websites with thebackground of authors/organizations. The main drawback of a literature study is that there isso much literature but yet so little information, especially because this thesis is on a securityprocess executed by closed organizations. Therefore we always combined our literature studywith expert contacts, to help us search through the big bulk of literature and provide us withextra information that public literature cannot provide us.

2.3.2 Expert contacts

We from the faculty Technology, Policy & Management are educated to handle the cross-over ofthe technology domain (Agent-Based modeling) with the policy domain (international securityand intelligence). However for the domains itself it was essential to gather in-depth knowledgefrom specialists on these disciplines3. Besides gathering knowledge we used our contact with theexperts as an opportunity to to validate the findings we already had by then. Secondary to theabove we also used the contact with experts to determine the state-of-art, which is presentedin section 1.2 and appendix B. Throughout this thesis we used expert statements as sources tosupport our reasoning. These expert sources are indicated by a letter and a number betweenbrackets, referring to the appendix that shows the report of that specific expert contact. In thenext paragraphs we will show the different types of expert contact we had, an overview of allthe expert contacts and the drawbacks of how we applied expert knowledge.

2.3.2.1 Types of expert contacts

To gather knowledge from experts directly we used interviews, e-mails and a survey. Preferablywe interviewed the experts because the interaction of an interview allows us to dig deeper and

3Especially on international security and intelligence because, opposed to general security and Agent-Basedmodeling, our faculty does not educate or performs research in this domain.

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more effective in their knowledge. However if time did not allow it, or the effort of organizinga meeting was bigger than the benefit of interactive contact, we used e-mail contact. For thevalidation phase we chose to use a survey to validate our findings among as much experts aspossible. Below we will discuss how we used the two types of contact in our thesis.

2.3.2.1.1 Interview contact Depending on the goals of the interview, we applied a semi-structured, structured or unstructured approach. In case our goal is to explore, we chose toapply a semi-structured interview approach. This is a smart balance of being unstructured andstructured in order to gather a wide range of information of the expert and still be able toconsistently report and be transparent about the interview process. When the interview goalis validating/verifying we applied de Structured approach, because in that case we need to beconsistent in our questioning to be able to aggregate the answers into a resulting level of valid-ity/verification [ibid., p.2]. When the interview goal is to receive information or documentationon the initiative of the interviewee itself then we took the unstructured approach to give theinterviewee the space for providing us with the information or documentation.

Note that the terminology of types of interview methods is based on an overview of literaturemade by McLaughlin [ibid.]. This overview represents the concepts from works of Dillman [43],Flick [48], Gubrium et.al. [59], Reis et.al. [94] and Taylor et.al. [106].

2.3.2.1.2 E-mail contact We used e-mails with experts to complement the contacts wealready had with experts using interviews. Due to the relatively limited information e-mailprovides, we only used it for requests to help us guide through literature and to validate orverify our findings. Validating and verifying was done by presenting our findings directly, withsome guiding notes, and asking the experts to which extend they felt our findings were valid orcould be verified.

2.3.2.1.3 Survey In order to be able to contact a wide range of experts to validate ourfindings and assumptions, we chose to use a survey. Such a survey can easily be send to expertsall around te world and requires less effort for each expert, than an interview or an e-mail,making the cooperation of experts more likely. Furthermore the validation phase requires thatall responses of experts are comparable, which requires a very structured approach in questioningand answering. A survey forces to ask exact the same questions and receive the same types ofanswers. Finally a digital survey also allows for easy and fast reporting of the results, as can beseen in appendix J.

2.3.2.2 Overview of expert contacts

All the Expert Contacts have been fully logged in appendices H (Interviews), I (E-mails), andJ (Survey). Besides the content of the expert contacts, the appendices also present per experta background profile of the interviewee and how- and why he/she was selected. For a briefoverview, table 2.1 shows all the expert contacts that have been present in this thesis. Thebackground of each expert is indicated with an abbreviation behind the name: ABM = Agent-Based modeling expert, Int = Intelligence expert, IR = International Relations expert, S&S =Safety & Security expert or an combination of any of these fields.

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Table 2.1: Overview of expert contacts

Part Reason ofinterview

Name of expert (expertise) Type of contact Appendix

Set-up Exploration Dr. Niels van Willigen (IR) Interview H.3of thesis US Col. Frederic Borch III (Ret.) (IR) Interview H.2

Prof.dr. Lars-Erik Cederman (ABM-IR) E-mail I.2Dr. Andrea Ruggeri (ABM-IR) E-mail I.3

Sub Gathering Prof.dr. Rudesindo Nunez Queija (Int) Interview H.4question 2 knowledge NL Capt. Onno Goldbach (Int) Interview H.4

E-mail I.6Dr. Giliam de Valk (Int) Interview H.5 H.6 H.7

E-mail I.4 I.5Mr.drs. Willemijn Aerdts (Int) Interview H.5

Subquestion 2 Verification Dr. Giliam de Valk (Int) Interview H.7 H.8

Sub Gathering Dr. Niels van Willigen (Int) Interview H.3question 3 knowledge Prof.dr. Rudesindo Nunez Queija (Int) Interview H.4

NL Capt. Onno Goldbach (Int) Interview H.4Dr. Giliam de Valk (Int) Interview H.5

Mr.drs. Willemijn Aerdts (Int) Interview H.5

Mainquestion

Validation Dr. Giliam de Valk (Int) Interview H.8

Sub Validation 15 IR and Int experts from 8 different Survey Jquestions nations, see table J.1 in appendix J

1 & 3 for an overview of the experts

2.3.2.3 Drawback of Expert Contact

Since this thesis is not organised in direct cooperation with NATO nor any of the NATO stateintelligence services, we had to base all our findings on intelligence practices on indirect infor-mation via literature and interviews with academics. However this should not harm the validityof the research too much since we know that our interview sources on intelligence practices, andthe literature they provided, are directly connected with a NATO state intelligence service (seeappendices H.5 and H.6). However the thesis is not conclusive about the value assessment of theStrategic Geopolitical Intelligence process since we cannot validate with the NATO; our view onwhat the correct value criteria are, to what extend these criteria are important to NATO stateintelligence services, and how the different analysis methods score on these criteria according toNATO.

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2.3.3 IDEF0 diagrams

IDEF0 is a qualitative modeling method to make diagrams that described the details of a process.The method is used for analyzing and communicating how a process is arranged [58, p.168]. Animportant drawback is that this modeling method can result in complicated looking diagramsfor people unfamiliar with the method. To prevent this each diagram should not show more thansix process blocks [53, p.21]. Another drawback is that the method is quite rigid and sometimesdoes not allow to show all the important aspects the modeler wants to show.

2.3.4 Instrumental case

An Instrumental case study uses a single case and is not so much interested in the case itselfas it is interested in the lessons that can be drawn from it by studying the case [102, p.16-17].We preferred a present case over a historic case because that made it easier to involve multipleexperts that were interested or familiar with the case. Furthermore it also positioned us in thesame situation of the intelligence analyst who analyzes future events without knowing that theywill occur. It is hard to reproduce this situation in hindsight with a historic case plus that wewill never find out when an intelligence service knew what. For us these advantages outweighedthe benefits of using a historic case with an abundance of information. Especially since the levelof realism of our model is only important to show the compatibility of our model with the otherprocesses of intelligence services and the types of information they have available.

To achieve that level of realism, only the types of dynamics in the instrumental case are importantand not the input information that affect these dynamics. The reason why some realism is stillimportant, is that it proves to the reader that an Agent-Based model can capture any type ofdynamic and not only the dynamics convenient to the modeler. Finally because we will onlydevelop a formal model, we will not have an assessment of possible future scenarios that canbe compared with the actual events that happened in hindsight to validate our method. Ofall the current cases, we chose Mali where during our exploration of possible cases the Frenchintervention took place. This made the case the most suitable case of all in terms of interestof relevant experts and media, and currently the case is still developing (where Syria is in astalemate [H.2.2.2.5]).

2.3.5 Formal Agent-Based model

We chose the 10-step framework of creating an Agent-Based model by van Dam et.al. [38, p.74-p.136], and only executed the first four steps required to develop a formal model. These foursteps are:

Step 1: Problem formulation and actor identification;

Step 2: System identification and decomposition;

Step 3: Concept formalisation; and

Step 4: Model formalisation.

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2.3.6 Objective-tree

The Objective-tree is a common used Systems Engineering method to decompose opaque crite-ria, like value, into more tangible criteria. Generally one decomposition will only provide lessopaque criteria, therefore these less opaque criteria are also decomposed, and these are alsodecomposed, etc. until operational criteria are identified [45, p.53-55]. Operational criteria arecriteria that can be measured or at least qualitatively assessed. Together all these operationalcriteria determine the main, opaque criterion. However this method does not automaticallyassure that the criteria are independent from each other.

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Chapter 3

Results on sub question 1:What makes Agent-Based modelingsuitable for Strategic GeopoliticalIntelligence compared to otheranalysis methods?

This section will provide an answer to sub question 1 by positioning Agent-Based modelingamong other types of modeling methods. We have created figure 3.1 to show this positioning.The lines/edges indicate the type of method and the blocks provide example of methods that areused in Strategic Geopolitical Intelligence. It shows that an Agent-Based model is a quantitative,open-form, inductive bottom-up, model that replicates a real world problem, used to study thatproblem. The following sections will discuss for every node why, under the assumed ComplexAdaptive Systems paradigm, what makes Agent-Based modeling a suitable analysis methodfor Strategic Geopolitical Intelligence compared to other analysis methods. To validate thiswe formulated per node the most powerful argument of why methods other than Agent-Basedmodeling are less suitable and asked experts via a survey whether they agreed on that argument.After explaining and validating each node, section 3.6 elaborates on how we have designed thesurvey we used to validate the results. Finally we will provide some notions on what cannotbe expected of an Agent-Based model in order to manage expectations of the potential user /policy maker.

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Strategic Geopolitical Analyses

Node1

Node2

QuantitativeQualitative

Open-formClosed-form

Deductive(Top-down)

Inductive(Bottom-up)

Node4

Studying a problem

Agent-Based modeling

Narration

Game TheoryPublic-, Rational choice theories

Discrete Event SimulationsDynamic Systems modeling

System Dynamics

Multi-Agent systems

Automaticallysolving a problem

Artificial Intelligence

Studying individual agents

Node5

Node3

Replication Generalisation

Statistics

Figure 3.1: Agent-Based modeling in perspective

3.1 (Node 1) Qualitative methods versus Quantitative methods

Qualitative methods are very flexible and do not force the analyst to look for equilibria or con-sider actors to be fixed and given with common and complete knowledge. Which is useful forpeople who, like us, consider geopolitical systems to be a Complex Adaptive System. However,ironically, reasoning from the Complex Adaptive Systems paradigm especially requires a quanti-tative methodology. This is due to the inherent complexity and context dependence of ComplexAdaptive Systems. The logic and strict reasoning required for quantitative analyses helps ana-lysts to maintain coherence and consistency in the analysis of a Complex Adaptive System [24,p.30], or limit the negative effects of psychological phenomena like Groupthink. Furthermore aquantitative study can help the analyst to explicitly formulate the used line of reasoning andproduce precise and testable theories [ibid.]. Finally, and maybe most importantly, quantita-tive analyses can run on computers, which can overcome the limitations of the human mind tocomprehend the high number of interrelations present in Complex Adaptive Systems [116] [45,p.176-177].

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Validation statement: When the results are exactly the same; the results of a quantitativemethod are more powerful than the results of a qualitative method.

Validation results: 70%, opposed to 30%, of the experts agreed with this validation statement.

3.2 (Node 2) Replication versus Extrapolation

Strategic Geopolitical Intelligence, as defined in our thesis, is aimed to develop future policiesand/or military plans. This requires the ability to make forecasts of the Geopolitical systemunder study. Basically there are two types of quantitative forecasting approaches. Either theanalyst forecasts based on replication or on extrapolation [45, 123-151]. One can extrapolatebased on past events in the case of interest or one can extrapolate between multiple similarcases. This way of forecasting is based on the assumption that events occurred in the past orother cases, will likely occur again if the situation is similar. Although these trends prove to bequite reliable in practice [16, p.100-130], an analyst will never be able to identify possible futuretypes of emergent events that have never occurred yet in history. The same goes for evaluatingthe effects of policy options that have never been implemented in a case similar to the case ofinterest. This does not reflect the Complex Adaptive Systems paradigm at all and is therefore,by itself, not sufficient to be used for Strategic Geopolitical Intelligence.

Replication on the other hand attempts to model the real world system under study, as anexperimental test bed. This allows an analyst to compare, in a fully controlled environment,different parameter settings1 similar to e.g. a chemistry experiment. Parameters represent dif-ferent experimental set-ups of a model and can represent varying starting situations, scriptedevents and/or policies, etc. This means that models allow the analyst to explore what-if ques-tions. The likelihood and impact of generated future scenarios can also determined, but thatis coupled to the accuracy of the intelligence used to determine the parameters settings. Todetermine these parameters one usually depends on quantitative and qualitative extrapolationanalyses. Another, additional, way to determine likely parameter settings is to create SeriousGames out of models [38, p.219-220]. This can be done for every type of model and is usefulto incorporate directly human behaviors in the model, with the additional benefit to get theinsights of the study better across to non-experts [ibid.].

Validation statement: Replication can do more than extrapolation e.g. study type of eventsthat have not occurred before and Serious Gaming. The other way around extrapolation cannot do more than replication, which makes extrapolation less useful than replication.

Validation results: 70%, opposed to 7%2, of the experts agreed with this validation statement.

3.3 (Node 3) Open-form models versus Closed-form models

When modeling a part of the real world, a modeler has to decide what dynamics are insidethe system and what dynamics are the environment of the system. Closed-form models aim tomodel all significant dynamics that influence a system. Open-form models the other hand onlyattempts to model the dominant dynamics and use input variables to replicate the effects of

1Parameters in a model can be simple input values as well as more complicated modeling aspects like decisionrules.

223% had no opinion on this statement

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the environment of the system [37, p.20]. Thus the input variables are a type of parametersthat in the experimental set-up represent the external dynamics on the system. The domainsof all variables used to replicate the environment can have practically unlimited different valuessince the environment around the system under study can develop in practically unlimitedways3. This means open-form models can generate unlimited possible futures, as opposed toclosed-form models that do not take into account practically unlimited parameter domains torepresent effects from the environment.

Closed-form models are preferred over open-form models since with their limited parametersettings and limited outcomes, they generate findings that are verifiable for the entire parameterspace [24, p.62-63]. However such models, properly representing a Complex Adaptive System likea volatile state, do not exist [ibid. p.64]. This is not to say that there are no useful closed-formmodels of geopolitical systems. A good example are the Game-theory models by RAND thathelped to develop the Mutual-Assured-Destruction doctrine that probably prevented a nuclearholocaust during the Cold War [90]. However in the post-Cold War era, three major assumptionsthat closed-form geopolitical modeling methods require became less valid. Because closed-formmodels exclude external ef-fects from the environment onto the system under study, such modelsassume Fixed and Given Actors and Common and Complete Knowledge [24, p.32-36]. Underthese assumptions;

• Actors do not change their preferences or identity through time;

• Actors are fully rational; and

• Actors know everything on the identity, preferences, and resources of all the other actors.

This is especially unrealistic when considering volatile states; where changing preferences ofactors can emerge into new aggregate actors who through revolution can even replace an actor.Another problem of closed-form games is that they often attempt to identify equilibria in thesystem under study. However in that case an analyst assumes that equilibria exist at all in asystem, and even when an equilibrium does exist then the historical processes should be fastenough to reach that equilibrium before its environment changes and the equilibrium itself willbe changed [ibid.]. These unrealistic assumptions required for closed-form modeling force theanalyst to use open-form modeling, unless one wants to ignore the high complexity in strategicgeopolitical systems and escape to extrapolation or qualitative methods.

Validation statement: It is not possible to assume for the duration of the analysis that actorsdo not change their preferences through time.


Note that in the survey we also tested the opinions of experts on the other assumptions necessaryto apply Closed-form modeling. These were all rejected by the experts by at least 57%. Further-more none of the experts accepted all the assumptions and 47% of the experts rejected all theassumptions.

3Under the assumption there are finite elements in the world, every possible future can be simulated. Howeverthe large amount of elements and possible combination make such an analysis intractable. Which means thatthere is no computational device in the world that can provide an outcome before the outcome itself occurred inthe real world [78, p.3].

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3.4 (Node 4) Deductive (top-down) approach versus Inductive(bottom-up) approach

From a top-down approach, one studies the real world system and deducts all perceived patternsand regularities into a model. There are two troubling assumptions with this approach:

• The analyst knows how the system behaves; and

• The system structure is static.

From a deductive approach it is necessary for the analyst to be able to identify the presenceof all the structures in a system under study. However this requires a lot of knowledge of theanalyst on the system structure even before modeling. This whilst we required a modelingmethod to gain knowledge on how the Complex Adaptive System is structured. Furthermorethe structures that an analyst can identify from the real world are usually metastable and maydissolve suddenly [ibid. p.53]. These metastable structures are assumed to be static in deductivemodels, which means that these models are only valid as long as the structure of the systemremains the same. This limited validity significantly impairs the usefulness of such a model ongeopolitical systems where some structures, that in the future will/can dominate the system, donot exist yet at the time of study (e.g. emerging strategic alliances).

At the cost of significantly more computation power4 and losing mathematical descriptions5, theabove presented problems can be overcome when using an inductive approach [ibid.] [84, p.368][28, p.4] [92]. By focusing on the low level interactions that causes patterns and regularities toemerge, one uses generative science to model his perspective on the real world by letting themodel grow. This means one does not have to identify or understand the global interdependenciesin the system to be able to grow a model that exhibits the effects of global interdependencies[14, p.7]. Growing a model provides the analyst with better understanding of the system understudy [46, p.51]. Furthermore, the grown model also includes critical paths and history thataffect future dynamics. This adds to the realism of the model and if input data with a relativelyhigh level of accuracy is available; the model can be used to identify possible paths that lead toa certain emergent event [38, p.55]. This is useful for both the ex ante analysis of what has tohappen to let a certain future event emerge, and for the ex post analysis to discover what likelyhappened in the past that created the current situation.

Validation statement: It is not possible to assume that for the duration of the analysis, thestructure of money flows, immigration flows, etc. is static.


3.5 (Node 5) Solving a problem versus Studying a problem ver-sus Studying individual agents

At this level of the taxonomy of analysis methods only three known tools remain. These toolsare theoretically distinctive from each other but in practice show a lot of overlap, resulting in

4Due to the high number of interactions and the extra simulations required to handle parallelism in inductivemodels (see appendix C), these models require significant more computer power than deductive methods.

5Since inductive models are programmed based on micro-interactions, the model will not produce a mathe-matical description of any behavior that is analytically tractable [68, p.5-6]. Therefore no existing mathematicalanalytical methods that require such expressions, like comparative statistics [24, p.62-63], can be used.

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confusion both in science as well as in practice [38, p.56] [I.4]. It is mainly the aim of the toolthat differs. Following our argumentation from the introduction of this thesis, the aim of the toolwe are looking for is one that helps an intelligence service understand the complex dynamicsin volatile states better. An Agent-Based model attempts to model, in silico, the real worldsystem under study in order to try to understand the real world better [38, p.55]. This is anessential difference with a Multi-agent system that attempts to make predictions on emergencein a system based on real-time data, and automatically act to control that emergence in thesystem [ibid. p.56].

Multi-agent systems are already successfully applied in practice for Strategic Geopolitical In-telligence [6], [86]. However these Multi-agent systems attempt to gather and track usefulinformation, instead of understanding and identifying regularities and patterns that emerge in areal world system under study. Then there is Artificial Intelligence that focuses on the individ-ual agents and attempt to replicate their; learning processes, decision making, etc. [38, p.56].Although quite distinctive from Agent-Based modeling and Multi-agent systems, the differencesblur when Artificial Intelligence models have multiple agents that can respond to each other hisactions [ibid.]. However the aim in Artificial Intelligence is to understand the individual agents,rather than understanding the emerging dynamics between agents like an Agent-Based modelingstudy does. Therefore the most suitable tool seems to be an Agent-Based model.

No validation required because this node is more about terminology of different very similarmethods than about its suitability for the Strategic Geopolitical Intelligence process.

3.6 Validation of the aspects that makes Agent-Based modelingsuitable for Strategic Geopolitical Intelligence

In order to validate the results of this chapter we have used a survey among experts. In order toprevent prejudices we left out every respondent who considered himself an expert on Agent-Basedmodeling. Furthermore we also limited prejudices by mixing positive and negative arguments.An example of a negative argument is that we asked experts whether they thought it wouldbe possible to assume for the duration of the analysis that actors are fixed. If they thought itwould be true, then it would invalidate our argument that this assumption cannot be made. Forclarity we inverted all the negative arguments into positive arguments in the validation resultsabove. For the entire report on the survey, including the detailed statistics and the overview ofrespondents, see appendix J.

Note that the survey gave the respondents the option to state whether they strongly agreed ordisagreed. However in hindsight we felt that the term strongly is too subjective. So we onlymade a distinction between agree and disagree.

3.7 Notes on Agent-Based modeling for the potential user /policymaker

As stated above there are many reasons why Agent-Based modeling can play an importantrole in Strategic Geopolitical Intelligence. However to manage expectations of Agent-Basedmodeling, we provided some remarks on the limitations of Agent-Based modeling below. Mostof the remarks are valid for all forecasting methods and can be considered quite obvious or even

33

seem childish, but they are apparently not redundant considering the major misuse and abuseof models by both analysts as policy makers [85].

3.7.1 All models are wrong but some are useful

A model that replicates all real world dynamics one-on-one is merely impossible to developbecause that means that every entity in the real world and its values has to be known andprogrammed into a model. Besides that it is practically impossible to know everything, realitywill occur before a computer model with everything is developed and yields useful results [78,p.36-39]. Furthermore, if a model becomes as complex as the real world itself, the model canhardly be used to explain the real world anymore [22]. This paradox between accurate replicationand understandability, added with the practical implications of identifying and replicating alldynamics in the real world, makes that “essentially, all models are wrong, but some are useful”[15, p.424]. Knowing that all models are wrong, the user should take into account that there isalways a discrepancy between the simulated reality and the true reality. Furthermore the usershould tread very carefully when adopting a model for a different use it was designed.

3.7.2 All parameter values are wrong but many are useful

Another reason why model users should always expect incorrect results, is that the parametervalues in a model are never fully accurate. Even in studies that are, unlike Strategic GeopoliticalIntelligence studies, rich of detailed and accurate data, the parameters will never be accurateenough to represent reality. This because the slightest of change in parameter values can lead tovery different results in Complex Adaptive Systems [78, p.297]. In order to handle the unavail-ability of fully accurate data, Agent-Based modeling studies generally apply Parameter Sweeps.Parameter Sweeps are done by executing multiple experiments and use different parameter set-tings per experiment [38, p.108-112]. In that way, one of the settings used will be close to thetrue values. Still none of the parameter settings will be fully accurate, but if the model is not“too wrong”, then one of the simulated futures will be very close to the one that will material-ize. However, along with the number of different parameters in a model the possible differentparameter settings rises exponentially.

Also consider that Agent-Based models are open-form models with practically unlimited possibleparameter values (see paragraph 3.3). Besides that not even a computer can simulate practicallyunlimited parameter settings6, it is also undesirable if the close-to-accurate simulated futurescenario will get lost among the great size of other simulated scenarios. Therefore the analystwill have to be smart in trying combinations of different parameter settings, think of samplingmethods like Latin Hyper Cube, etc. [ibid. p.108-111]. Also the certainty around used parametervalues allows for less wide value domains that need to be simulated7. Beware though that leavingout parameter settings, based on logical chance (sampling) or certainty assessments, will allowBlack Swans to emerge after the study.

Black Swans, as coined by Taleb [105], are events with major impacts that no man or computerever thought of. It is the task of the analysts and policy makers to be aware of the existence

6Especially when considering that in an Agent-Based model there are extreme numbers of possible simulationspossible using the same parameter settings, due to the different pseudo-random orders agents can act in differentsimulations to represent parallelism

7If certain sets of values are certain to be incorrect than they do not have to be simulated.

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of Black Swans and not to ignore signals that are not in line with the expected future(s). So agood Agent-Based modeling study will only minimize the likelihood of failing to identify BlackSwans but, based on our reasoning in the rest of this section, an Agent-Based model will do thisbetter than any other method because it is more suitable to make forecasts in volatile statesthan the other discussed methods.

3.7.3 The threat of the flexibility of Agent-Based models

An Agent-Based model is striking a golden mean when it comes to the flexibility in which itcan be used. Since it is quite rigid compared to qualitative methods, it helps the analyst tomaintain coherence and consistency, and limit bias. On the other hand, Agent-Based modelingis also quite a flexible method when comparing it to other quantitative methods. This relativeflexibility allows Agent-Based models to include concepts as bounded rationality and expertjudgements, however this also allows Agent-Based modeling analysts to become more sloppythan analysts using rigid modeling methods [24, p.62]. Furthermore, all modeling decisionsmade by the analyst, e.g. the delineation of the system under study, are affected by the analysthis view of the real world system [78, p.43]8. Because of the above reasons, the bias of the analystcannot be fully disregarded in an Agent-Based modeling study. Therefore it is still importantto; log every assumption in the study, use relevant literature or expert judgements to supportthe assumptions, etc.

Note though that Agent-Based modeling should always be considered as just a tool supporting alarger analysis framework using other qualitative (and quantitative) methods. An Agent-Basedmodel will only generate possible patterns and regularities, but the human mind has to recognizeand interpret these behaviors, operations a computer is not suited for9.

8Note that this phenomenon called Observer Dependency makes that every model is always at least a doublesimplification of the real world: Real world ⇒ Analyst his view on the real world ⇒ Model of the Analyst hisview on the real world.

9A nice example indicating this interdependency between humans and computers when analyzing complexity,is one Dr.ir. Igor Nikolic mentions in his lectures at the TU Delft; “We rely on computers to store databases offaces but only recently we managed to train advanced computers in such a way they can recognize a face, whereasa baby recognizes the face of its mother as soon it can see”[79].

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Chapter 4

Results on sub question 2:What is the current StrategicGeopolitical Intelligence process?

Based on multiple interviews with intelligence experts and acquired documentation, we managedto determine how currently the Strategic Geopolitical Intelligence process should be executedaccording to NATO doctrine. We have developed a set of IDEF0 diagrams to present and analyzethe processes of Strategic Geopolitical Intelligence. This chapter will only show and explain themain processes, for the detailed description and analysis we refer to appendix E. The maindiagram is presented in figure 4.1 and shows the basics of the Strategic Geopolitical Intelligenceprocess1. It also shows the basics of how an IDEF0 diagrams should be read, for a more elaborateexplanation on the IDEF0 method we refer to appendix D. The first paragraph will show thedistinction and the positioning of the two types of Strategic Geopolitical Intelligence processesperformed by NATO state intelligence services. This is followed by two paragraphs that explainper type of Strategic Geopolitical Intelligence process how it is executed. The final paragraphshows the results on the verification we did in this chapter.

Inputs

Mechanisms

Outputs

Controls

A0-B0

Strategic Geopolitical Intelligence

Intelligence reportInformation

General intelligence

Data

Intelligencetask

NATO doctrine

Intelligence analyst

Intelligence gatherer

Figure 4.1: IDEF0 diagram of the Strategic Geopolitical Analysis process

1The figure shows that Intelligence gatherers and Intelligence analysts turning Data and General intelligenceinto Intelligence reports, according to NATO doctrine [77] [99] and guided by an Intelligence task set up by themandated ministers [113, art.7e] or NATO agreements [H.7.2.8].

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4.1 (A0) Strategic Geopolitical Intelligence

The first decomposition of the IDEF0 diagram is presented in figure 4.2 and it shows in the col-ored frames, the two different types of Strategic Geopolitical Intelligence methods the NATO pre-scribes: Near term assessments and Mid/long term assessments [112, p.3-10 (1)] [99] [H.6.2.2.2].Near term assessments are aimed on situation monitoring and crisis detection. Mid/long termassessments are aimed to pro-actively determine policy options in order to develop the propercapabilities to contain future crises [I.5]. The positioning of these goals within crisis manage-ment at the strategic geopolitical level is presented in table 4.1. The crisis states in the table areapplicable to both interstate- and intrastate conflicts that (potentially) affect NATO memberstates [ibid. p.8]. Basically the two assessment methods are executed as two independent sets ofprocesses, the only interaction is that the intelligence gathered for the mid/long term assessmentis also used as general intelligence input to set up near term assessments [H.7.2.1]. Below willfirst the near term assessment set of processes explained (A1-A3) and then the mid/long termassessment set of processes.

Table 4.1: Overview of the overall crisis management process, adopted from the NATO handbookfor Early Warning [77, p.5].

Crisis states

Escalation De-escalation

PeaceDisagree-

mentConfront-

ationArmedconflict

Builddown

Stability

Cri

sis

Man

agem

ent

Acti

vit

ies Situation

monitoringX X X X X X

Crisisdetectionsupport

X X

Containment X X X X

Disengagement X X X

Peacebuilding X X

4.2 (A1-A3) Near term assessment processes

The blue section of figure 4.2 shows the three main processes of the near term assessment.The figure shows that an Intelligence collection plan is developed to guide the Gathering ofintelligence, but also that sets of Scenarios and Critical indicators that are developed to guidethe Assessment of intelligence [99, p.3] [112, p.10 (1))] [77, p.11]2. These three controls help tofocus the Intelligence- gathering and assessment processes to be more efficient with resources.This signifies Indicator based intelligence from classical intelligence [99, p.4] [H.6.2.2.2]. All

2An indicator is a single event in the sequence of possible future events that make a Scenario [98, p.1]. ACritical indicator is one of those events that indicate a significant change in the level of threat and allows theanalyst to make, change or modify an assessment [99, p.12].

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processes are executed in a linear fashion without any iterations between the processes, unlesserrors in the execution of other processes are discovered. The whole set of processes starts againwhen a new intelligence task is provided.

Near term assessment processes

A2

Gathering intelligence for

near term assessment

A3

Assessing intelligenceIntelligence

on critical indicators

Near term intelligence

report



A1

Setting up near term assessment


Intelligence task

Warning problem

Set of Critical indicators

Set of Scenarios

Intelligence collection plan

Mid/long term assessment processes

B1

Developing n number of plausible

hypothesesB3

Analysis of competing hypotheses

B2

Gathering intelligence for mid/long term

assessment

Intelligence on plausible hypotheses

Intelligence for hypotheses

Mid/long term intelligence report

Plausible hypotheses

Request for additional intelligence

NATO doctrine

NATO doctrine

NATO doctrine


Data




Data

Figure 4.2: A0 diagram of the Strategic Geopolitical Intelligence process

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4.3 (B1-B3) Mid/long term assessment processes

The red section of figure 4.2 shows the mid/long term assessment processes. It shows that thethree main processes of the near term assessment have similar equivalents in the mid/long termassessment. However there are many differences. In mid/long term assessments the intelligencegathering process is continues and focuses on general indicators based on NATO doctrine and theindividual best practices of the intelligence gatherers [H.7.2.6]. The gathered intelligence is thenused to generate a set of new hypotheses. A hypothesis in mid/long term assessments are, similarto a scenario in near term assessments; a falsifiable hypothetical sequence of possible futureevents that will lead to a certain end state [H.7.2.7]. From the set of new generated hypothesesa selection of Plausible hypotheses is made, all implausible hypotheses will be iteratively refinedinto a plausible hypothesis (see appendix E.8) [112, p.3 (1)]. Then specific intelligence onthe Plausible hypotheses is used in the Analysis of competing hypotheses to determine whichPlausible hypotheses are most likely to be true. In case insufficient intelligence is available toreject or accept hypotheses in this final process, more intelligence on these hypotheses is gathered[H.7.2.6]. So in the whole mid/long term assessment process; first intelligence is gathered todevelop many Plausible hypotheses (exploration) and then intelligence is gathered to determinewhich of the Plausible hypotheses are most likely to be true (validation) [ibid.]. The reports thatfollow from the mid/long term assessment can range from short updates for short term policyto elaborate analyses for long term policies like capability development in the region [H.7.2.8].

4.4 Verification of described intelligence processes

Since NATO nor any related intelligence services has been directly involved in this thesis ithas not been possible to really validate the results of this chapter. However we did verify withour source who provided us with the information on the intelligence processes, whether ourdescription above is accurate. The process is presented and explained to Dr. Giliam de Valk,who is an expert on intelligence methods and has a close academic relation with the DutchDefense intelligence services [H.5.1.1.1]. Based on the presentation of the complete descriptionin appendix E he confirmed that the appendix and this chapter are an accurate description ofthe Strategic Geopolitical Intelligence process as executed by NATO state intelligence services[H.8].

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Chapter 5

Results on sub question 3:Which criteria should be used tocompare the value of differentStrategic Geopolitical Intelligenceprocesses?

This chapter will provide an answer to sub question 3 by decomposing the term value of StrategicGeopolitical Intelligence into more operational criteria. In figure 5.1 is the whole decompositionpresented. The percentages next to each criteria are gathered using the survey among expertsand indicate the percentage of experts who felt the criterion is relevant to value a StrategicGeopolitical Intelligence process. The first three sections explain the three decomposition choicesand on which theories it is based. The final section will indicate how we have designed the surveywe have used to validate the results. In appendix F is every criterion explained in detail in casethe names of the criteria are not self-evident enough. All the sections in this chapter reflect oneof the colors in figure 5.1.

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Input process output:

Iron triangles:

Criteria:

Value of analysis process

Credibility of a report

Dependability of a report

Transferability of a report

External validity of an analysis

Objectivity of an analysis

Reliability of an analysis

Internal validity of an analysis

Duration of intel. gathering

Failure costs of intel. gathering

Operation costs of intel. gathering

Low costs

Short time

Fulfillment of scope

Low costs

Short time


Value of gathering –

analysis interface

Value of a Strategic


process

Required integrity of gathered intel.

Duration of an analysis

Failure costs of an analysis

Operation costs of an analysis

Value of analysis -reporting interface

Low costs

Short time


Relevance of a report to intel. task

Operation costs of reporting

Failure costs of reporting

Duration of reporting

Confirmability of a report

Validity

86%

86%

93%

86%

93%

86%

93%

100%

100%

100%

86%

93%

86%

93%

100%

100%

71%

71%

93%

Figure 5.1: Objective-tree showing the decomposition of value of Strategic Geopolitical Intelli-gence into tangible criteria

5.1 (Blue) Input ⇒ Process ⇒ Output

The first decomposition, decomposes the Strategic Geopolitical Intelligence process, into thethree aspects that can always be identified in a process: Input ⇒ Process ⇒ Output. Thisdecomposition is inspired by the IPO-model [20, p.41-43] and the concept of Black-box models[23, p.4]. The inputs and outputs are respectively coming from and going to other processesthan the analysis process. Although these processes, e.g. intercepting communication signals,are outside the scope of this thesis, the interfaces between these processes and the analysisprocess have a major influence on the overall value of the Strategic Geopolitical Intelligence1.

5.2 (Red) The Iron triangle

To decompose the term value itself we adopted the Project management triangle, also known asthe Iron triangle, that values a project in terms of; Costs, Time and Scope [117, p.4-5]. TheScope is the sum of all products that should be provided at the expected level of integrity. TheIron triangle is considered suitable since a single Strategic Geopolitical Analysis is as much aproject as any other and also has to deal with the Iron triangle dynamics. The Iron triangledynamic is that a positive change in the value of one of the aspects (Costs, Time or Scope) hasa negative effect on one or both other aspects. For example an analysis can be performed moreelaborate (high Scope value) but this will be more expensive and/or takes longer to execute.

5.3 (Green) Final decomposition

As can be read in the next paragraphs, only the costs and scope are decomposed in more tangiblecriteria.

5.3.1 Decomposition of costs

Based on project management theory we chose to make a distinction between costs coupleddirectly to the standard execution of processes and costs that are a result of the process [47,p.111]. This resulted in respectively in the criteria Operation costs and Failure costs.

Note though that the concept of costs is not only financial and should be be considered in thebroadest sense. Especially with failure costs; consider reputation loss, environmental costs, lossof lives, etc.

5.3.2 Decomposition of the scope

After the decomposition using the Iron triangle we are still left with the opaque term of scope,integrity of product, which is often used interchangeably with quality of product [101]. For in-telligence gathering we consider the Scope to be the integrity of the gathered intelligence (whichcriteria determine the integrity of intelligence is indicated in appendix E.5.3). For the scope

1This argument is based on project management body of knowledge that considers the emphasis on interfacesbetween multiple processes, a critical factor for success of each process [67, p.2-3].

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of the analysis process and reporting we derived value criteria that are also used for scientificresearch methods. The four criteria for the analysis process are the traditional four criteriafor scientific research methods, which are generally used for studies performed in a controlledenvironment [51]; Internal validity, External validity, Reliability and Objectivity. The four cri-teria for reporting are mainly used, and especially developed, for social studies [72]; Credibility,Transferability, Dependability and Confirmability. Although debates on these value criteria gen-erally are about which sets of criteria are the best [18], we chose to include both sets. In oureyes this respects both the value of the experiment itself in a controlled environment (modelingstudy), as well as the value of the conclusions the experiment will draw onto the social/openenvironment. Although the derived criteria for the different scopes are hardly measurable, theycan be qualitatively assessed [ibid.]. Furthermore we felt that an additional criterion is requiredthat judges whether the analysis provides results that were relevant to the intelligence taskthat was issued by the client / policy maker. Otherwise are methods that provides only validconclusions on a tactical level, still score well as a strategic analysis method.

5.4 (Orange) Validation of results

Similar to the validation of sub question 1 we have used a survey to validate the results ofthis chapter. We did this by posing all identified criteria and asking per criterion whether theexperts felt it is a relevant criterion. The detailed results of these survey questions can be foundin appendix J. Figure 5.1 shows a brief overview of the percentages of experts that consideredthe different criteria relevant. As can be seen in the validation block, all the results indicatequite strongly that the criteria are relevant. Two experts suggested additional criteria2 but theseseem more on the results of analyses and independent of the method (see figure J.2 in appendixJ). All the other experts considered the list of criteria to be complete.

Note that the survey gave the respondents the option to state whether they strongly agreed ordisagreed. However in hindsight we felt that the term strongly is too subjective. So we onlymade a distinction between agree and disagree.

2Examples of suggested additional criteria are Desirability and Confidentiality

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Chapter 6

Results on the main researchquestion:How can Agent-Based modeling addvalue to Strategic GeopoliticalIntelligence of NATO stateintelligence services?

In order to provide an answer to the main research question we bring together the resultsof all three sub questions. We have evaluated the value of the current Strategic GeopoliticalIntelligence process, as identified in sub question 2, using the criteria we have identified in subquestion 3. Based on this evaluation we have listed all identified limitations of the currentStrategic Geopolitical Intelligence process in sections 6.1 to 6.5. In these sections we discusswhether the abilities of Agent-Based modeling, as identified in sub question 1, are able toovercome the identified limitations. Below every section that discusses a limitation, the suggestedimprovement is presented as well as on which value criteria this has a positive effect. Afterthese sections, section 6.6 provides three additional identified limitations and suggestions forimprovements which are not directly related to Agent-Based modeling. These suggestions areuseful to take into account when redesigning the Strategic Geopolitical Intelligence processto accommodate the benefits of Agent-Based modeling. The final section will present howwe have validated the identified limitations and the expected positive effects of the suggestedimprovements.

6.1 Assessments are practically completely qualitative

As argued in paragraph 3.1 it is desirable to use quantitative methods to analyze volatile states,preferably Agent-Based modeling because this will provide more valid results (assuming theComplex Adaptive Systems paradigm). Using an Agent-Based model would also teach theanalyst more about the relevant dynamics of a case, or type of case, and thereby possibly addsome relevant insights into the report. Findings on the types of dynamics that were identified can

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also be relevant across cases, thereby adding to the transferability of the report. Furthermoreboth traditional assessments are now based on a relatively opaque brainstorm process thatdelivers a set of three scenarios or about 10 hypotheses.

If these scenarios / hypotheses would be developed using quantitative methods, like Agent-Basedmodeling, then more precise and testable scenarios / hypotheses will be developed. The valuingof the scenarios and hypotheses on likelihood and impact will also be more objective, because itis based on the likelihood of the root causes rather than on the likelihood of the scenario itselfoccurring. This opens up the possibility for near term assessments to use quantitative methodsto deduct critical indicators and use quantitative assessments to support the development of anintelligence collection plan. With a quantitative model the sensitivity of the conclusions to thegathered intelligence can be tested using a sensitivity analysis, during the intelligence assessmentprocess. The sensitivity of critical indicators to possible outcomes can also be determined, whichallows to prioritize the critical indicators on the level of overriding importance. This prioritiza-tion can save valuable time during intelligence gathering, when the focus is on intelligence witha high level of overriding importance.

Furthermore, by using Agent-Based modeling to develop quantifiable scenarios, it is also possibleto study the critical paths that lead to certain types of events. When one knows which eventsshould happen to have certain end states to occur, one has determined the critical indicators.However, knowing the critical paths, it is also possible to discover which end states do not occurwhen certain events occurred. This can be very relevant for the policy makers. Furthermoreit also allows the analyst to add critical de-warning indicators to provide negative warnings,which are not used in near term assessments and is a known limitation of near term assessments[H.6.2.2.3].

Suggested improvement: Use Agent-Based modeling to make quantifiable scenarios / hy-potheses that can be used throughout the analyses.

Improved value criteria: Duration of intelligence gathering, Internal validity of an analysis,External validity of an analysis, Reliability of an analysis, Objectivity of an analysis, Relevanceof a report Transferability of a report and Confirmability of a report.

6.2 Lacking integration of analysis methods

In chapter 4 we have indicated that the Strategic Geopolitical Intelligence process consists oftwo different methods: Near term- and Mid/long assessment. These two analyses have differentaims, focus on different time frames and are largely executed independently. However bothanalyses are both in parallel executed during each crisis state1 and have many similar processes,like the development of hypotheses/scenarios and the assessment of intelligence. It would makesense to integrate the two analyses in order to allow insights drawn from the one analysis tobenefit the other analysis and the other way around, in order to achieve a higher Externalvalidity. Furthermore, if some of the processes that are done in both analyses only have to beexecuted once in order to execute the analysis process with lower costs in a shorter time.

There is probably already some implicit integration between the two analyses since analystswill read the reports of each other, share intelligence in fora or are maybe even executing

1See table 4.1 that indicates that situation monitoring (Near term assessments) is executed during each crisisstate and that the containment task (Mid/long term assessment) is only not executed during the peace andstability crisis states.

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both processes [H.8.2.3]. However, by making the integration explicit the value of the StrategicGeopolitical Intelligence process in terms of Reliability and Confirmability would likely increase.The integration of the two analyses can largely be achieved by redesigning the structure ofprocesses using systems engineering. However the different aims and considered time framemakes that the experts develop hypotheses/scenarios with different levels of detail. It wouldtake a lot of extra effort for an analyst to develop a hypothesis over the mid/long term with thesame level of detail, an analyst makes a scenario over the near term. The different level of detailbetween the hypotheses and scenarios makes the two analyses incompatible for an extensiveintegration with each other. This problem can partly be overcome if an Agent-Based modelwould be used to develop hypotheses/scenarios, because then it would not cost extra resourcesto develop hypotheses with the same level of detail as scenarios. When hypotheses and scenariosare developed with the same level of detail it would even be possible to use the same hypothesesand scenarios to execute the entire Strategic Geopolitical Intelligence process with lower costsin a shorter time.

Suggested improvement: Make integration between Near term and Mid/long term assess-ments explicit by redesigning the structure of processes and developing hypotheses/scenariosusing Agent-Based modeling.

Improved value criteria: Analysis operation costs, Duration of an analysis, Internal validity,External validity, Reliability of analysis and Confirmability of report.

6.3 Low number of scenarios and hypotheses

The more detail is used to describe a scenario or an hypothesis, the less likely that this scenariowill occur as described. So if an analyst wants to have a set of only ten scenarios, where oneof the scenarios will occur as described, he cannot use too much detail. This can be overcomeby simply developing more scenarios/hypotheses and describing every possible combination oflikely events. Developing all these scenarios manually will make the analysis intractable, i.e.the reality will progress faster than it can be predicted. This is where one should utilize on thepower of a computer which can develop a high number of scenarios/hypotheses a lot faster thanthe human mind. By developing a high number of scenarios/hypotheses it is more likely thatthe scenario/hypothesis set contains the real future and that the scenarios/hypotheses can bedescribed in a much higher detail. This respectively adds to the external validity of the analysisand the relevance of the report to the intelligence task. The development of a lot of scenarioscan be achieved using an open-form modeling method like Agent-Based modeling since such amodel develops almost as easily five or five hundred scenarios.

Suggested improvement: Use Agent-Based modeling to develop a high number of scenariosin Near term assessment, and iterate between processes.

Improved value criteria: External validity and Relevance of a report to intelligence task.

6.4 Evaluation of input intelligence is qualitatively

The value of an intelligence report is dependent on the integrity of the intelligence used. Whenthere is some uncertainty about the integrity of the intelligence, it would be useful to quanti-tatively test the sensitivity of the near term assessment to that individual piece of intelligence.

46

Then it is possible to map the consequences of type I and type II errors, that can materialize ifthe intelligence appears not to be true. It also would show to what extend certain intelligencehas to be incorrect to yield certain consequences. These are valuable insights when policy mak-ers have to base decisions on uncertain intelligence. Furthermore, if the integrity of intelligenceis properly assessed and sensitivity analyses are executed, it would be possible to calculate thelikelihood function of developed scenarios.

Using parameter sweeps in Agent-Based models, it is possible to execute sensitivity analysesthat will help the analyst assess the likelihood of scenarios based on gathered intelligence andtaking into account the varying integrity of intelligence. When more criteria are used to assessthe integrity of intelligence, it is possible to evaluate the certainty of the integrity in more detail.This will help the sensitivity analysis to choose more suitable domains for the parameter sweeps,which will produce more distinctive results.

Suggested improvement: Use Agent-Based modeling to develop scenarios so sensitivity anal-yses can be performed and likelihoods of scenarios can be determined.

Improved value criteria: Required integrity of the gathered intelligence, External validityand Relevance of a report to intelligence task.

6.5 No explicit integration with the policy maker

When focusing on the goal of the mid/long term assessment, we see that it only aims to providea set of most likely hypothesss. Based on these hypotheses, policy makers attempt to developrelevant capabilities and policies. We feel that when Agent-Based modeling would be used todevelop hypotheses, there are many alternate goals to be achieved. Besides that the analystlearns more of the system under study itself when growing an Agent-Based model, the analysthas created a very useful tool to evaluate and even develop policies. The model can be usedto do what-if analyses to evaluate the effects of policies, taking into account every developedhypothesis.

When taking this a step further, it would even be possible to develop adaptive policies. Suchpolicies are especially useful when dealing with Deep uncertainty2, which within intelligence isassumed to be often present in Strategic Geopolitical Intelligence3. Such adaptive policies aredesigned to evolve depending on new intelligence [70, p.57]. This is a proactive way of developingpolicies, which allows the actors involved act faster on impending crises. However if one wants todetermine on beforehand which events should occur to take certain actions, one needs to knowwhat events can happen and what its effects are. These events and effects can be determinedbased on the Agent-Based model developed for the Strategic Geopolitical Intelligence process.Hence it would be very useful to combine the assessment with the capability analysis to developadaptive policies [87]. Something which is already being implemented for the Dutch NationalRisk Assessments [ibid.].

Suggested improvement: Use Agent-Based modeling to develop adaptive policies.

2Deep Uncertainty “exists when analysts do not know, or the parties to a decision cannot agree on, (1) theappropriate models to describe the interactions among a system his variables, (2) the probability distributions torepresent uncertainty about key variables and parameters in the models, and/or (3) how to value the desirability ofalternative outcomes” [70, p.3-4]. The models meant in this definition can be both qualitative and quantitative.

3This assumption is based on the little intelligence that can be gathered abroad [H.1]. This assumption islisted in appendix A.2

47

Improved value criteria: Relevance of a report to intelligence task (although developingpolicies is not really an intelligence task. For a consideration of why developing policies mightpartly should be an intelligence task, we refer to paragraph 10.3.5.6).

6.6 Additional identified limitations

6.6.1 Framework is not risk based

The Strategic Geopolitical Intelligence process, for both near- as mid/long term, is based onhypotheses/scenarios that are the most likely (trend), that have the greatest impact (worst case)or that are the most unlikely (wild card). It would make sense to base the Strategic GeopoliticalIntelligence process on hypotheses/scenarios with the highest risks. This allows the inclusion ofhypotheses/scenarios with a fairly high likelihood and a fairly high impact that otherwise wouldbe ignored because it does not stick out in either likelihood or impact. Hypotheses/scenarios witha high product of likelihood and impact, have a high risk and, should according to risk theorydrive decision making [3] in safety and security issues4. Furthermore does the adoption of theconcept of risk allow the usage of the scientific body of knowledge related to risk management,for other improvements of the intelligence methodology. This source of scientific knowledgemight lead to a lot of quality improvements since scientific research to risks is assumed to be alot greater than the scientific research to intelligence studies5.

In order to include the concept of risk and risk management into the Strategic Geopolitical Intel-ligence process is again mostly an issue of redesigning the structure of processes using systemsengineering. However when Agent-Based modeling is used to generate hypotheses/scenariosthen they can also be used to make structured and accurate assessments on the likelihood andimpact of each hypothesis/scenario.

Note that risk management should only be considered as an addition to, instead of a replacementof, intelligence analyses. Both the focus on risk values as on critical unknowns are essential inStrategic Geopolitical Intelligence [H.8.2.3].

Suggested improvement: Consider the body of knowledge on risk management. Let riskdetermine the development of hypotheses/scenarios, and use Agent-Based modeling to assessthe risks of each generated hypothesis/scenario.

Improved value criteria: Possibly every quality criteria, especially Relevance of a report tointelligence task.

6.6.2 Lacking criterion in classification of intelligence integrity

Unlike Multi-Agent systems, Agent-Based modeling is not a tool to gather intelligence. Asdiscussed in paragraph 3.5, Agent-Based modeling studies a problem and is therefore focusedon the analysis. However we still want to make two notes on the intelligence gathering. First wewant to note that the classification of intelligence integrity can be more elaborate, by using morecriteria besides reliability and credibility. Suggested criteria by Dr. de Valk are; the distance

4Thus makes the reports the most relevant to the intelligence task.5This is an assumption based on the fact that scientific risk studies are a lot older [3] than scientific intelligence

studies [H.5.2.3]. For a further elaboration on this explanation we refer to appendix A.2.

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to source and the overriding importance of crucial information/intelligence to the hypothesis /scenario [H.7.2.5]. These extra two criteria nuance every piece of intelligence better in termsof validity and impact, which will contribute to the external validity of the analysis. Second,we want to note that intelligence is gathered using a wide range of methods, which results in awide range of types of intelligence in different forms. That is why it is good that Agent-Basedmodeling is so flexible and thus able to adopt varying types of input parameters.

Suggested improvement: Consider the distance to source and the overriding importancecriteria when valuing intelligence.

Improved value criteria: External validity.

6.6.3 (A1-A3) Lack of iteration throughout Near term assessment process

When focusing only on the structure of near term assessment processes we notice that it lacksiteration between processes. The Near term assessment process is a linear sequence of three mainprocesses. Only in rare situations when mistakes are discovered in the intelligence collectionplan there is some iteration by improving the intelligence collection plan [H.7.2.1]. The lack ofiteration makes the Near term assessment more prone to errors, affecting internal or externalvalidity. This is because every process is only executed once and therefore it has to be right thefirst time.

The external validity of the set-up process can be improved if findings from the intelligencegathering are used to redefine a scenario. Furthermore the internal validity can be improvedwhen processes are executed multiple times, because then the analyst will go through his previousanalyses and might spot some errors he might have made before. Also when when intelligence isupdated, it is not always necessary to execute a whole new analysis but it does make sense to usethe old analysis insights and update the reports. Using the old insights can save time and theconstant updating of reports increases the dependability of the reports. De facto there is oftenquite some iteration, however this strongly depends on the individuals and the professionalismof the NATO state in question [H.8.2.3]. Similar to the integration, making the iteration explicitin the process will add significant value to the Strategic Geopolitical Intelligence process. Theiteration of the two analyses can largely be achieved by redesigning the structure of processesusing systems engineering.

Suggested improvement: Make the iteration throughout the Strategic Geopolitical Intelli-gence processes explicit by redesigning the structure of processes.

Improved value criteria: Duration of analysis, Internal validity, External validity, Reliabilityof analysis, Dependability of report and Confirmability of report.

6.7 Validation of the relevance of the suggested improvementsand its suggested improvements on the value criteria

For this validation we had only a single expert available with the required knowledge on intel-ligence methods and processes; Dr. Giliam de Valk. In an interview where we presented thesuggested improvements and its expected effects on the value criteria, he confirmed the rele-vance of the suggested improvements as well as their potential value improvements [H.8.2.3].In addition we have constructed a new Strategic Geopolitical Intelligence process based on the

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suggested improvements in this chapter, which we partly tested on an actual case. The processwe have designed, the execution on an actual case and the evaluation of this are presented inpart III. Here can be seen that it seems feasible to implement all the suggested improvementsinto a new Strategic Geopolitical Intelligence process and reap the benefits of it.

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Chapter 7

Discussion

This chapter will discuss the results of the previous chapter in order to provide an answer to themain research question; How can Agent-Based modeling add value to the Strategic GeopoliticalIntelligence process?. After this the practical implications of Agent-Based modeling will bediscussed and finally in paragraph 7.3 the validity of our study will be discussed.

7.1 How Agent-Based modeling can add value to the StrategicGeopolitical Intelligence process

7.1.1 As a forecasting tool in the Complex Adaptive Systems paradigm

The main reason why Agent-Based modeling can add value to the Strategic Geopolitical Intel-ligence process is because Agent-Based modeling is an analytical tool based on the ComplexAdaptive Systems paradigm. Especially for volatile states this paradigm seems quite suitable,which would let Agent-Based modeling increase the external validity of the Strategic Geopoliti-cal Intelligence process. The suitability of the Complex Adaptive Systems paradigm to perceivevolatile states is confirmed by the survey in sub question 1. During that validation the expertsrejected all assumptions that are necessary for other paradigms. We have also seen in sub ques-tion 1 that Agent-Based modeling is the only method that can study geopolitical dynamics fromthe Complex Adaptive Systems paradigm. Although technically narration is also able to reasonfrom the Complex Adaptive Systems paradigm, practically this qualitative method is not able todo so without oversimplifying the system under study. Furthermore the ability of Agent-Basedmodeling to explore across times of instability can significantly change foreign policy of NATOstates [104, p.33-35].

7.1.2 As a quantitative open-form and inductive bottom-up modeling method

Besides comparing Agent-Based modeling with other geopolitical analysis methods to identifywhich method is the most suitable to capture the Complex Adaptive Systems, we studied howAgent-Based modeling can add value as a quantitative, open-form, inductive bottom-up model;replicating a real world problem, used to study that problem. Therefore we have studied howthe current Strategic Geopolitical Intelligence process is executed and what makes a Strategic

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Geopolitical Intelligence process valuable. Described per property of Agent-Based modeling, ourstudy resulted in the following list of additional added values of Agent-Based modeling adds:

• Quantitative method

– The Strategic Geopolitical Intelligence process will become more explicit and pro-cess driven. This should make the process more objective, increase internal validity,increase reliability and increase confirmability.

• Replicative (modeling) method:

– Serious gaming is enabled which allows direct adoption of human behavior into studyand improve the understanding of the study more to the user.

• Open-form modeling method

– A high number of risk scenarios can be taken into account, thereby decreasing thelikelihood of unexpected events and allowing the development of more detailed sce-narios.

– Sensitivity analyses can be executed to assess intelligence, to take into account the un-certainty of intelligence, identify critical unknowns, determine de-warning indicatorsfor negative warnings, prioritize indicators in the intelligence collection plan, providebandwidth values for relevant critical unknowns and develop more accurate condi-tional conclusions. This will allow more intelligence to be used, increase effectivenessof intelligence gathering and assessment and be explicit about the uncertainty of theconclusions.

– Explicit integration between near term- and mid/long term assessments can be re-alized, due to the same detail the Agent-Based model will provide for both types ofscenarios. Thereby making the process less costly, increase internal validity, reliabilityand confirmability.

– (Adaptive) Policies can be deducted and developed based on the risk scenario set,thereby making policies more effective and pro-active.

• Bottom-up (inductive) modeling method

– The intelligence analyst can gain a better understanding of the dynamics of thesystem under study, thereby increasing the transferability of the analysis as well asthe external validity in the long run.

As can be seen the implementation of Agent-Based modeling into the Strategic GeopoliticalProcess, enables a lot more abilities than just the ability to analyze volatile states from theComplex Adaptive Systems paradigm.

7.1.3 The need for the added value of Agent-Based modeling

After identifying the added value of Agent-Based modeling we wondered how much these bene-fits are needed by intelligence services. We decided to check this by comparing the added valuesof Agent-Based modeling with the most prevalent limitations of intelligence as identified byextensive evaluations of intelligence practices. We chose to use the study by Lefebvre [69] onintelligence practices of NATO states in general and the evaluation by Heuer on The Senate Se-lect Committee on Intelligence Report on the U.S. Intelligence Communitys Prewar Intelligence

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Assessments on Iraq [61]1. These two studies considered the following as the most prevalentlimitations of intelligence services:

• Occasional surprise is inevitable in intelligence [61, p.77];

• Cognitive limitations:

– Analyst and intelligence organizations tend to prefer self-promotion over putting for-ward valid but unwelcome judgements and insights [69, p.6] (also in the survey iden-tified as a problem, see figure J.2);

– Mental models from analysts, though derived from academia, make analyses and theirresults opaque and dependent of the individual [ibid. p.12];

– The human mind cannot cope with the complexities in the world and therefore con-structs a too simplified mental model [61, p.85];

– Perception of information varies per individual and strongly determines the outcomeof the analysis [ibid. p.79-80];

– The human mind is fundamentally reluctant to shift its mindset even under changingconditions. Even when the person wants to change his mindset it is difficult [ibid.p.80-82].

• Methodology limitations:

– Forecasts are strongly based on linear extrapolation, whilst relevant dynamics aregenerally non-linear [69, p.13];

– Current methods should consider complex systems theory more [ibid. p.25-26];

– The effects of the precautionary principle2 should be more carefully considered [ibid.p.31-33]; and

– Analysis and communication on uncertainty of results is not explicit enough [61,p.87-88].

After comparing the added value of Agent-Based modeling with the list above, one can see howwelcome these added values are to the intelligence process. All the cognitive limitations canbe improved due the quantitative nature of Agent-Based modeling that limits the subjectiveinfluence of the analyst, is able to compute through a high number of interrelations and makesthe process explicit to allow checks. Also the methodology limitations are less when usingAgent-Based modeling because it is a non-linear analysis method and it can elaborately studycause-effect relations and uncertainty ranges explicitly. So Agent-Based modeling provides somevery valuable extra value although we must not forget that the occasional surprise in intelligenceremains inevitable.

1Both authors have worked as an intelligence analyst and have received broad recognition for their work onstudying intelligence processes [69] [61].

2The precautionary principle basically means that “when an activity raises threats of harm to human health orthe environment, precautionary measures should be taken even if some cause and effect relationships are not fullyestablished scientifically”[5, p.18]

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7.2 Practical implications of Agent-Based modeling

After developing and executing the new Strategic Geopolitical Intelligence process in respec-tively chapters 10 and 11, we have identified practical implications of Agent-Based modeling inStrategic Geopolitical Intelligence in section 12.1. These practical implications are:

• Using relative values as well as wide parameter ranges allows the Agent-Based model tocapture relevant but intangible geopolitical dynamics;

• Developing an Agent-Based model and keeping a development log can be very time con-suming;

• Experimenting with an Agent-Based model for Strategic Geopolitical Intelligence is verycomputational expensive; and

• People who do not understand the Complex Adaptive Systems paradigm well enough orhave a perpetual fear of math and computers, are hesitant to trust results from an Agent-Based modeling study.

The last three implications are quite troubling limitations of Agent-Based modeling. Due to therequired extra time and computation power, the suitability of Agent-Based modeling becomesvery case specific. To put it into perspective we have asked for the experiences on processdurations of both an intelligence expert and an Agent-Based modeling expert. Table 7.1 showstheir answers.

Table 7.1: Perspective of process durations

RangingProcess from to Source

Near term assessment (upon request)3 1 Day 1 Week I.6

Mid/long term assessment 1 Month 3 Months I.6

Constructing an operating Agent-Basedmodel on Mali based on the formal

model presented in chapter 111 Month 5 Weeks I.7

So whether Agent-Based modeling can be used depends on the urgency of providing results andwhether a new Agent-Based model can be built on top of an older version (hence the iteration andre-use of the Agent-Based model in the suggested Strategic Geopolitical Intelligence process).As an alternative to Agent-Based modeling the analyst can revert to narration, which was in thevalidation of chapter 3 not convincingly rejected by experts anyway. It will not only save timeand computational power, it will also make the results more credible to policy makers who havea perpetual fear of math and computers. Thereby narration loses all the limitations presentedabove but it will also lose all the added values presented in the previous paragraph. The otheralternative, which was in chapter 3 not convincingly rejected as well, is System Dynamics4.System Dynamics is also a quantitative open-form modeling method and can therefore utilizealmost all additional added values of Agent-Based modeling as well. A relevant (Exploratory)

4The other deductive (top-down) modeling methods are discrete and focus more on operational dynamics,instead on strategic dynamics like System Dynamics [14, p.4].

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System Dynamics model can be built, and will yield relevant results in one day [88]. However,a valid System Dynamics model requires the assumption that the structure of the volatile stateunder study is static which is still considered impossible by almost a two-third majority ofexperts. The validity of the assumption of a static structure strongly depends on how volatilethe state of interest really is.

So when time is too limited to use Agent-Based modeling and the system structure of a state canbe considered relatively static, it is advised to use System Dynamics rather than narration orAgent-Based modeling. Especially for near term assessments which generally requires the mosturgent analyses and only studies a few months in the future, it is likely that a certain systemstructure can be assumed static for the duration of the analysis. The problem of computerfearing policy makers remains with System Dynamics. However for both Agent-Based modelingand System Dynamics this should be possible to overcome by clearly informing the reader of thereport about the method and why it does provide valid insights. In addition we would adviseto make the policy makers more familiar with the modeling methods through animations andserious games.

7.3 Discussing the validity of the thesis

Throughout the thesis that our arguments and findings are well validated by literature, expertsand experimentation. However we do acknowledge three limitations of our research that affectsthe validity of our thesis. We will discuss these limitations and its implications below.

7.3.1 Lack of NATO involvement

Even though the Strategic Geopolitical Intelligence process of the NATO is central to this thesis,we hardly had direct contact with NATO or one of the NATO state intelligence services. Thisaffects especially the validity of the identified Strategic Geopolitical Intelligence process, therebythe validity of the main research question and the credibility of this thesis. All the informationon the identified Strategic Geopolitical Intelligence process was acquired via a single expert,making the validity of our thesis very dependent on this expert. However this expert lecturesin intelligence processes, is well connected to the Dutch defense intelligence services, providedus with NATO Doctrines and consulted active intelligence analysts to answer our questions[H.5.1.1.1] [I.4]. Furthermore we also had some direct contact with an active defense intelligenceanalyst [H.4] [I.6]. So although we were not able to validate our identified Strategic GeopoliticalIntelligence process with NATO or a NATO state intelligence service, we are still quite confidentabout the validity of our findings.

7.3.2 Limitations of our survey

In order to validate sub questions 1 and 2 we have set-up a survey that was filled in by 15relevant respondents. Due to the positive feedback by some respondents on the survey qualityand the high consensus on most questions we are confident that the survey validated our resultspowerful enough. However in hindsight we should have tested the survey with a few experts,improve the survey based on their feedback and then roll it out to all the respondents. Basedon the feedback we discovered that some of the questions are a bit ambiguous and that we

55

should have given the respondent space to write comments next to a question. Improving theselimitations would have made the validation power of our method more powerful and would likelyprovide us more insights. Furthermore, if we had more experts on each area of expertise, thevalidation power would increase and we could gain expertise specific insights (e.g. identify thedifference in perspective by different types of experts).

7.3.3 Exploratory nature of study

By answering how Agent-Based modeling can add value, we only provide a list of opportunitiesthat Agent-Based modeling provides to increase the value of the Strategic Geopolitical Intelli-gence process. This underlines the exploratory nature of this study. Although we made effortsto validate our findings as much as possible we never intended to provide a validated guide tohow Agent-Based modeling will add value to the Strategic Geopolitical Intelligence process. Forsuch a thing, a more empirical study is required in cooperation with a NATO state intelligenceservice which compares an Agent-Based modeling driven Strategic Geopolitical Intelligence pro-cess with the current process. The relevance of our study is that it indicates that such anempirical study should be worth the investment of NATO or a NATO state intelligence service.Our study also provided a first suggestion of a Strategic Geopolitical Intelligence process thatuses Agent-Based modeling which can be used for that empirical study. This suggested StrategicGeopolitical Intelligence process should then be iteratively improved throughout the empiricalstudy.

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Chapter 8

Conclusion

This chapter will present the main conclusion of our hypothesis and main research question. Inaddition we present the added value of this thesis to science and society.

8.1 Main conclusion

This thesis has presented the hypothesis that the introduction of Agent-Based modeling toNATO intelligence services can add value to their Strategic Geopolitical Intelligence. We basedthis hypothesis on two things; 1) the assumption that the most realistic way of studying a volatilestate is to perceive it from the Complex Adaptive Systems paradigm and 2) our expectationthat an Agent-Based modeling study is the best way to study a Complex Adaptive System. Toexplore the validity of our hypothesis, we decided to perform an exploratory study to answerthe following main research question: How can Agent-Based modeling add value to StrategicGeopolitical Intelligence?

Since there was no direct cooperation with any NATO state intelligence service, let alone thepossibility to test our findings, we are unable to make fully validated claims on how Agent-Basedmodeling will add value to Strategic Geopolitical Intelligence. The scope of the present thesis islimited to our studies on the abilities of Agent-Based modeling, how the Strategic GeopoliticalIntelligence process currently is executed and what makes a Strategic Geopolitical Intelligenceprocess valuable. Our study provided powerful indications that our hypothesis is valid and thatAgent-Based modeling can add value to Strategic Geopolitical Intelligence by using it to developrisk scenarios. When Agent-Based modeling is used to develop risk scenarios it opens up thefollowing opportunities that can increase the value of Strategic Geopolitical Intelligence:

• Risk scenarios can be built in line with the Complex Adaptive Systems paradigm, therebyincreasing the external validity of analyses of volatile states;

• A high number of risk scenarios can be taken into account, thereby decreasing the likelihoodof unexpected events and allowing the development of more detailed scenarios;

• Sensitivity analyses can be executed to assess intelligence, to take into account the un-certainty of intelligence, identify critical unknowns, determine de-warning indicators fornegative warnings, prioritize indicators in the intelligence collection plan, provide band-width values for relevant critical unknowns and develop more accurate conditional conclu-

57

sions. This allows for more intelligence to be used, providing more focus in the intelligencegathering process and be explicit about the uncertainty of the conclusions;

• The intelligence analyst can gain a better understanding of the dynamics of the systemunder study, thereby increasing the transferability of the analysis as well as the externalvalidity in the long run.

• The Strategic Geopolitical Intelligence process will become more quantitative, therebymaking the process more objective and increase internal validity, reliability and confirma-bility;

• Explicit integration between near term- and mid/long term assessments can be realized,due to same detail the Agent-Based model will provide for both types of scenarios. Therebymaking the process less costly, increase internal validity, reliability and confirmability; and

• (Adaptive) Policies can be deducted and developed based on the risk scenario set, therebymaking more effective and pro-active policies.

So besides increasing the external validity of the analysis by being able to built scenarios in linewith Complex Adaptive Systems, we have discovered that the use of Agent-Based modeling alsopositively affects other important aspects of Strategic Geopolitical Intelligence.

We have designed a new Strategic Geopolitical Intelligence process that utilizes all these benefitsof Agent-Based modeling. Based on literature and our own experience with executing a largepart of this process on an actual case, we are confident that it is possible to implement ournew Strategic Geopolitical Intelligence process design into practice. This supports our hypoth-esis that Agent-Based modeling can add value to Strategic Geopolitical Intelligence. However,we have also discovered that integrating Agent-Based modeling into the Strategic GeopoliticalIntelligence process can significantly extend the duration of the analysis.

Therefore, it depends on the case and the available time for the analysis, whether it is sensibleto apply the new Agent-Based modeling driven Strategic Geopolitical Intelligence process orthe traditional Strategic Geopolitical Intelligence process. Furthermore, since the new Strate-gic Geopolitical Intelligence process has rigorously changed the analysis process, it might besensible from an implementation point of view to first execute the new process in parallel tothe traditional one. Although this initially will increases operational costs of the analyses, itwould also allow future research to validate, optimize and standardize our suggested StrategicGeopolitical Intelligence process.

8.2 Summarizing the added value of this thesis

All our efforts in this thesis have contributed to the conclusion described above. Some of theseefforts also added value to science and society because the thesis has;

• Positioned Agent-Based modeling among other types of analysis methods that can be usedfor Strategic Geopolitical Analyses;

• Described the Strategic Geopolitical Intelligence process of NATO intelligence services instandardized process diagrams (IDEF0);

• Provided criteria to value a Strategic Geopolitical Intelligence process, which is both usefulto value the design as to value an individual execution of the process.

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• Identified points of improvement of the current Strategic Geopolitical Intelligence process,based on the IDEF0 diagrams and literature;

– Use more quantitative methods and depend less on the subjectivity of individuals;

– Introduce de-warning indicators in warning based intelligence;

– Introduce the security risk management body of knowledge to improve risk relatedaspects in the intelligence process;

– Introduce the distance to source criterion for valuing intelligence;

– Develop a bigger scenario set to take into account more possible situations;

– Make integration of near term- and mid/long term assessments explicit;

– Make iteration throughout the Strategic Geopolitical Intelligence process explicit;and

– Make the involvement of intelligence analysts into policy making more explicit.

• Suggested a new Strategic Geopolitical Intelligence process with Agent-Based modeling,which can be used as a starting point for future studies and implementation.

• Provided an example of how the first processes of a new Strategic Geopolitical Intelligenceprocess could look like for Mali.

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Chapter 9

Recommendations

Based on the research, we have a set of recommendations for future research. We have twocategories of recommendations and present them accordingly in different sections.

9.1 Recommendations to improve the validity of our study

• Study the assumption of Complex Adaptive Systems for Strategic Geopolitical Analysesand consider its validity for different types of geopolitical systems.

• Study the cause of discrepancy between the state of science and the state of practice.

• Study the possibilities of Agent-Based modeling for Strategic Geopolitical Intelligence inclose cooperation with NATO or a NATO state intelligence service.

• Consult more (varied) experts on Agent-Based modeling, International security and In-telligence practices. Preferably also experts with knowledge and experience across thesedomains.

• Execute the full Strategic Geopolitical Intelligence process for a specific case let NATO orintelligence service score the results of this process on identified criteria. Compare scoreswith traditional process on same case.

• Perform the above multiple times for different type of cases, e.g. in different stages of thecrisis management cycle or different kind of current regimes.

• Identify the added value of all the different suggested improvements separately.

9.2 Recommendations to improve the new Strategic Geopoliti-cal Intelligence process, as suggested in the next part of thisdocument

• Study the implementation of Agent-Based modeling into other domains like economics andidentify opportunities and threats for the implementation of Agent-Based modeling intoStrategic Geopolitical Intelligence.

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• Optimize the new Strategic Geopolitical Intelligence process by studying the preferredtrade-off of NATO state intelligence services, between time, costs and accuracy of the newStrategic Geopolitical Intelligence process.

• Study to which extend certain processes or choices can be standardized. Thereby depend-ing less on the individual intelligence analyst and speed up the whole process.

• Execute the proposed Strategic Geopolitical Intelligence process with a different modelingmethod. Preferably a quantitative open form modeling method which allows the sameprocess steps, e.g. System Dynamics. Compare results and study varying suitabilitydepending on case.

• Study the possibility to couple multiple analysis methods with each other, e.g. geographicinformation systems analyses for tactical purposes with the Agent-Based model.

• Study the value that Agent-Based modeling can add to Tactical- and Operational Geopo-litical Intelligence. Also think of possible couplings of models or processes.

• Study how different aspects of the new Strategic Geopolitical Intelligence process can sep-arately be used, for both soft implementation purposes as for customization to intelligencetasks.

• Study the possibilities to increase integration/cooperation between intelligence analystsand policy makers.

• Study the possibilities to integrate Serious Gaming into an Agent-Based model in orderto capture (military) strategies of armies and train (military) decision makers.

• Study the opportunity Agent-Based modeling provides for the development of new typesof policies.

• Study what is necessary to let policy makers make the right conclusions based on anAgent-Based modeling study.

• Consider other system engineering analysis methods than IDEF0 to study Strategic Geopo-litical Intelligence processes.

• Compare intelligence frameworks with risk management frameworks and study the differ-ent paradigms and possible mutual learning points for both practices and sciences.

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Part III

Suggestion to NATO

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Chapter 10

Suggested Strategic GeopoliticalIntelligence process

10.1 Introduction to the discussion

Before presenting the new Strategic Geopolitical Intelligence process, we will present and discussthe guidelines that were used to develop the new process. After this, section 10.3 will presentthe process and will indicate along the way how the guidelines shaped the process design.Throughout this chapter will be often referred to paragraphs in appendix E which shows thecomplete overview of the current Strategic Geopolitical Intelligence process.

10.2 Guidelines for the new Strategic Intelligence process

The starting point for the new design we considered the general aim of an Agent-Based model.As discussed in paragraph 3.5, Agent-Based modeled are meant to study a problem. This is whythe designed process is focused on the analysis process of Strategic Geopolitical Intelligence. Theprocesses of intelligence gathering and reporting are only considered to be interfaces, to whichthe new process has to be compatible with. Then to fulfill the suggested improvements of chapter6, there were three main requirements of the new design:

1. Introduce Agent-Based modeling and utilize its benefits that were suggested in chapter 6;

2. Utilize on the available knowledge on risk management; and

3. Integrate the near term assessment with the mid/long term assessment.

The main guideline for the introduction of Agent-Based modeling was the 10-step framework ofcreating an Agent-Based model by van Dam et.al. [38, p.74-p.136]. This book was used becausethis was the most readily available literature on how to develop and use an Agent-Based model.We used the Security Risk Management Body of Knowledge (SRMBOK) [103] as a source ofknowledge on risk management. This book was used because it is a very comprehensive bookon security risk management with a practical viewpoint, and a special section on intelligence insecurity risk management. The main guideline we took from that book is the risk managementprocess framework they strongly recommend for intelligence [ibid. p.228]. This framework,which is in line with the first two processes of the Observe, Orient, Decide and Act Loop

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[ibid. p.226-229], is shown in figure 10.1. Finally integrating the near term assessment with themid/long term assessment was based on our own sense of logic.

Figure 10.1: Security Risk Management process design for intelligence advised by the SecurityRisk Management Body of Knowlegde [capture from the book [103, p.228]]

10.3 Proposed Strategic Geopolitical Intelligence process withAgent-Based modeling

The new process that includes Agent-Based modeling, is represented using IDEF0 in figure 10.2.As can be seen there is now just one Strategic Geopolitical Intelligence process, instead of two.However the outgoing near term assessment reports and the mid/long term assessment reportswill produced with the same frequency and in the same format as in the traditional processes.How the 10-steps of Van Dam et. al., and the risk management framework of the Security RiskManagement Body of Knowledge are in line with the new designed process, can be seen in table10.3. The individual processes are described in the paragraphs below the table.

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Figure 10.2: α0 diagram of the proposed Strategic Geopolitical Intelligence process

Table 10.1: Overview of the integration into the process de-sign

Process IDDescribed

inparagraph

Part of 10-steps of developingan Agent-Based model

Part of Risk managementframework

Gatheringintelligence

α1 10.3.1System identification

(Inventory)Establishing context

Definingintelligence

problemα2 10.3.2

Problem formulation andactor identification

Establishing context

DevelopingAgent-Based

modelα3 10.3.3

System decomposition(Structuring)

Concept formalisationModel formalisation

Software implementationModel verification

Identify risks

Developing riskscenarios

α4 10.3.4

Developingexperimental

setupα41 10.3.4.1 Experimentation (setup) Identify risks

Determiningcriteria weights

α42 10.3.4.2 Problem formulation Establishing context

Determininglikelihoods ofinput values

α43 10.3.4.3 Experimentation (setup) Assess the risks (likelihood)

Constructing riskscenarios

α44 10.3.4.4Experimentation (execution)

Data analysisAssess the risks

Analyzing riskscenarios

α5 10.3.5

Validating riskscenarios

α51 10.3.5.1 Model validation Assess the risks

Identifyingcritical indicators

α52 10.3.5.2 Model use Monitor

Continued on next page

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Table 10.1 – Continued from previous page

Process IDDescribed

inparagraph

Part of 10-steps of developingan Agent-Based model

Part of Risk managementframework

Developingintelligence

monitoring planα53 10.3.5.3 Model use Monitor

Reporting onnear term risks

α54 10.3.5.4 Model use Communicate

Reporting onmid/long term

risksα55 10.3.5.5 Model use Communicate

Developingadaptive policy

adviceα56 10.3.5.6 Model use

Treat the risksCommunicate

Assessingintelligence

updateα6 10.3.6 Model use Monitor and review

10.3.1 (α1) Gathering intelligence

The process of gathering intelligence remains the same and does not require any changed to becompatible with the new process design. However recent models in Strategic Geopolitical Sci-ence, including Agent-Based models, manage to benefit from the integrated use of GeographicalInformation Systems (GIS) [57] [121]. Due to this integration, new models can be connected toexisting GIS datasets which saves the modeler a lot of effort to find and prepare the right inputdata for his models. This benefit could also be achieved in Strategic Geoplitical Intelligencewhen intelligence gathering will be focussed on geospatial intelligence (GEOINT). This intel-ligence gathering principle, mentioned in paragraph E.5.2, aims to store gathered intelligencein GIS databases. Examples of such GIS databases on geopolitical conflicts, that have beenintegrated into Agent-Based models before, are: Uppsala Conflict Data Program GeoreferencedEvent Dataset [107] [I.3] and the Armed Conflict Location and Events Dataset [52] [119, p.1182-1183]. Finally we would like to suggest to include the distance to source criterion when valuingintelligence. This criterion provides an important nuance to the value of a certain piece of intel-ligence and is useful for every type of intelligence analysis method [H.7.2.5]. Due to the abilityof an Agent-Based model to perform sensitivity analyses, it is possible to assess the value of theintelligence as a whole in more detail. See process α6 in paragraph 10.3.6 on the assessmentof intelligence updates, where the value of intelligence is considered and where the overridingimportance of the intelligence is determined.

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10.3.2 (α2) Defining intelligence problem

Defining the intelligence problem in the new process design includes besides the problem formu-lation, also the identification of actors and criteria. These three aspects are explained below.

10.3.2.1 Problem formulation

The problem formulation remains similar to defining the warning problem, as mentioned inparagraph E.4.1, but should also include the following aspects:

• The exact lack of insight that is addressed;

• The observed emergent pattern of interest;

• Whether there is a desired emergent pattern, and if so, how it is different from the observedemergent pattern; and

• The initial hypothesis on how the emergent patterns emerge, or why the observed anddesired emergent patterns differ.

Including these aspects is in line with what Van Dam et. al. prescribes for the problem formu-lation phase of developing an Agent-Based model [38, p.75].

10.3.2.2 Actors

Identifying the relevant actors is also in line with Van Dam et. al. [ibid.]. We would suggestto do this by executing an actor analysis according to Enserink et. al. [45, p.106-121], whoprescribes the following six steps:

Step 1: Formulate a problem (which is the same as the problem formulation, described above).

Step 2: Make an inventory of all involved actors and identify the problem owner.

Step 3: Represent a formal actor map that shows the tasks and relation between actors.

Step 4: Determine the interests, goals and problem perception of each actor.

Step 5: Map the interdependencies between actors by identifying sources of support and informalrelations.

Step 6: Determine the consequences of these findings for the problem formulation.

Making such an elaborate actor analysis helps the modeler to identify important relations, whichare not so clear at first sight but are strong enough to invoke emergent behavior. Howeverdepending on the time available and the required level of insights, the modeler can also chooseto only make an inventory of all involved actors as suggested by Van Dam et. al. [38, p.75].

10.3.2.3 Criteria

For the risk management approach it is important to define impact criteria on beforehand. Thesecriteria help the analyst to differentiate between less and more desirable emergent patterns.Criteria should be measurable and follow from the problem formulation. A tested method todeduct measurable criteria from a problem formulation is to make an objective tree, as we did

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in chapter 5, to deduct tangible criteria from the concept of value of a Strategic GeopoliticalIntelligence process. However it is also possible to borrow criteria from literature, for examplethe ones that the Fund for Peace uses to create the well known Failed State index [50].

Note that we do not intend to use these criteria as predictors. Just as indicators of a level ofconsequence/damage/impact at time of measurement.

10.3.3 (α3) Developing Agent-Based model

Developing an Agent-Based model consists out of five steps which should be iteratively executed;Structuring intelligence, concept formalisation, modal formalisation, software implementationand model verification. Throughout the execution of these five steps, the modeler should beaware of his role in the Strategic Geopolitical Intelligence process. His role is to translate thegathered intelligence into an Agent-Based model that replicates the case of interest. This modelshould be able to provide a range of plausible future scenarios which are required to report onshort-term risks, to develop an intelligence monitoring plan and to develop an adaptive policyfor the problem owner.

10.3.3.1 Structuring intelligence

The first step to develop the Agent-Based model is to structure the available intelligence anddetermine what should be modeled. As prescribed by Van Dam et. al. [38, p.78-81] this shouldbe done by executing the following steps in an iteratively fashion:

Step 1: Identify the relevant agents and objects.

Step 2: Identify the relevant attributes of the identified agents and objects.

Step 3: Identify the relevant interactions between the identified agents and objects.

Step 4: Identify which attributes of the identified agents and objects are affected by the inter-actions and which value changes in the attributes affect other attribute values or invokeinteractions.

Step 5: Note which agents, interactions and behaviors are dynamic and which are static and atwhat time frame.

Step 6: If necessary, organize agents hierarchically by ordering them in a nested way (also thinkof the formal actor map mentioned in paragraph 10.3.2.2).

Step 7: Iterate by re-evaluating the above steps by:

• Check if there are any agents, objects, states, interactions identified in the intel-ligence gathering process that have not been identified in the previous steps, butare important to the model.

• Check if there are any agents without any ingoing or outgoing interactions, and de-termine whether these agents should be eliminated or that interactions or missing.Or the other way around; check whether there are interactions without agents.

Step 8: Determine what variables should affect the model, but are not part of the model, andare thus part of the environment.

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10.3.3.2 Concept formalisation

The first translation step from the gathered and structured intelligence into an Agent-Basedmodel, is the concept formalisation. Concept formalisation aims to remove all ambiguity ofall the concepts that will be used for the Agent-Based model [38, p.83]. Only concepts thatare explicit and formal, can be understand by a computer. To achieve this we advise to applythe UML Class diagram. This diagram follows the Unified Modeling Language (UML) which isaccepted by the International Organization for Standardization (ISO) as an industry standardfor modeling software systems [63] [64].

10.3.3.3 Model formalisation

The model formalisation is the last step before the model will be actually constructed. Thisstep will result in the architecture of the model, which a programmer can use to implement themodel into software. Since it is a generative model, the architecture consists out of a narrative ofhow and when which agents act and interact in order to let patterns and regularities emerge [38,p.87-88]. Again we advice to use the Unified Modeling Language (UML) to make the narrativeexplicit, this time in an UML activity diagram. The computational actions of every agent peractivity should be described too in order to make the narrative complete. Van Dam et. al.also prescribe to write a pseudo-code as part of the model formalisation. This is per action anexplicit description of how the computations should be programmed into software [38, p.89-91].

10.3.3.4 Software implementation

If the previous steps are executed properly, then programming the model itself should not be dif-ficult for any experienced programmer. However the programmer should choose wisely in whichmodelling environment he programs the Agent-Based model. Common modeling environmentsare NetLogo, Repast and custom code like Java [38, p.94-95]. For an extensive discussion onwhen to use which modeling environment we would like to refer to Van Dam et. al. [ibid.].

10.3.3.5 Model verification

After the model is implemented in software, the modeler should verify whether the formal modelis correctly translated into computer code. Van Dam et. al. prescribes the following four mainparts to verify models, which are extensively discussed in their book [ibid.]:

1. “Recording and tracking agent behavior, in which relevant metrics are identified and recorded1;

2. Single-agent testing, in which the behavior of a single agent if verified;

3. Interaction testing limited to minimal model, in which the interaction between agents istested; and

4. Multi-agent testing, in which the emergent behavior of multiple agents is examined.”

1These metrics should at least be the pre-determined criteria

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10.3.4 (α4) Developing Risk scenarios

When the model is verified, the model can be used to develop risk scenarios. Risk scenariosare generated through, what Van Dam et. al. would call, experimentation and data analysis2

[38, p.106-116]. Before continuing we want to make a distinction between three terms that caneasily be confused with one another:

• Simulation: A series of events over time, generated by the Agent-Based model with acertain parameter configuration and a certain random seed;

• Experiment: A set of simulations that all use the exact same parameter configuration buthave a varying random seed3; and

• (Risk) scenario: A cluster of simulations that all score similar values on all metrics oneach time step and therefore can be considered as a single story. When a certain risk valuecan be attached to a scenario, it becomes a risk scenario.

When we prepare the execution of the model properly, we can automatically generate riskscenarios and then we only need to describe in words how the risk scenario looks like. To dothis we first need to determine how we will use the Agent-Based model to generate scenarios,how important each criteria is and what the likelihood of each parameter value is. These stepsof developing risk scenarios are in line with the Security Risk Management Body of Knowledge[103, p.228] and are presented in the IDEF0 in figure 10.3. Below is each sub process separatelydiscussed.

2Van Dam et. al. describe two different approaches for experimentation, depending on the type of hypothesis.This Strategic Geopolitical Intelligence process will always be experimenting in order to explore in what possible(future) worlds we might find the emergent behaviors of interest, which is in terms of Van Dam et. al. a Type 2hypothesis.

3All Agent-Based models contain random generators, due to the assumption of parallelism (see appendixC), therefore two simulations with the same parameter configuration can behave differently. This is why eachparameter configuration is executed multiple times in a single experiment.

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α41

Developing experimental

set-up

Intelligenceanalyst

Doctrine

Generalintelligence

Agent-Basedmodel

α44

Constructing risk scenarios

Intelligenceanalyst

Doctrine

Riskscenarios

Agent-Basedmodel

Experimental setup

α42

Determining criteria weights

Intelligenceanalyst

Doctrine

Criteria

Generalintelligence

Weighted criteria

α43

Determining likelihoods of input values

Intelligenceanalyst

Doctrine

Generalintelligence

Agent-Basedmodel

Likelihoods of input values

Invalid risk scenarios

Expectedcritical

intelligence update

Figure 10.3: α4 diagram of the proposed Strategic Geopolitical Intelligence process: Developrisk scenarios

10.3.4.1 (α41) Developing experimental setup

The experimental setup contains the domains for the parameters, the number of runs, thesampling method, the size- and the amount of the time steps. The wider the domains, the higherthe number of simulations in a single experiment, the more extensive the sampling, the smaller-and the more the time steps, the more complete the study becomes but at the cost of computingpower. A smart trade-off needs to be made between the desire to execute a complete as possibleexperiment and the computing time/power available [38, p.106-116]. Although this trade-offshould be made per situation, we would generally advise to use relatively wide domains for theparameter input values, as well as a relatively high number of simulations in each experiment,Latin Hypercube Sampling, time steps of one day and two time frames. One short time frameand a long time frame for the policy analysis aim of the Agent-Based modeling study. The widedomains of parameters are suggested because the deep uncertainty involved with the analysisof volatile states makes it hard to know what parameter values are accurate, especially since ingeopolitical models opaque attributes like moral are also modeled. For the same reason are ingeopolitical models also a lot of random variables used, which makes the model prone to chaos.Therefore a high number of simulations per experiment are required to be able to make reliable

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statements. Finally in order to explore the full domains of the parameter values, without makingthe experiments with the wide parameter domains and high number of runs too computationalexpensive, it makes sense to use a sampling method. By taking only samples from the parameterdomains, not every parameter value has to be run. However to still explore the full extend of theparameter domains, one should use Latin Hypercube Sampling to make sure that the sampleswill be chosen uniformly over the parameter domains.

10.3.4.2 (α42) Determining criteria weights

Basically the consequences of each simulation are the scores on the pre-determined criteria atthe end of a simulation. However to be able to get an individual risk value for each simulation,it is important to be able to combine the scores into a single dimensionless consequence value.This should be done by normalizing each score on each criteria, with 0 equals most favorableand 1 equals least favorable consequence. Then each normalized score should be weighted bymultiplying by a number from 0 to 1, from not- to extremely important to the problem owner.Finally the average of the weighted normalized scores should be determined to end up with asingle dimensionless consequence value, with 0 equals most favorable consequence and 1 equalsleast favorable consequence. All these computations will be executed automatically by theAgent-Based model when it is being run. However the weight values of each criteria shouldbe determined manually together with the problem owner. This process is technically part ofdefining the intelligence problem. However since the interests of problem owners can shift everytime the insights significantly changes, it is important to update the criteria weights every timenew scenarios are developed.

10.3.4.3 (α43) Determining likelihoods of input values

The likelihood of each simulation will also be computed automatically, but it requires someoneto determine the likelihood of each parameter input value. This should be done by sketching aprobability distribution over each domain of parameter inputs. These probability distributionscan be based on expert judgements and gathered intelligence. The value of the intelligenceshould be taken into account when determining the likelihoods. For some opaque parametervalues, e.g. moral, it is difficult to determine probability distributions based on expert opinionor available intelligence. Therefore it is important to calibrate these parameter values beforesketching probability distributions of the likelihoods. Preferably the model is calibrated with thepast of the case under study, but it should also be possible to calibrate with similar cases althoughthis requires a strong argument on why that other case is really similar to the case under study.For an example on how to calibrate an Agent-Based model for a Strategic Geopolitical Analysis,we refer to Bhavnani et. al. who did this recently for civil violence in Afghanistan based ona GIS dataset [10, p. 45-46]. Depending on the type of parameter, the available intelligenceand the available expert knowledge, one should sketch the probability distributions around thecalibrated values. The vaguer the parameter and the less we know about the parameter value,the more uniform the distribution has to be. This simply because we do not know which valueis more likely than another value within that parameter domain. The other way around, ifit is a clear parameter where we know a lot about, then the probability distribution will belooking a lot more like a normal distribution with a small standard deviation. In the model thelikelihood of each simulation result is determined by multiplying all the likelihood values of allthe parameter values used for that simulation.

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Note that the calibration of parameters includes the probability distributions inside the model toreplicate the, seemingly, random events in the world. However after these probability distribu-tions inside the model are calibrated, they should not be taken into account for determining thelikelihood of a certain scenario. Unless multiple probability distributions are sketched for a singleparameter, then the likelihood of which probability distribution is an accurate replication of thatrandom event should be taken into account for determining the likelihood of a certain scenario.

10.3.4.4 (α44) Constructing risk scenarios

After determining the weights of the criteria, the likelihoods of the input parameters and theexperimental setup, the Agent-Based model can finally be used to construct risk scenarios.As opposed to the about ten scenarios developed with the traditional Strategic GeopoliticalIntelligence methods, the wide parameter domains and the high number of simulations willprovide thousands of different possible risk scenarios. To cope with the high number of riskscenarios, we advise to cluster multiple similar simulations into a single scenario. This will alsoprevent that the three scenarios with the highest risk values will all be very similar. Becauseeach parameter configuration will be run multiple times and will only change the random seed,each experiment is likely to produce many near duplicates. Near duplicates can also occur whenthe different parameter configurations are too similar to produce significant different simulations4. After clustering we have a significant smaller set of scenarios, for which we need to calculatethe risk values in order to obtain risk scenarios. After this the most relevant risk scenariosshould be chosen and translated from raw data into a written risk scenario. Below we discussthe process of clustering and the process of selecting and writing risk scenarios.

10.3.4.4.1 Clustering simulations into risk scenarios Clustering of similar simulationsaround a certain norm simulation and calculate the risk values should be done automatically.Clustering itself is done by selecting simulations into a single cluster that at every time stepscore on each criteria, and if desired each other metric, within a certain bandwidth of the normsimulation. We advise to cluster by selecting the simulation with the highest consequence valueas a norm simulation and then cluster all similar simulations. Then select the simulation withthe highest consequence value outside that cluster, as norm simulation and cluster all similarsimulations. This should continue until all simulations are in a cluster. The consequence valueof each scenario is now represented by the range between the lowest consequence value in thecluster and the highest consequence value in the cluster. After this all the likelihood valueswithin a cluster should be added to each other to determine the relative likelihood value of therisk scenario. Relative because the exact likelihood value can only be determined when themodel is assumed to be fully accurate, all possible parameter values are used in a full factorialexperiment that runs every possible parameter configuration for every possible random seed.This is also an aspect that should be taken into account when developing the experimentalsetup. One should also take into account the size of the bandwidth he uses to cluster. Becausethe larger the bandwidth, the higher the likelihood values of the developed scenarios but at thecost of creating less accurate scenarios with larger ranges of consequence values. Finally therisk scenarios are complete when the risk value of each scenario is also determined. In line withthe most common description for risk in the Security Risk Management Body of Knowledge

4This despite the chaos that will likely be present in the model. But chaos in a model only means that slightchanges in a parameter value, or even the random seed, can provide significant different results but will not alwaysdo so.

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[103, p.146], we use risk = likelihood * consequence. We advise to use the average consequencevalue of a cluster, as the consequence value of that scenario in order to calculate the risk value.When communicating about the consequences of a developed risk scenario we still advise theranges because, especially with a large bandwidth of the clusters, the ranges are a more accuraterepresentation of the consequence of the risk scenario.

10.3.4.4.2 Selecting the risk scenarios Since even after clustering there will be thousandsof risk scenarios left, depending on the chosen bandwidth size. It is very impractical to analyzeand describe all these risk scenarios. Therefore we advise to select the three simulations withthe:

• Highest likelihood: Because of the partly monitoring aim of this Strategic GeopoliticalIntelligence process, it is also important to know what are the most likely scenarios.

• Highest risk value: Because of the partly policy development aim of this Strategic Geopo-litical Intelligence process, it is important to focus on scenarios with the highest risks.This prioritization is in line with the Security Risk Management Body of Knowledge [103].

• Highest consequence value: Because we do not want to miss devastating scenarios thataccording to inaccurate likelihoods had a low risk (Black Swan scenarios). These scenariosare especially relevant for the policy development that requires mid/long term scenariosand the further away in the future the geopolitical system needs to be simulated, the lessreliable the likelihood values will become.

• Special scenarios of interest: Some scenarios are of special interest to the problem ownereven though they do not score the highest on either likelihood, risk or consequence. Forexample it is for political and strategic reasons important to the United States to know allthe scenarios wherein a neighboring nation of one of his allies manages to obtain nuclearweapon capability.

Basically we have replaced the types of scenarios that would require a paradigm shift in thesystem under study , with scenarios with the highest risks. The paradigm shifted scenariosare removed since they are only relevant when they are likely, have high consequences or posehigh risks. Furthermore the development of such scenarios is not required any more to forceanalysts to think out of the box, because that is what the Agent-Based model already shouldhave done for them. After selection it is not unthinkable though, that some risk scenarios arein multiple categories. So we do not per se arrive at nine scenarios, furthermore the analyst canalso choose to include more or less than three scenarios per category since we chose the numberthree arbitrarily.

Note though that only the selected risk scenarios will be described, but that every simulationsshould still be stored for future processes. Also note that the analyst can choose more risk sce-narios here than he will finally report on. In that case he has to select the most valid riskscenarios using analysis of competing hypotheses during the reporting processes (see paragraph10.3.5.5). Finally also note that the analyst can choose to describe the scenarios in brief nota-tions and elaborate them in the reporting process, although this can affect the internal validityand the reliability of the analysis..

10.3.4.4.3 Qualifying the risk scenarios The selected risk scenarios are for now only asequence of stored values in a matrix. In order to be able to see really what is happening in a

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scenario it is important to visualize the results properly. Since geopolitical systems are relativelyconcrete, we advise to use animation to visualize the scenarios. Frames from these animationswould resemble the images in figure 10.4 that shows the violence and control in Isral, Gaza andthe West Bank at two different times [11, p. 67]. Based on the animations it is possible todescribe the selected risk scenarios in the same way as they would be done with the traditionalStrategic Geopolitical Intelligence approach. Make sure to use more detail for the near termevents in the risk scenarios, so they can be used for both the near term reports as for themid/long term reports.

Figure 10.4: Images showing an example of animating a risk scenario

10.3.5 (α5) Analyzing risk scenarios

The product of this process will be a report on near term risks, an intelligence monitoring plan,a report on mid/long term risks and an adaptive policy advice. But first the developed riskscenarios and the model has to be validated. Figure 10.5 shows the validation process and theprocesses involved to deliver the end products. The individual processes are explained below.

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α51

Validating risk scenarios

Riskscenarios

α52

Identifying critical indicators and de-warning indicators

α53

Developing intelligence

monitoring plan

Criticalindicators

Validatedrisk scenarios

α55

Reporting on mid/long term risks

Validatedrisk scenarios

α56

Developing adaptive policy

advice

Report on mid/long term

risks

Report on mid/long term

risks

Policy advice

Intelligencemonitoring

plan

Intelligenceanalyst Intelligence

analyst

Intelligenceanalyst

Intelligenceanalyst

Agent-Based model

Policy analyst


Doctrine

Doctrine

Doctrine

Doctrine

Doctrine

Agent-Basedmodel

Agent-Basedmodel

Agent-Basedmodel

Agent-Basedmodel

Validated risk scenarios

Intelligence

Intelligence

α54

Reporting on near term risksIntelligence Report on

near term risks

Intelligenceanalyst

DoctrineCritical

indicators

Expectedintelligence

update

Figure 10.5: α5 diagram of the proposed Strategic Geopolitical Intelligence process: Assess riskscenarios

10.3.5.1 (α51) Validating risk scenarios

In order to check whether the generated risk scenarios could really occur and whether the riskvalues are realistic, it is important to validate the source of the risk scenarios (the Agent-Based model) as well as the risk scenarios themselves. This is in line with the STANAG 2022NATO doctrine for valuing the reliability of intelligence [H.7.2.5] [75], as discussed in paragraphE.5.3. In order to validate the risk scenarios themselves it is important for the analyst to firstunderstand the dynamics in the model that makes certain behaviors emerge. After analyzingthe model behavior, we advise, based on suggestions of Van Dam et. al. [38, p.127], to useFace validation through expert consultation and to use Literature validation. Face validation

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asks domain experts whether the described behavior and the related values seem reasonable. Italso checks whether the experts come up with the same reasoning behind why certain behaviorsemerged, as that the model did. In case they do not, it is still worth a check whether theexperts considers the reasoning of the model to be reasonable in hindsight [ibid. p.128-129].This because one of the special benefits of using an Agent-Based modeling study, is that itidentifies emergent behavior no human has ever thought of. Literature validation focuses at theemergent behavior as well and checks whether in other cases, or in other (modeling) studies thesame behavior has already been identified [ibid. p.129]. Whenever a scenario seems unrealistic,the analyst should both question the input parameters as well as the model structure itself.Furthermore it is also possible to validate the model itself using Historic replay which let themodel start in the past and uses data between the past and present to validate the model [ibid.p.127]. However whether this method can be used depends on the amount of available historicdata. The available historic data is generally very limited, especially considering a significantamount of it is already being used for calibration (see paragraph 10.3.4.3). It is also possibleto let another analyst also create a model on the same case, using Agent-Based modeling oranother method, and check whether the results are similar [ibid. p.129-130]. However this is avery labour intensive operation and is therefore not really advised in the Strategic GeopoliticalIntelligence setting, where time is definitely constraint. If the model appears to be invalid thenthe analyst should adapt the model, if the model appears to be valid but a scenario invalid thenthe analyst should adjust the parameter input values and if both appear to be valid then theanalyst can continue with the next processes.

10.3.5.2 (α52) Identifying critical indicators and de-warning indicators

This process is very similar to (x13) Determining critical indicators for each scenario, as discussedin paragraph E.4.3. To resummarize, an indicator is a single event in the sequence of possiblefuture events that make a Scenario [98, p.1]. A Critical indicator is one of those events thatindicate a significant change in the level of threat5 and allows the analyst to make, change ormodify an assessment [ibid. p.12]. It also an event on which there is little detail available, sobasically all critical indicators are critical unknowns. An important feature of the Agent-Basedmodel is that it is able to apply statistical methods like Feature selection and Random forest, assuggested by the Policy Analysis Simulation Lab of the TU Delft, to identify which parametersare the most influential on making a certain simulation fit into a certain scenario [42]. Using asensitivity analysis on these most influential parameters the analyst can be describe the criticalindicator in more detail, e.g. provide upper and lower limits for indicators like unemploymentor deaths due civil disorder. Furthermore the sensitivity analysis can also be used to validatewhether the occurrence of a set of critical indicator always leads to that risk scenario or that thereare many more possible scenarios with the same set of critical indicators. The other way aroundit is also possible to setup a list of de-warning indicators by identifying with the sensitivityanalysis, events that deny the occurrence of a certain risk scenario of happening (i.e. providea negative warning [H.8.2.4]). For the de-warning indicators we advise to consider, almost, thesame list of criteria as what NATO prescribed for the critical indicators [ibid. p.11]:

• Critical indicator/de-warning indicator is forward looking: they are future events;

• Critical indicator/de-warning indicator is collectible, early, reliable, diagnostic, and unam-biguous;

5Threats are considered by the NATO to be a product of the intentions, capabilities and the actions of an“opponent” [ibid. p.7]

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• The set of critical indicators/de-warning indicators per Scenario covers Military, Political,Social, and Economic considerations (sometimes Science and Technology as well);

• The set of critical indicators provides clues that a Scenario is occurring; and

• The set of de-warning indicators provides clues that a Scenario is not occurring.

10.3.5.3 (α53) Developing intelligence monitoring plan

As discussed in paragraph E.4.4 on the intelligence monitoring plan, it is important to selectand prioritize the indicators based on usefulness. Usefulness of indicators is described by NATOdoctrine as the product of the probability that a crisis will occur if event materializes, theprobability the event will be detected and the uniqueness of that event for a specific Scenario[77, p.100]. All three values can be determined using the Agent-Based model. These values willbe relative, as discussed above in paragraph 10.3.4.4.1, but this will serve te purpose of selectingand prioritizing.

10.3.5.4 (α54) Reporting on near term risks

Since the format of the risk scenarios and the critical indicators are the same, it is possible touse the same reporting structure as the traditional method. So for the near term risks, the Tier3, 2 and 1 reports as presented in paragraph E.6. The only change is to include the de-warningindicators into the reports. This reporting is a continues process based on the continues incomingintelligence updates, until the intelligence update implicates that one or more of the Scenarioscannot be true anymore. More about assessing the intelligence updates will be discussed laterin paragraph 10.3.6.

10.3.5.5 (α55) Reporting on mid/long term risks

Similar to near term risks, the reports on mid/long term risks will also remain the same. Theywill be based on the selected and validated risk scenarios, as well as how they were developed,selected and validated. If the analyst desires to cut the number of scenarios on which he reports,then he can use the analysis of competing hypotheses method to identify the most valid riskscenarios in each category (i.e. highest consequence, highest likelihood and highest risk). Theanalysis of competing hypotheses can be supported by the sensitivity analysis of the Agent-Basedmodel, so testing the influence of questionable intelligence on the validity of a risk scenario. Seeparagraph E.13 on how analyses of competing hypotheses works.

Note that for both reporting methods it remains important to keep a log of all assumptions,decisions and trade-offs made during the analysis, just like the traditional reports.

10.3.5.6 (α56) Developing adaptive policy advice

This process is represented with dashed lines because developing new policies is actually outsidethe mandate of NATO intelligence services. However due to the major potential of develop-ing adaptive policies using models, we advise a certain integration/cooperation between theintelligence service and the relevant governmental services. This due to the presence of Deep

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uncertainty6 we assume to be generally present in Strategic Geopolitical Intelligence7. For intelli-gence purposes the Agent-Based model only develops risk scenarios under the current conditions.A policy maker can change the conditions by implementing another policy. The Agent-Basedmodel can also develop risk scenarios for these new conditions, before implementing the actualpolicy, in order to ex-ante evaluate that policy. The Agent-Based model can also be used todeduct from the developed risk scenarios, what good policies are for these scenarios. By usingthe statistical methods that are used to identify critical indicators it also possible to identify theparameters that have the most influence on whether a risk scenario is acceptable or not. Finallyit is also possible to use the Agent-Based model to develop a policy that is the most effectivefor all the possible thousands of risk scenarios that were developed. Such a policy would theninclude logic reasoning, so if critical unknown A appears to be true then focus on changingcondition/parameter B. Where A could be the number of hostile forces in a region and B thenumber of allied troops assigned to that region. Using the sensitivity analysis it would thenalso be possible to explore threshold values for critical unknowns, e.g. when between 100 to 200hostile troops are in a region then the allied forces should be reinforced to about 100 troops. Byincluding such logic reasoning the policy becomes adaptive, which means that not with everysituation change a new policy has to be developed and approved. In the new process we suggestthat the Agent-Based model, which is constantly kept up to date through iteration, should beused directly to develop such adaptive policies. It would make sense to involve the intelligenceanalysts, who developed the model and keeps it up to date, when using the model to developpolicies. This because, despite the elaborate logging, the tacit knowledge the intelligence analysthas on the case and on the model would help the development of the policies using that samemodel. Although it is possible to let the policy maker develop its own Agent-Based model orcopy the model, it would make sense from an efficiency and effectiveness point of view to cutthe interface between the intelligence analyst and the policy maker. We realize though that thisintegration can pose some serious legal and political problems. Hence the dashing of this processin figure 10.5.

Note that when developing adaptive policies, the assessment of the likelihoods of risk scenar-ios becomes less important. This because the adaptive policy will be developed to handle everyplausible risk scenario.

10.3.6 (α6) Assessing intelligence update

As discussed above; Strategic Geopolitical Intelligence operates in Deep Uncertainty, whichmeans that especially in the long term we are not very sure which risk scenario is most likely.Therefore it is important to monitor all incoming intelligence and keep on checking whetherit is still in line with what has been reported before. We consider four different intelligenceupdates that each results in a different iteration step. An intelligence update, on an event,can be either expected or unexpected. When the event occurs in a significant number of thethousands of generated risk scenarios, then it is an expected intelligence update. An intelligenceupdate can also be critical or non-critical. When the event has the power to exclude oneor more of the selected scenarios then it is a critical intelligence update, basically this is the

6Deep Uncertainty “exists when analysts do not know, or the parties to a decision cannot agree on, (1) theappropriate models to describe the interactions among a system his variables, (2) the probability distributions torepresent uncertainty about key variables and parameters in the models, and/or (3) how to value the desirability ofalternative outcomes” [70, p.3-4]. The models meant in this definition can be both qualitative and quantitative.

7This assumption is based on the little intelligence that can be gathered abroad [H.1]. This assumption islisted in appendix A.2

81

overriding importance criterion. The assessment on whether the intelligence update is criticaland/or unexpected, should be done by analyzing the generated risk simulations. This includesa sensitivity analysis to take into account the possible questionable value of the intelligenceupdate, also consider the extra intelligence value criterion as discussed in paragraph 10.3.1. Incase of unexpected intelligence, the analyst has to update/rebuilt the Agent-Based model tocheck whether the update is critical or not. If the unexpected intelligence update is not reliable,then make sure to set the parameter values in such a way that the new event is not always runin each simulation. Below is indicated per type of intelligence update which iteration should bemade and why.

• Expected intelligence update should be noted in the monitoring reports of the intelligenceservice. However nothing else has to be updated because when an intelligence update isexpected and not critical, it means that the event is in line with both the model as theselected risk scenarios.

E.g. for NATO states it was presumably expected; that a certain number of French groundforces arrived in Mali at a certain time.

• Expected critical intelligence update excludes at least one of the selected risk scenariosand can be replaced by another one. Because the intelligence update was critical, certainparameter configurations have had to be incorrect. We advise to develop new risk scenar-ios with the added knowledge on which parameter configurations can be true and whichparameter configurations can not be true. This will lead to more complete developmentof risk scenarios within the valid parameter configuration, since the previous developmentof risk scenarios also had to develop risk scenarios that appear to be invalid in hindsight.After developing new risk scenarios, the analyst can select the top risk scenarios in eachcategory and will discover most likely that only the rejected scenarios has been replacedby new ones.

E.g. for NATO states it was presumably expected but critical; that the French militaryrecaptured certain cities in Mali within a certain time frame.

• Unexpected intelligence update is an update that indicates that the model failed to takeinto account certain events. However when it is not critical it means that after updatingthe model with that unexpected event that not one or more of the selected risk scenarioshad to be rejected. Therefore only the Agent-Based model has to be updated and newrisk scenarios developed. It is likely that the previously selected risk scenarios are not inthe top of the categories anymore but that does not mean that the update is critical, sincethe old risk scenarios have still been generated by the model.

E.g. for NATO states it could have been unexpected; that China would offer troops for theUN peacekeeping mission in Mali.

• Unexpected critical intelligence update is an update that indicates that the model failedto take into account certain events. However because the occurrence of such an event alsomade certain selected risk scenarios impossible to be true, it is a sign that the originalperspective might have been incorrect. Therefore the intelligence problem has to be re-defined and the analyst should decide between adjusting the current model or rebuildingit. This decision depends on how much the intelligence update was out of line with theperspective of the model.

E.g. for NATO states it could have been unexpected and critical; that the Tuareg mercenarieswould return so heavily armed from Libya and join the battle in Mali.

82

Chapter 11

Strategic Geopolitical Intelligencetowards a formal model on Mali

This appendix shows how the first three processes would look like when applied to the Mali case.The aim is to experience and to show how the suggested process design, presented in chapter10, would work applied to a real case. Since this case study is only instrumental, as discussedin the method paragraph 2.3.4, the aim is not to provide an intelligence report or policy advice.Therefore one should not read this chapter to see how well the Mali case is modelled, but oneshould read this to see how well a case like Mali can be assessed using Agent-Based modeling.

11.1 (α1) Gathering intelligence

For this case study we assume that this whole Strategic Geopolitical Intelligence process startedafter the adoption of the United Nations Security Council Resolution 2085 [36] on December20th 2012. Which provided sufficient general intelligence to Define the intelligence problem(process α2). This section presents the sources and the intelligence inventory that providedsufficient general intelligence to Develop the Agent-Based model (process α3).

11.1.1 Sources

We use publicly available news articles from the month January 2013 to identify relevant infor-mation, for which we assume was already available to NATO state intelligence services duringthe adoption of the resolution. A special focus was on the weekend of 11 to 13 January 2013,since then the French intervention started and the most information was published. We gatheredall the articles of; Het Parool, De Volkskrant, Trouw and NRC Handelsblad, that contained theterm: Mali, from January 11 to January 13 2013. These newspapers were used because these arethe only ones available via the TU Delft license of Nexis. Furthermore we used a single news-paper source for the rest of the month January, to discover additional intelligence that couldonly be gathered after the press started studying the Mali case in detail. We decided to use asingle newspaper to prevent too much ambiguous information. We chose NRC Next, becausewe are already subscribed to that newspaper. We still acknowledge the difficulty of providingintelligence by gathering and assessing information, but one sided information suffices for thepurpose of this instrumental case study and saves a lot of time.

83

11.1.2 Intelligence inventory

Table 11.1 shows the intelligence inventory that has been created based on the gathered infor-mation out of the news sources.

11.2 (α2) Defining intelligence problem

11.2.1 Problem formulation

The UN Security Council resolution 2071 prescribes that the Mali government military forcesshould recapture northern Mali with foreign support. After recapturing Mali, the Mali govern-ment should secure the region with their own forces, foreign support is required to train theMali security forces [36]. This study aims to develop scenarios to explore the foreign capabilitiesrequired to achieve a sustainable stability of Mali, according to the UN Security Council reso-lution. The main emergent pattern of interest is the volatile stability of local regions as a resultof conflict, as well as the interrelating effects with the volatility of state stability. Therefore thedynamics of conflict and recovery in local regions are being modelled, as well as the dynamicsof conflict and recovery within and between governmental organizations like the Mali state andthe rebel organizations. By modeling both the state as the local arena’s into one model, we canexplore the necessary foreign capabilities on a local level and state level to achieve a sustainablestability of Mali as a whole.

11.2.2 Criteria

Since the main emergent pattern of interest is the volatile stability of local regions and the statelevel, as a result of conflict, we adopt the criteria from the Failed State Index of the Fund forPeace [50]. This Failed State Index is often used by the world press to compare the stability ofdifferent states [12]1 [1]2. We only adopted the high level indicators to simplify the instrumentalcase study, since an even more complex study would not directly add to the goal of executingthis case study for this thesis3. The used criteria are presented below:

Criterion 1: Socio-economic index

Criterion 2: Mortality

Criterion 3: Migration speed

Criterion 4: Migration volume

Criterion 5: Tension between citizen groups

Criterion 6: State legitimacy / allegiance of citizens to state

Criterion 7: Rebel activity

Criterion 8: Terrorist bombings

1Article in The New York Times referring to the Failed State Index of the Fund for Peace2Article in The Economist referring to the Failed State Index of the Fund for Peace3Example of a high level indicators is socio-economic index instead of the combination of Economic Deficit,

Unemployment, Youth Employment, Purchasing Power, Available Infrastructure, Civil liberties, etc.

84

Table 11.1: Inventory of gathered intelligence

Actors/objects Newspaper Date

Muslim-extremist rebels Trouw 11-01-13

Volkskrant 11-01-13

City Trouw 11-01-13

Volkskrant 11-01-13

Ansar Dine (most important rebel group) Trouw 11-01-13

Volkskrant 11-01-13

Malinese army Trouw 11-01-13

Volkskrant 11-01-13

NRC 11-01-13

Home nation Trouw 11-01-13

Jihadists Trouw 11-01-13

Aqim (Al-qaida in mahgreb, rebel group) Trouw 11-01-13

Trouw 12-01-13

Local citizens Volkskrant 11-01-13

UN security council NRC 11-01-13

Malinese government NRC 11-01-13

West-African UN army NRC 11-01-13

ECOWAS NRC 11-01-13

NATO NRC 11-01-13

African Union NRC 11-01-13

Neighbor nations Trouw 12-01-13

European Union Trouw 11-01-13

Foreign citizens (of NATO nations) Volkskrant 12-01-13

Mujao/MUJWA (rebel group) Volkskrant 12-01-13

MLNA (Touareg movement fighting for independence) Next 14-01-13

Boko Haram (foreign rebel groups) Next 14-01-13

Migration routes Next 24-01-13

Resources Next 24-01-13

Smuggling routes of rebels (to earn money) Next 24-01-13

Weapon routes of rebels (to fight) Next 24-01-13

Behaviors Newspaper Date

Uncontrolled areas (e.g. due to revolution) attracks jihadists Trouw 11-01-13

Implementing Sharia upon citizens when area is in control Trouw 11-01-13

Trouw 12-01-13

Recruit and train warriors Trouw 11-01-13

Panic among citizens, near " fallen" cities Volkskrant 11-01-13

Kidnapping foreigners to earn money NRC 11-01-13

Salafisten aim to subject the whole region Trouw 12-01-13

Act when announced that in future an international force comes Trouw 12-01-13

Train local army Parool 12-01-13

Aerial attacks NRC 12-01-13

Cooperation of rebel groups Trouw 11-01-13

Volkskrant 11-01-13

NRC 11-01-13

Parool 12-01-13

Rebels develop defend works when controlling area (tunnels, etc.) Next 14-01-13

Violent revenge Next 24-01-13

85

Flows Newspaper Date

Migration flow of jihadists Trouw 11-01-13

Flow of weapons, ammo, etc. through uncontrolled areas Trouw 11-01-13

Flow of military (French) NRC 11-01-13

Interactions Newspaper Date

Terrorist attacks to home nation Trouw 11-01-13

Jihadists in firefights with security troops Trouw 11-01-13

Force Sharia upon citizens in Toeareg controlled area Trouw 11-01-13

Distruction of artefacts Trouw 11-01-13


Show of support by other nations Parool 12-01-13

Show of support by internal groups Parool 12-01-13

Military attack to militants Volkskrant 12-01-13

Support of local army Volkskrant 12-01-13

Criminals cooperate with rebels and provide them money Next 14-01-13

Properties / attributes Newspaper Date

Garnizon city NRC 11-01-13

Organization of army (discipline, weaponry, etc.) NRC 11-01-13

Accessibility of area Next 14-01-13

Ability of accessing/staying in rough areas Next 14-01-13

Porosity of borders Next 24-01-13

Integrity of rebels/military Next 24-01-13

Relevant concepts Newspaper Date

Control of government Trouw 11-01-13

Border Trouw 11-01-13

Jihad areas Trouw 11-01-13

Threat level to home nation Trouw 11-01-13

Revolution Trouw 11-01-13


State stability Trouw 11-01-13

Stability = weapon control Trouw 12-01-13

Religion (Islam, Christianity) Trouw 11-01-13

Volkskrant 11-01-13

NRC 11-01-13

Parool 12-01-13

Economic interest of west in region Volkskrant 12-01-13

Availability of airspace NRC 12-01-13

Spill overs to foreign nations (security) Trouw 11-01-13

Volkskrant 11-01-13

NRC 11-01-13

Parool 12-01-13

Next 14-01-13

States Newspaper Date

City is taken by actor Trouw 11-01-13

Revolution Trouw 11-01-13

Soldiers health&moral NRC 11-01-13

Stability of gov. controlled Mali NRC 11-01-13

86

Criterion 9: Dependency of external intervention

11.2.3 Actors

Since we are not going in to deep with this analysis, we chose to just list the relevant actorswithout fully executing the actor analysis as suggested in paragraph 10.3.2.2.

• The French government and its military who, by request of the Mali government, is leadingthe international community to fulfill the UN Security Resolution [problem owner].

• Mali state

• Malian military and security forces

• Rebel organizations

• Militia forces of rebel organizations

• Citizens of Mali

11.3 (α3) Develop Agent-Based model

This section will show the steps of developing an Agent-Based model, until the development ofthe formal model. So in the next paragraph will the intelligence from the intelligence inventorystructured, then concept formalisation will take place and finally the formal model will bepresented.

11.3.1 Intelligence structuring

Tables 11.2 and 11.3 show respectively the result after structuring the agents and objects. Itshows which attributes each agent or object contains, with the relating variable sign. It alsoshows which actions or dynamics can be executed per agent and object. Finally table 11.4 showsfor every agent which interactions it with other agents or objects can perform. It also showswhat attributes of the other agent or object can be affected by that interaction.

11.3.2 Concept formalisation

Figure 11.1 shows the UML class diagram with all the formalized concepts and relations of themodel. The attributes of every thing in the model are presented in alphabetic order in theboxes. Next to the attribute names are the data types presented that are used in the model torepresent the attribute values. We have listed the data type meanings and our usage of thembelow:

• Boolean (bool): Can only have the value True or False

• Float: Can be any fractional value, but we limit all floats between 0 and 1 with 3 decimals.We use floats to represent relative values like army size since, so we do not require preciseintelligence on attributes like that. 0 represents the minimum value, 0.5 the norm valueand 1 the maximum value. Floats are also used for parameter values which are used to

87

calibrate the power and direction of causal effects. For parameter values we extend therange from -1 to 1, to include negative causal effects.

• Integer (Int): Can be any whole number. We use integers to represent the identificationof e.g. a state agent that can be the Mali state or for example a more ad-hoc rebelorganization like Ansar Dine. Integers also represent for a certain military/militant force,with a certain identification value, to which actor they answer to.

Table 11.2: Structured entities (agents)

Ag

en

tA

ction

s

Citize

nC

ultu

re--

CD

ecid

e to

mig

rate

Mig

ratio

n d

esire

--C

mE

va

lua

te re

latio

n to

state

s

Re

latio

n to

cultu

re--

CC

Eva

lua

te re

latio

ns to

oth

er cu

lture

s

Re

latio

n to

state

[ID]

--C

SE

va

lua

te se

curity

pe

rcep

tion

Se

curity

pe

rcep

tion

--C

sec

Eva

lua

te so

cio-e

con

om

ic we

alth

So

cio-e

con

om

ic we

alth

--C

seM

igra

te

Mil. fo

rceA

lleg

ian

ce to

state

--M

sD

ecid

e to

de

sert

Arm

y size

--M

aE

va

lua

te re

latio

ns

De

sired

arm

y size

--M

dM

an

ag

e se

curity

force

s

En

ga

ge

d in

wa

r--

Me

Re

qu

est p

rovisio

ns

Mil. fo

rce [ID

]--

MW

ag

e w

ar w

ith o

the

r mil. fo

rce

Mo

ral

--M

m

Pro

fessio

na

lism--

Mp

Pro

vid

ing

sup

po

rt--

Msu

p

Re

latio

n to

cultu

re--

MC

Re

latio

n to

mil. fo

rce [ID

]--

MM

Re

latio

n to

state

[ID]

--M

S

Re

sou

rces

--M

r

Se

curity

task

--M

task

Sta

teA

rmy size

--Sa

Dip

lom

acy

Mo

ral

--Sm

Go

ve

rn co

ntro

lled

reg

ion

s

Re

latio

n to

cultu

re--

SC

Go

ve

rn M

il. Fo

rces

Re

latio

n to

state

[ID]

--SS

Go

ve

rn te

rrorists

Re

sou

rces

--Sr

So

cio-e

con

om

ic we

alth

--Sse

Sta

te ID

--S

Te

rrorist ce

llR

ela

tion

to sta

te [ID

]--

TS

Atta

ck re

gio

ns

Re

sou

rces

--T

rT

erro

rist dip

lom

acy

Te

rrorist ce

ll [ID]

--T

Attrib

ute

s -- Va

riab

le

88

Table 11.3: Structured entities (objects)

Ob

ject

Dy

na

mics

Re

gio

nA

ccessib

ility to

cultu

re--

Ra

ccesC

Eva

lua

ting

acce

ssibility

Acce

ssibility

to sta

te [ID

]--

Ra

ccesS

Eva

lua

ting

attra

ction

leve

l for cu

lture

Attra

ction

leve

l for cu

lture

in m

igra

tion

ne

two

rk--

Ra

ttCE

va

lua

ting

con

trol b

y sta

te [ID

]

Co

ntro

l by sta

te [ID

]--

Rc

Eva

lua

ting

ge

ne

ral so

cio-e

con

om

ic we

alth

De

sired

secu

rity--

Rd

Eva

lua

ting

secu

rity

Re

gio

n [ID

]--

R

Se

curity

--R

sec

So

cio-e

con

om

ic we

alth

--R

se

With

in M

ali b

ord

er

--M

ali

Mig

ratio

n n

etw

ork

Ve

ctor fo

r citizen

[typ

e]

--V

CE

va

lua

ting

ve

ctors

En

viro

nm

en

tn

/aP

rovid

es lo

cal a

id

Sp

on

sorin

g te

rrorists

Su

pp

ort sta

tes

Un

go

ve

rne

d e

ve

nts a

ffectin

g citize

ns

Un

go

ve

rne

d e

ve

nts a

ffectin

g th

e sta

te

Attrib

ute

s -- Va

riab

le

89

Table 11.4: Structured interactions

From To What Affects attribute

Citizen Citizen Evaluating culture Relation to culture

Citizen State Evaluating state Relation to state

State Mil.force Mil. force governance Army size

Moral

Resources

State Region Region governance Socio-economic wealth

State State Diplomacy Interstate relation

State Terrorist cell Terrorist diplomacy Relation to state

State Terrorist cell Terrorist governance Resources

Terrorist cell Mil.force Terrorist attack Army size

Resources

Terrorist cell Region Terrorist attack Security

Socio-economic wealth

Terrorist cell Terrorist cell Terrorist diplomacy Relation to state

Mil. force Citizen Evaluating culture Relation to culture

Mil. force Mil. force Securing region Create new mil. force

Security task

Mil. force Mil. force Waging war Army size

Interforce relation

Moral

Providing support

Resources

Mil. force Region Securing region Accessibility

Control

Desired security

Security

Mil. force Region Waging war Security


Mil. force State Deserting Moral

Mil. force State Evaluating state Relation to state

Mil. force State Securing region Army size

Environment Citizen Ungoverned events Relation to culture

Relation to state

Environment Region Local aid Accessibility

Attractiveness

Security


Environment State Diplomacy Relation to state

Environment State Ungoverned events Army size

Moral

Resources


Environment Terrorists i Foreign support Resources

90

Thin

g

Age

nt

Ob

ject

-Arm

y size : float

-Mo

ral : float

-Reso

urces : flo

at-So

cio-eco

no

mic w

ealth : flo

at-State ID

: int

-Terrorist sp

on

sor : flo

at

State

-Ethn

icity : int

-State level : bo

ol

Cu

lture

-Relatio

nsh

ip level : flo

at

State - C

ultu

re re

lation

**

-Relatio

nsh

ip level : flo

at

State - Te

rrorist ce

ll relatio

n

**

-Relatio

nsh

ip level : flo

at

Citize

n - C

ultu

re re

lation

**

-Relatio

nsh

ip level : flo

at

State - C

itizen

relatio

n

**

-Allegian

ce to state : in

t-A

rmy size : flo

at-D

esired arm

y size : float

-Engaged

in w

ar : bo

ol

-Mil. fo

rce ID : in

t-M

oral : flo

at-R

esou

rces : float

-Security task : b

oo

l

Mil. fo

rce-R

elation

ship

level : float

State - M

il. force

relatio

n

**

-Allegian

ce to state : in

t-R

esou

rces : float

-Terrorist cell ID

: int

Terro

rist cell

-Migratio

n d

esire : bo

ol

-Security p

erceptio

n : flo

at-So

cio-eco

no

mic w

ealth : flo

at

Citize

n

1

*

**-R

elation

ship

level : float

Mil. fo

rce - C

ultu

re re

lation

-Availab

le for terro

rist attack : bo

ol

-Desired

security : flo

at-P

reviou

s govern

or : in

t-R

egion

ID : in

t-Secu

rity : float

-Socio

-econ

om

ic wealth

: float

-With

in M

ali bo

rder : b

oo

l

Re

gion

*

*

-Accessib

ility : float

-Co

ntro

l : float

State - R

egio

n re

lation

Migratio

n n

etw

ork

1

*

*

*

*

*

-Accessib

ility : float

-Attractiven

ess : float

Re

gion

- Cu

lture

relatio

n

-Relatio

nsh

ip level : flo

at-A

llied : b

oo

l

Inte

rstate re

lation

**

-Ho

stile : bo

ol

-Pro

vidin

g sup

po

rt : bo

ol

Inte

rforce

relatio

n

**

Figure 11.1: UML class diagram of the Mali model

11.3.3 Model formalisation

Figure 11.2 inside the fold out poster shows the UML activity diagram of the narrative. Thecomputational actions of every agent per activity, are described in appendix G.

Note that the dashed action boxes mean that the environment affects certain values of that agentor object at that time during that action. These are environment interactions that are identifiedin the bottom of table 11.4.

92

State

Update socio-economic

wealth of own state

Determine change

of own mil. force moral

Go

vern

co

ntr

olle

d

regi

on

sG

ove

rn o

wn

mil.

fo

rces

Determine army size

allocation

Determine resource

allocation

Go

vern

ter

rori

sts

Determine terrorist

resources allocation

Determine change in

socio-economic wealth

of regions

CitizenTerrorist cell Mil. force Region Migration network

Change socio-economic

wealth in own region

Change moral

Change army sizeArmy size

Army size

ResourcesChange resources

Resources

Resources

Change resources

Resources

Decide which

region to attack

Terr

ori

st a

ttac

k

Determine effect of attack

to mil. force army size in regionChange army size

Change resourcesDetermine effect of attack to

mil. force resources in region

Determine effect of attack to

socio-economic wealth of region


wealth

Mig

rati

on

d

ecis

ion

Update migration desire

Inte

rnal

cit

izen

dyn

amic

s

Update relations

to other cultures

Update relations to states

Update security perception


wealth

Wag

e w

ar w

ith

oth

er m

il . f

orc

e

Is mil. forceproviding support?

Is mil. force

engaged in war?

[no]

Update hostile armies

in range

[no]

Are hostile

armies too

strong?

Update army sizes

based on war progress

[no]

[yes]

Update mil. force resources


Determine effect of war to

socio-economic wealth in region


wealth


security in region

Determine effect of attack to

security of regionChange security

Change security

Man

age

secu

rity

fo

rces

Update

desired security

[yes]

Is mil. forcesecurity tasked?

[no]

[yes]

Update war

progress

Update providing

support status

Is mil. forcesecurity tasked?

Is mil. forceengaged in war

ORprovides support?

Determine excesive army

size for allocation to state

[yes]

Change army sizeArmy size

Army size

Update desired

army size

Create new mil. force

with security task

Is army size ≤ desired security?

[no]

[no]

Update security task

[yes][no]

Req

ues

t p

rovi

sio

ns

[yes]

[yes]

Update desired

resources

Des

erti

on

Eval

uat

e re

lati

on

s

Update relation to

cultures

Update relations to states

Update allegiance

Update mil. force moral



wealth

Eval

uat

e re

gio

n a

ttri

bu

te v

alu

es

Update security

Update attractiveness

Update accessibility

Update control

Mig

rati

on

n

etw

ork

Update edges in

migration network

[no]

[yes]

Mig

rati

ng

Direct migration

Migration desire?

Update moral of own state

Dip

lom

acy

Update interstate relationships

Determine succes of attack

Is attack successful?

[yes][no]


interstate relationsChange interstate relations

Chapter 12

Evaluation overview of the newStrategic Geopolitical Intelligenceprocess

This evaluation is done in two steps1. First we evaluate our experiences of executing the sug-gested Strategic Geopolitical Intelligence process on the Mali case, in order to identify thepractical implications of Agent-Based modeling in the process. Secondly we evaluate the po-tential of the suggested Strategic Geopolitical Intelligence process, which includes the practicalimplications identified with the case study.

12.1 Practical implications of executing the new Strategic Geopo-litical Intelligence process on the Mali case

Based on the execution of the new Strategic Geopolitical Intelligence process we have gatheredthe following practical implications:

• Developing an Agent-Based model can be a very time consuming.

• Every bit of reasoning has to be made explicit in the model and a log, which forces theanalyst to justify all the reasonings. However due to the amount of assumptions, decisionsand trade-offs, logging can be very time consuming.

• Practically all parameter values will be relative values, this requires less explicit intelligencebut also provides less explicit results.

• Not knowing the power of causal relations requires for every causal relation a parameterthat can explore every plausible causal power.

• Due to the high number of effects in a geopolitical system that are considered random, itis important to use a relative high number of simulations per experiment.

1These two steps are in line with figure 1.1, which presented the research questions and the structure of thisthesis.

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• Due to the high number of parameters and the high number of desired simulations perexperiment, the model will be extreme computational expensive when executed full facto-rial.

• Agent-Based modeling allows to test and take into account multiple assumptions, by pro-viding a parameter that multiplies that assumption by zero when the assumption shouldnot be taken into account.

• Explain to a historian that you are developing a computer model to analyze geopoliticaldynamics in Mali and he will tell you such analyses are futile because some things canonly be done by human.

• Explain to an engineer that you are developing a computer model to analyze geopoliticaldynamics in Mali and he will praise you for introducing solid information/intelligence todecision makers who according to him until now only acted on gut feelings.

12.2 Potential and drawbacks of the proposed Strategic Geopo-litical Intelligence process

Below in table 12.2 we have presented an evaluation overview of the expected added value ofour proposed design compared to the traditional design. + is an increased value, - decreasedvalue, 0 same value and +/- a change for which the direction is uncertain. These expected valuechanges are based on the findings of chapter 6 and its validation through the evaluation of thecase study.

Table 12.1: Overview of the value criteria changed by thenew Strategic Geopolitic

Criterion ∆Described

inparagraph

Criterion ∆Described

inparagraph

Operation costsof intelligence

gathering0 12.2.1

Reliability of ananalysis

+ 12.2.9

Failure costs ofintelligencegathering

0 12.2.1Objectivity of

analysis+ 12.2.10

Duration ofintelligencegathering

- 12.2.2Operation costs

of reporting+ 12.2.11

Requiredintegrity ofgathered

intelligence

- 12.2.3Failure costs of

reporting+ 12.2.12


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Table 12.1 – Continued from previous page

Criterion ChangeDescribed

inparagraph

Criterion ChangeDescribed

inparagraph

Operation costsof an analysis

+ 12.2.4Duration ofreporting

+ 12.2.13

Failure costs ofan analysis

- 12.2.5Relevance of a

report tointelligence task

+ 12.2.14

Duration of ananalysis

+ 12.2.6Credibility of a

report+/-

12.2.15

Internal validity + 12.2.7Transferability of

a report+ 12.2.16

External validity + 12.2.8Dependability of

a report+ 12.2.17

Confirmability ofa report +

12.2.18

12.2.1 Same operational- and failure costs of intelligence gathering

Change: The new process is designed in such a way it requires the same type of intelligence,so the intelligence gathering process did not needed to be changed.

Effect: Since the intelligence gathering process remains practically the same, thereare no expected changes in the operational- and failure costs of intelligencegathering.

12.2.2 Shorter duration of intelligence gathering

Change: Use Agent-Based model to determine level of overriding importance of critical- andde-warning indicators and prioritize these accordingly in intelligence monitoring plan.

Effect: By knowing which indicators are able to make conclusive conclusions, it ispossible to focus on these instead. This prevents losing time by gatheringindicators that in hindsight hardly are relevant. Although the near term as-sessment already uses and prioritizes critical indicators, the mid/long termassessment does not. Furthermore due to the addition of de-warning indica-tors and the ability to be more precise on the bandwidth of the indicators,the gathering of indicators with a high overriding importance is also moreefficient and faster for near term assessments.

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Change: Store intelligence in GIS databases and couple the GIS databases to the inputs of theAgent-Based model.

Effect: Currently GIS databases are already used to store intelligence but the analystor the intelligence gatherer first needs to analyze the GIS database before hecan use the intelligence for the analysis. This step can be partly skippedwhen the Agent-Based model runs directly on the GIS database and therebysaving time.

Change: When assessing the value of intelligence, take into account the distance between thesource and the information.

Effect: This extra criterion will make that the value assessment will take a bit longer.However it should not require an extra study into the source and therefore nottake more extra time than was saved by prioritizing indicators and couplingGIS to an Agent-Based model.

12.2.3 Less integrity required of gathered intelligence

Change: Execute sensitivity analyses on uncertain parameter values, using the Agent-Basedmodel.

Effect: When the intelligence source is unreliable, is not close to the informationand/or the information itself is not credible, then it is important to studythe impact of the intelligence to the conclusions. Using an Agent-Basedmodel it is possible to develop scenarios for every variant on that piece ofintelligence. For example when the intelligence is that a group of 50 rebelspillaged a certain town it is possible to develop a scenario for that situation,but also scenarios where they entered another town in the same province, orthat they did entered the specific town but did not pillaged it, or that only25 rebels entered the town, or a combination of the before, etc. When isbeing discovered that in most of these situation a certain event will occur,it has been useful intelligence even though the intelligence was not entirelyaccurate. It is also useful to be able to report conclusions with detailedconditions under which they will be true or false. For example, with thesensitivity analysis, a conclusion can be that it is essential to move troopsto a certain province under the condition that more than about 20 rebelsentered any of the towns in that province. Under these conditions are thedetails of the intelligence less important and is a less a value of intelligencerequired to decide to move troops to that certain province.

Change: The Agent-Based model only requires relative values.

Effect: Although Agent-Based modeling is a quantitative method, it is able to han-dle very qualitative intelligence. An Agent-Based model can use relativevalues to represent attributes, use parameters determining the strength ofcausal effects and develop scenarios for any attribute value and any power ofcausal effect. So an Agent-Based model does not require quantitative dataon attribute values or the power of causal effects. Using the likelihood dis-tributions the analyst can point out that a certain attribute value is morelikely than another. So despite that the new process relies on a quantitative

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method it does not require a higher integrity of gathered intelligence, becauseAgent-Based modeling can use the same type of intelligence as qualitativeanalyses.

12.2.4 Higher operational costs of analysis

Change: The new Strategic Geopolitical Intelligence process will run parallel to the traditionalprocess.

Effect: The new process can take longer than the traditional process to execute,see analysis duration criterion. So in situations where time is very limitedit might be possible that only the traditional method can provide usefulconclusions in time. However during the execution of the traditional process,the new process needs to be executed as well to make sure that it remainsup to date. So even when disregarding the issues of implementation, the newStrategic Geopolitical Intelligence process will run parallel to the traditionalprocess. Therefore the total costs of the analysis will increase.

Change: It would be very valuable to have a lot of computing power available to run thesimulations on to get faster results with a higher external validity.

Effect: An important bottleneck of the proposed Strategic Geopolitical Intelligenceprocess is the requirement to run a very high number of simulations. Al-though the required computer power has not been assessed, it will likelyprove to be very valuable for the analysis to have a lot of computing poweravailable. There are different ways of obtaining more computing power, e.g.run simulations on multiple cores and computers, use very powerful comput-ers, etc. A future study has to show whether significant more computingpower is required at all, whether it is technically possible at all and whetherobtaining it is significant costly or not.

Change: Integration of near term and long term assessments into a single intelligence process.

Effect: Due to the integration of the two assessments into a single intelligence pro-cess, the new process requires less analysts than would require for the nearterm- and the mid/long term assessments together. So in case the new processis able to a large extend replace the traditional process, it could significantlylower the operational costs of the analysis. In any case the addition of thenew process next the the traditional one will not double the operational costs,since near term and mid/long assessments are executed in a single process.

12.2.5 Lower failure costs of analysis


Effect: As discussed at the required integrity of gathered intelligence criterion, thenew process is even able to provide useful conclusions even on uncertainintelligence. Therefore it is less likely to discover during the analysis thatthere is too little certain intelligence available to make useful conclusions.

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Effect: The development of the Agent-Based model has taught the analyst moreabout the dynamics in a case. So even without any recent and accurateintelligence the analyst can make some conclusions about relevant dynamicsin the case.

12.2.6 Longer duration of analysis

Change: Developing scenarios using an Agent-Based model.

Effect: The development of an Agent-Based model can be quite time consuming. Sowhen only a few scenarios have to be constructed, it is a lot faster to do thisvia a brainstorm. The higher the number of scenarios, the less this differencein duration becomes though.

Effect: As discussed at the criterion on operational costs of an analysis, we are notsure how computational expensive the models generally are. However if theyare computational expensive the development of all the scenarios can take asignificant amount of time, depending on the available computing power.

Change: Analyzing a case using an Agent-Based model.

Effect: Due to the extra possibilities a Agent-Based model offers to analyze a case,e.g. sensitivity analyses on gathered intelligence, the total time of analyzing acase can take longer with the new Strategic Geopolitical Intelligence process.

Change: Iterating based on intelligence updates.

Effect: The new Strategic Geopolitical Intelligence process is designed in such a waythat it depends on the intelligence update what part of the process has tobe redone. As long as the intelligence updates are in line with what theAgent-Based model produced, there is no need to develop a new model. Re-using a powerful tool like the Agent-Based model can save a lot of timeof analyzing. Depending on how often unexpected intelligence updates willoccur, the overall duration of analyzing a certain case might even be shorterwith the new process than the traditional process.

Change: Integrating the near term- and mid/long term assessments into a single process.

Effect: As discussed at the operational costs criterion, the integration of the twoassessment process might result in a decrease of total a number analysissteps. Thereby not only saving money but also time.

12.2.7 Higher internal validity of analysis

Change: Using an Agent-Based model to develop and analyze risk scenarios.

Effect: Developing an Agent-Based model forces the analyst to be very explicit in allhis assumptions, decisions and trade-offs. Plus that all these assumptions,decisions and trade-offs can be deducted from the model architecture. Dueto all the information stored in the model and the related log, there is lesstacit knowledge about the analysis for another analyst to miss. This benefitsthe internal validity when multiple analyst work together on a single case.

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Change: Making the integration of near term- and mid/long term assessments explicit in theStrategic Geopolitical Intelligence process.

Effect: The de facto integration between near term- and mid/long term assessmentsmake that the sharing of the right intelligence between the assessments de-pends on the experience of the analysts and coincidence. When this integra-tion is made explicit in the process design, then the sharing of informationcan be more structured and focused. This will make sure that the intelligenceand insights gathered in the one assessment but that is also relevant for theother, will be shared.

Change: Making the iteration of near term- and mid/long term assessments explicit in theStrategic Geopolitical Intelligence process.

Effect: Similar to the de facto integration of the assessment methods; when theiteration in a process is made explicit then the sharing of the right insightsbetween multiple analyses (resulting in different reports) will depend less onthe experience of the analyst or on coincidence.

Effect: By iterating throughout an analysis, the same processes will be executedmultiple times thereby making analysis errors more prone to be spotted.

12.2.8 Higher external validity of analysis

Change: Analyzing volatile states from a Complex Adaptive Systems paradigm using Agent-Based modeling.

Effect: Under the assumption that Complex Adaptive Systems paradigm is the mostrealistic way of perceiving volatile states, the use of Agent-Based modelinggreatly increases the accuracy of analyses. Complex Adaptive Systems as-sume that structures in a system are meta-stable and emerge through lowlevel interaction. Such systems can only be replicated by Agent-Based modelswhich lets low interactions create meta-stable structures. Being able to ac-curately describe the system under study, the volatile state, greatly increasesthe external validity of an analysis.

Change: Integrating near term and mid/long term assessments.

Effect: By making the integration between near term and mid/long term assessmentsexplicit, the results of both analyses are more forced to be compatible witheach other. Without the integration it could be possible that near termassessment reports could provide certain expected developments in a case,which would make the conclusions of the mid/long term assessment invalidand the other way around. This compatibility check is actually a way ofvalidating both reports.

Change: Iterating throughout the Strategic Geopolitical Intelligence process

Effect: When iterating explicitly throughout an intelligence process then the analystis forced to take re-evaluate and improve previous analysis steps, which willlead to an analysis with a sharper eye to detail and therefore likely lead to amore accurate result.

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Change: Include the distance to source criterion when assessing the value of gathered intelli-gence.

Effect: Taking into account the distance between the source and the information willprevent false conclusions based on credible intelligence from a reliable sourcethat prove to be wrong because the sources the source used were unreliable.

Change: Execute sensitivity analyses on gathered intelligence, using the Agent-Based model.

Effect: As discussed at the required integrity of gathered intelligence criterion, us-ing the sensitivity analyses allows the new intelligence process to take intoaccount uncertain intelligence as well. This automatically leads to a higheravailability of intelligence that can be used for the analysis and thereby amore accurate analysis.

Change: Use the Agent-Based model to develop a high number of scenarios.

Effect: By developing scenarios for every type of parameter configuration the newStrategic Geopolitical Intelligence process takes into account a very diversi-fied set of a many possible scenarios. This raises the likelihood of taking intoaccount the scenario that will in the end be materialized2.

12.2.9 Increased reliability of analysis

Change: Using an Agent-Based model to develop and analyze scenarios.

Effect: As discussed at the internal validity criterion, the development of an Agent-Based model forces to be very explicit in all his assumptions, decisions andtrade-offs. If the analyst has logged the reasoning behind all his assumptions,decisions and trade-offs, then together with the model itself, another analystwill be able to produce the same scenarios3. This is more reliable than repli-cating scenarios qualitatively, since the scenario development then dependsmore on the insights of the analysts.

Change: Making integration and iteration explicit in the Strategic Geopolitical Intelligenceprocess

Effect: As discussed at the internal validity criterion, making the iteration and inte-gration explicit forces the analysts to share intelligence and insights explicitly.This makes it more clear which intelligence and insights is used and where itcomes from, thereby increasing the confirmability of the report.

12.2.10 Increased objectivity of analysis

Change: Using an Agent-Based model to develop risk scenarios.

Effect: As discussed at the reliability criterion; using an Agent-Based model to de-velop scenarios, makes the scenarios itself less dependent on the insights ofthe analyst himself.

2Thereby lowering the likelihood of running into Black Swan events3For exact the same scenarios it might prove to be important that it is known which random seeds are used

for every simulation, depending on the level of chaos involved in the system.

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Effect: Using an Agent-Based model to automatically calculate consequence andlikelihoods of scenarios whilst they are constructed, forces the analyst to putlikelihood values on causes rather than on consequences. Separating the de-termining of likelihoods from developing scenarios makes that the analysts isnot/less influenced by the resulting scenarios when he determines the likeli-hoods.

12.2.11 Similar operational costs of reporting

Change: The only significant change in the required tools to report in the new design, is theAgent-Based model.

Effect: Even when the new Strategic Geopolitical Intelligence process is executedparallel to the traditional process, the results can be written in a singlereport. Only the use of Agent-Based modeling, if required extra computingpower appears to be costly, can make a difference in the operational costs ofreporting.

12.2.12 Higher failure costs of reporting

Change: The use of an Agent-Based model that provide detailed scenarios with exact values.

Effect: The Agent-Based model will provide scenarios with detailed values for everyattribute. So in the report can possibly be found the maximum number ofrefugees that will cross a certain border. However these exact values are bysome policy makers easily mistaken for accurate predictions. If the model wasnot made to assess the number of expected refugees crossing a certain border,then these values are only meant to describe a behavior and not to providepolicy makers with an indication on how big the refugee camps should be.This negative effect is limited when the model only uses relative values.

12.2.13 Longer duration of reporting

Change: Using an Agent-Based model for Strategic Geopolitical Intelligence can provide sig-nificant more useful insights.

Effect: The downside of an Agent-Based model being able to provide in more detail,more conclusions, is that the burden of reporting increases.

Change: Develop an Agent-Based model for the Strategic Geopolitical Intelligence process.

Effect: As described at various criteria above, using an Agent-Based model forces theanalyst to be more explicit about all his assumptions, decisions and trade-offs. However logging all these for the development of the Agent-Based modelalone can be very time consuming. However there are some software solutionson e.g. version control of models that help to log some of the decisionsautomatically [38, p.96-97]. Furthermore logging elaborately, explicitly andunderstandable for other analysts saves a lot of time during iteration andcommunication between analysts [ibid.].

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12.2.14 Increased relevance of report to intelligence task

Change: Use Agent-Based modeling to develop a high number of scenarios.

Effect: When developing a high number of scenarios, they can all be described inmore detail and as a set together still be as comprehensive as a small setof less detailed scenarios. Basically the ability of Agent-Based models todevelop a high number of scenarios also allows the development of moredetailed scenarios, which increases the analytical accuracy of the report.


Effect: As discussed at the required integrity of gathered intelligence criterion, thesensitivity analyses can provide relatively detailed conditions under whichcertain scenarios can realize or not. These conditions help focus politicaldiscussions on the right critical unknowns.

Change: Select scenarios also based on its risk value.

Effect: In line with risk management practices it makes sense to focus resourceson the scenarios with the highest risks. This saves resources that otherwisewould be wasted on scenarios that do not have a high impact anyway or areunlikely to occur at all4.

Change: Use the Agent-Based model to develop (adaptive) policies.

Effect: Although the development of policies technically exceeds the intelligence task,the integration of policy making into the intelligence process would benefitthe total crisis management operation. Due to this integration there is lesstacit knowledge lost in the communication between the intelligence serviceand the policy makers. Furthermore the suggestion to use the Agent-Basedmodel to develop adaptive policies would also make the policies more effectiveand more pro-active.

12.2.15 Changed credibility of report

Change: Using a computer to analyze geopolitical dynamics.

Effect: Many people with a social science background will tell you that using acomputer to analyze geopolitical dynamics are futile because some thingscan only be done by humans. Therefore, if not carefully presented, somereaders might distrust the findings just because they partly follow from acomputer analysis.

Effect: Many people with an engineering background will praise you for using acomputer to analyse geopolitical dynamics, because you are introducing solidinformation to decision makers who according to them until now only actedon gut feelings.

4Note though that when valuing consequences as much as likelihood, some of the highest risk can still be veryunlikely due to the extreme high consequences related to that scenario. The analyst should therefor never blindlyselect the top risk scenarios

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In order to check how open international relations and intelligence service experts areto quantitative methods we asked them in the survey, see appendix J, whether theyagreed on that quantitative methods can prove to be very useful. 79%, opposed to 7%,agreed with this statement and 14% had no opinion. This suggests that quantitativemethods should not be scaring away too many people in the international securitysector despite their social sciences background.

Change: Using animations to present the most important risk scenarios.

Effect: To compensate the distrust some people have with computers, the anima-tions that are generated by the model generally prove to be very useful tocommunicate findings [38, p.122].

12.2.16 Increased transferability of report

Change: Report on the dynamics that were identified during the development of the Agent-Based model.

Effect: Developing an Agent-Based model should teach the analyst more about therelevant dynamics of a case, or type of case, and thereby possibly add somerelevant insights into the report. These dynamics might also apply to similarcases.

Effect: It is likely that between cases a significant part of the model architecturecan be copied. However for every part has to be justified why it is possibleto copy it to another case. It is important to prevent that a groupthink ofAgent-Based models will occur.

12.2.17 Increased dependability of report

Change: Iterate throughout the Strategic Geopolitical Intelligence process.

Effect: In the new design will be based on the assessment of every intelligence updatehow much of the Strategic Geopolitical Intelligence process has to be re-done. This iteration can save important time when it appears that only theanalysis of risk scenarios has to be re-done. In that case the reports canbe updated faster than in the traditional process. This likely increases theoverall dependability of the reports because the reports will be more oftenup to date with the available intelligence.

12.2.18 Increased confirmability of report

Change: Using an Agent-Based model to develop and analyze risk scenarios.

Effect: As discussed several criteria above; developing an Agent-Based model forcesthe analyst to be very explicit in all his assumptions, decisions and trade-offs.Plus that all these assumptions, decisions and trade-offs can be deducted fromthe model architecture. The model, together with the log, are a rich sourceon how the analysis was executed and how this resulted into the findings thatare presented in the report.

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Change: Making integration and iteration explicit in the Strategic Geopolitical Intelligenceprocess

Effect: As discussed at the internal validity criterion, making the iteration and inte-gration explicit forces the analysts to share intelligence and insights explicitly.This makes it more clear which intelligence and insights is used and where itcomes from, thereby increasing the confirmability of the report.

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Part IV

Bibliography

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Bibliography

[1] Failing states: SOS. The Economist, June 2007.

[2] R.L. Ackoff. From data to wisdom. Journal of Applied Systems Analysis, 16:3–9, 1989.

[3] Bernardus Ale. Risk: An Introduction: The Concepts of Risk, Danger and Chance. Taylor& Francis, April 2009.

[4] Edward G. Anderson. A dynamic model of counterinsurgency policy including the effects ofintelligence, public security, popular support, and insurgent experience. System DynamicsReview, 27(2):111–141, April 2011.

[5] D. Appell. The new uncertainty principle. Scientific American, 284(1), January 2001.

[6] Autonomy. A different approach, 2013.

[7] Robert Axelrod. The Evolution of Cooperation: Revised Edition. Basic Books, revisededition, December 2006.

[8] Jan Bakker. Definitie van inlichtingen - postionering van een eeuwenoude maar nog steedscruciale functie in een moderne omgeving. NISA Intelligence, October 2012.

[9] Catherine M. Banks and John A. Sokolowski. From war on drugs to war against terror-ism: Modeling the evolution of colombias counter-insurgency. Social Science Research,38(1):146–154, March 2009.

[10] Ravi Bhavnani and Hyun Jin Choi. Modeling civil violence in afghanistan: Ethnic geog-raphy, control, and collaboration. Complexity, 17(6):42–51, July 2012.

[11] Ravi Bhavnani, Dan Miodownik, and Hyun Jin Choi. Violence and control in civil conflict.Comparative Politics, 2011.

[12] D. Bloom. Africa’s daunting challenges - room for debate. The New York Times, May2011.

[13] F. Borch. Fred borch | LinkedIn, 2012.

[14] Andrei Borshchev and Alexei Filippov. From system dynamics and discrete event topractical agent based modeling: Reasons, techniques, tools. In The 22nd InternationalConference of the System Dynamics Society, Oxford, England, July 2004.

[15] George E. P. Box and Norman Richard Draper. Empirical model-building and responsesurfaces. Wiley, January 1987. Essentially, all models are wrong, but some are useful(p.424) AND Remember that all models are wrong; the practical question is how wrongdo they have to be to not be useful (74).

108

[16] Bear Braumoeller and Anne Sartori. The promise and perils of statistics in internationalrelations. In Models, Numbers, and Cases: Methods for Studying International Relations.University of Michigan Press, Michigan, 2004.

[17] Antony Bryant and Kathy Charmaz. The SAGE Handbook of Grounded Theory: PaperbackEdition. SAGE, January 2010.

[18] Alan Bryman, Saul Becker, and Joe Sempik. Quality criteria for quantitative, qualitativeand mixed methods research: A view from social policy. International Journal of SocialResearch Methodology, 11(4):261–276, 2008.

[19] W.M. Bulleit and M.W. Drewek. Agent-based simulation for human-induced hazard anal-ysis. Risk Analysis, 31(2):205–217, 2011.

[20] David S. Bushnell. Input, process, output: a model for evaluating training. Training &Development Journal, 44(3):41, March 1990.

[21] D. Caldwell. The 1990 middle east crisis: A role-playing simulation. Foreign PolicyAnalysis Notes, 16(2):13–15, 1991.

[22] K. Carlson. Strategic planning and models of reality, 2013.

[23] Emil Cauer, Wolfgang Mathis, and Rainer Pauli. Life and work of wilhelm cauer (1900-1945). In Proceedings of the 14-th International Symposium on Mathematical Theory ofNetworks and Systems, Perpignan, France, 2000.

[24] Lars-Erik Cederman. Emergent Actors in World Politics. Princeton University Press, May1997.

[25] Lars-Erik Cederman. Endogenizing geopolitical boundaries with agent-based modeling.Proceedings of the National Academy of Sciences of the United States of America, 99(Suppl3):72967303, 2002.

[26] Lars-Erik Cederman. Articulating the geo-cultural logic of nationalist insurgency. Order,conflict, and violence, page 242270, 2008.

[27] Lars-Erik Cederman. Cederman, lars-erik, prof. dr., 2013.

[28] Lars-Erik Cederman and Luc Girardin. Exploring geopolitics with agent-based modeling.2005.

[29] US Central Intelligence Agency. INTelligence: human intelligence, 2010.

[30] US Central Intelligence Agency. INTelligence: signals intelligence, 2010.

[31] US Central Intelligence Agency. INTellingence: open source intelligence (sic), 2010.

[32] Jeffrey T. Checkel. Transnational dynamics of civil war. In workshop Mobilizing AcrossBorders: Transnational Dynamics of Civil Warsponsored by the Centre for the Study ofCivil War, Peace Research Institute, Oslo (PRIO) and the School for International Studies,Simon Fraser University. Oslo, Norway, page 2021, 2010.

[33] Nazli Choucri, Christi Electris, Daniel Goldsmith, Dinsha Mistree, Stuart E. Madnick,J. Bradley Morrison, Michael D. Siegel, and Margaret Sweitzer-Hamilton. Understanding& modeling state stability: Exploiting system dynamics. In Aerospace Conference, 2006IEEE, page 111, 2006.

[34] Saul Bernard Cohen. Geopolitics of World System. Rowman & Littlefield, 2003.

109

[35] Hart Rudmann Commission. New world coming: American security in the 21st century.Technical report, Departmen of Defense (United States), Washington D.C., September1999.

[36] United Nations Security Council. Resolution 2085 (2012), December 2012.

[37] C. Daalen, W.A.H. Thissen, E. Pruyt, and H. Phaff. Continues Systems Modelling, SystemDynamics - SPM2310/EPA1321. Delft University of Technology, Delft, August 2007.

[38] Koen H. van Dam, Igor Nikolic, and Zofia Lukszo, editors. Agent-Based Modelling ofSocio-Technical Systems. Springer, 2013 edition, October 2012.

[39] Robert A. Day. How to Write & Publish a Scientific Paper: 5th Edition. Oryx Press, 5edition, June 1998.

[40] Ad de Jonge Center. Lecturers - ad de jonge center - university of amsterdam, February2013.

[41] Defensie. Defensie inlichtingen en veiligheids instituut | ministerie van defensie, 2013.

[42] Policy Analysis Simulation Lab TU Delft. Exploratory modelling and analysis (EMA)workbench exploratory modeling workbench, 2011.

[43] Don A. Dillman. Mail and Internet Surveys: The Tailored Design Method – 2007 Updatewith New Internet, Visual, and Mixed-Mode Guide. Wiley, November 2006.

[44] Kevin Dutton. The Wisdom of Psychopaths: What Saints, Spies, and Serial Killers CanTeach Us About Success. Scientific American / Farrar, Straus and Giroux, October 2012.

[45] B. Enserink, J.F.M. Koppenjan, W.A.H. Thissen, D.P. Kamps, and G. Bekebrede. Analysevan complexe omgevingen. Number SPM 2110 in Analyse van Complexe Omgevingen.Faculty of Technology, Policy and Management, Delft, 2008.

[46] Joshua M. Epstein. Generative Social Science: Studies in Agent-Based ComputationalModeling. Princeton University Press, 2006.

[47] Armand Vallin Feigenbaum. Total quality control. McGraw-Hill, 3 edition, 1991.

[48] Uwe Flick. An Introduction to Qualitative Research. SAGE, March 2009.

[49] Centre for Mathematics and Informatics. CWI amsterdam | research in mathematics andcomputer science, 2013.

[50] The Fund for Peace. The indicators, 2013.

[51] Center for social research methods. Qualitative validity, October 2006.

[52] Centre for Study of Civil War and International Peace Research Institute. ACLED, 2013.

[53] US Air Force. ICAM architecture part II-Volume IV - functioning modeling manual(IDEF0). Technical Report AFWAL-TR-81-4023, US Materials Laboratory, Air ForceWright Aeronautical Laboratories, Air Force Systems Command, Wright-Patterson AirForce Base, Ohio, June 1981.

[54] Jay W. Forrester. System dynamics, systems thinking, and soft OR. System DynamicsReview, 10(2-3):245256, 1994.

[55] Alexander L. George. Presidential Decisionmaking in Foreign Policy: The Effective Useof Information and Advice. Westview Press, February 1980.

110

[56] Luc Girardin and Lars-Erik Cederman. A roadmap to realistic computational models ofcivil wars. In Advancing Social Simulation: The First World Congress, page 59, 2007.

[57] Kristian Skrede Gleditsch and Nils B. Weidmann. Richardson in the information age:Geographic information systems and spatial data in international studies. Annual Reviewof Political Science, 15(1):461–481, June 2012.

[58] Varun Grover and William J. Kettinger. Process Think: Winning Perspectives for BusinessChange in the Information Age. Idea Group Pub, December 1999.

[59] Jaber F. Gubrium and James A. Holstein. Postmodern Interviewing. SAGE, March 2003.

[60] Guetzkow. Varieties of simulations in international relations research. Journal of ConflictResolution, 7(4):668–687, January 1963.

[61] Richards J. Heuer. Limits of intelligence analysis. Orbis (Philadelphia), 49(1):7594, 2005.

[62] Richard J. Heuer Jr. Psychology of intelligence analysis. US Government Printing Office,1999.

[63] (ISO) International Standardization Organization. ISO/IEC 19505-1:2012 - informationtechnology – object management group unified modeling language (OMG UML) – part 1:Infrastructure, 2012.

[64] (ISO) International Standardization Organization. ISO/IEC 19505-2:2012 - informationtechnology – object management group unified modeling language (OMG UML) – part 2:Superstructure, 2012.

[65] Mark P. Kaminskiy and Bilal M. Ayyub. Terrorist population dynamics model. RiskAnalysis, 26(3):747–752, June 2006.

[66] J.P. Keller, K.C. Desouza, and Y. Lin. Dismantling terrorist networks: Evaluating strate-gic options using agent-based modeling. Technological Forecasting and Social Change,77(7):1014–1036, 2010.

[67] Khadimally. Project interface management, June 2011.

[68] David. Lane. Artificial worlds and economics. Technical Report 92-09-048, Santa FeInsitute, 1992.

[69] Stphane Lefebvre. A look at intelligence analysis. International Journal of Intelligenceand CounterIntelligence, 17(2), 2004.

[70] Robert J. Lempert, Steven W. Popper, and Steven C. Bankes. Shaping the next onehundred years. Product page, RAND, Santa Monica, 2003.

[71] Tara Leweling and Otto Sieber. Using systems dynamics to explore effects of counterter-rorism policy. In System Sciences, 2007. HICSS 2007. 40th Annual Hawaii InternationalConference on, page 198198, 2007.

[72] Yvonna S. Lincoln and Egon Guba. Naturalistic Inquiry. SAGE, April 1985.

[73] MIVD. Jaarverslag MIVD 2011. Technical report, MIVD, Den Haag, 2011.

[74] D Moreno. Potential US intervention in peru: A simulation. Foreign Policy AnalysisNotes, pages 4–5, 1992.

[75] NATO. STANAG 2022. DODSID DW9705, Information Handling Services.

111

[76] NATO. NATO glossary of terms and definitions, 2012.

[77] NATO and Euro-Atlantic Partnership Council. Generic Early Warning Handbook, volume(2001)2. NATO, Brussels, 2001.

[78] I. Nikolic. Co-Evolutionary Method For Modelling Large Scale Socio-Technical SystemsEvolution. PhD thesis, Tu Delft, Delft, November 2009.

[79] Igor Nikolic. Lectures on agent-based modeling (SPM4550), 2012.

[80] T. O’Connor. Intelligence collection, 2013.

[81] University of Amsterdam. Kwalitatieve analyse technieken (voorheen methoden envaardigheden), 2012.

[82] University of Amsterdam. Quantitative analysis techniques for intelligence services - UvAstudiegids - vak beschrijving, 2012.

[83] US Department of Defense. Department of Defense Dictionary of Military and AssociatedTerms. US Department of Defense, Washington D.C., December 2012.

[84] T. B. Pepinsky. From agents to outcomes: simulation in international relations. EuropeanJournal of International Relations, 11(3):367394, 2005.

[85] Orrin H. Pilkey and Linda Pilkey-Jarvis. Useless Arithmetic: Why Scientists Can’t Predictthe Future. Columbia University Press, 2007.

[86] PRNewswire. DATOPS presentation, 2013.

[87] E. Pruyt. State of art versus state of science of integrated risk-capability analysis underdeep uncertainty. Delft, June 2012. Delft University of Technology.

[88] Erik Pruyt and Caner Hamarat. The concerted run on the DSB bank: An exploratorysystem dynamics approach. In Proceedings of the 28th International Conference of theSystem Dynamics Society, 2010.

[89] Qualtrics. Qualtrics.com | sophisticated research made simple, 2013.

[90] corporation RAND. Nuclear deterrence | RAND, 2013.

[91] M. Raska. International security: Concepts, paradigms and theories - lecture two, January2011.

[92] Bhavnani Ravi, Dan Miodownik, and Jonas Nart. REsCape: an agent-based frameworkfor modeling resources, ethnicity, and conflict. The Journal of Artificial Societies andSocial Simulation, 11(27), 2008.

[93] Ute von Reibnitz. Scenario Techniques. McGraw-Hill, 1988.

[94] Harry T. Reis and Charles M. Judd. Handbook of Research Methods in Social and Per-sonality Psychology. Cambridge University Press, March 2000.

[95] J. Rowley. The wisdom hierarchy: representations of the DIKW hierarchy. Journal ofInformation Science, 33(2):163–180, February 2007.

[96] Jennifer E. Rowley and John Farrow. Organizing Knowledge: An Introduction to ManagingAccess to Information. Ashgate Publishing, Ltd., April 2008.

112

[97] Peter Schwartz. The Art of the Long View: Planning for the Future in an UncertainWorld. Currency Doubleday, reprint edition, April 1996.

[98] Dutch Defense Intelligence & Security Service and Netherlands Defence Academy. Guidingnotes for lecture on military intelligence: Indicator based warning intelligence, 2012.

[99] Dutch Defense Intelligence & Security Service and Netherlands Defence Academy. Militaryintelligence: Indicator based warning intelligence, 2012.

[100] S. Sharma, H. Singh, and G.R. Gerhart. Simulation of convoy of unmanned vehicles usingagent based modeling. In Proceedings of SPIE - The International Society for OpticalEngineering, volume 6736, 2007.

[101] B. Snyder. RIP: the project triangle, October 2009.

[102] Robert E. Stake. The Art of Case Study Research. SAGE, April 1995.

[103] Julian Talbot and Miles Jakeman. Security Risk Management Body of Knowledge. Wiley,2nd updated edition, August 2009.

[104] N. N. Taleb and M. Blyth. Black swan of cairo: How suppressing volatility makes theworld less predictable and more dangerous, the. Foreign Aff., 90:33, 2011.

[105] Nassim Nicholas Taleb. The black swan: the impact of the highly improbable. PenguinBooks, April 2008.

[106] Steven J. Taylor and Robert Bogdan. Introduction to qualitative research methods: aguidebook and resource. Wiley, February 1998.

[107] UCDP. UCDP georeferenced event dataset - uppsala university, sweden, November 2012.

[108] Leiden University. War, peace and state building, 2009-2010, 2009.

[109] Leiden University. Willigen, dr. N.J.G. van - medewerkers - faculteit der sociale weten-schappen, September 2011.

[110] Leiden University. Fred l. borch - medewerkers - campus den haag, January 2013.

[111] Gilliam Valk de. Dutch intelligence - towards a qualitative framework for analysis : withcase tudies on the Shipping Research Bureau and the National Security Service (BVD).PhD thesis, Rijksuniversiteit Groningen, Groningen, October 2005.

[112] Gilliam Valk de and Willemijn Aerdts. Lectures of the course qualitative analysis methodsof the minor intelligence studies, 2012.

[113] Ministerie van Algemene Zaken, Ministerie van Binnenlandse Zaken en Koninkrijksrelaties,Ministerie van Defensie, and Ministerie van Justitie. Wet op de inlichtingen- en veilighei-dsdiensten 2002 (wiv). Technical Report Wiv 2002, Staatscourant, Den Haag, February2002.

[114] M. Van Leeuwen. De Arabische lenteen geopolitiek. Atlantisch Perspectief: Tijdschriftvoor Internationale Betrekkingen en Veiligheidspolitiek, 35(4):48, 2011.

[115] Charles Walcott. Simple simulations 2: a collection of simulation/games for politicalscientists. American Political Science Association, December 1980.

113

[116] Michael D. Ward, editor. Theories, Models, and Simulations in International Relations:Essays and Research in Honor of Harold Guetzkow. Westview Pr (Short Disc), illustratededition edition, September 1985.

[117] P. Weaver. Origins of modern project management. Vancouver, April 2007.

[118] Nils Weidmann and Christoph Zuercher. How wartime violence affects social cohesion:The spatial-temporal gravity model. In APSA 2011 Annual Meeting Paper, 2011.

[119] Nils B. Weidmann. Violence from above or from below? the role of ethnicity in bosniascivil war. The Journal of Politics, 73(04):1178–1190, August 2011.

[120] Nils B. Weidmann and Idean Salehyan. Violence and ethnic segregation: A computationalmodel applied to baghdad. Int Stud Q, 2011.

[121] J. Wucherpfennig, N. B. Weidmann, L. Girardin, L.-E. Cederman, and A. Wimmer. Po-litically relevant ethnic groups across space and time: Introducing the GeoEPR dataset.Conflict Management and Peace Science, 28(5):423–437, February 2011.

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Part V

Appendices

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Appendix A

Assumptions, research decisions anddefinitions used

A.1 Overview of subjective decisions

During the research some subjective decisions had to be made. These concern assumptions onpositions where no conclusive proof against it or in favor of it exists. Or formulations of certaindefinitions of terms that are otherwise too broadly interpretable. A table of contents of theappendix is presented below:

A.1 Overview of subjective decisions . . . . . . . . . . . . . . . . . . . . . 115

A.2 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

A.3 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

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A.2 Assumptions

This sections shows in table A.2 an overview of all the assumptions made in this research. Thisexcludes the assumptions made for the model, presented in appendix 11.

Table A.1: List of assumptions during research

Assumption Where Justification

For Strategic GeopoliticalAnalyses it is best to con-sider a geopolitical systemto be a Complex AdaptiveSystem

Section1.1

As shown during the Arab spring, low level interac-tions like communicating ideas can explode into rev-olutions and let existing actors dissolve and new ac-tors emerge. This behavior does not fit the traditionalparadigms of (Neo-)Realism, (Neo-)Liberalism. Therejection of the necessary assumptions of these twoparadigms by experts in the survey, validates this as-sumption (see paragraph 7.1.1.

Currently are Agent-Basedmodeling studies not im-plemented by Dutch, andlikely very little other, in-telligence & security ser-vices to execute StrategicGeopolitical Intelligence.

Paragraph1.2.2

The expert judgements of all the interviews presentedin appendix H indicated that Agent-Based modelingwas likely a new method for the purpose of Strate-gic Geopolitical Analyses. One e-mail of an expertwho was informed by other sources did indicated thereare models using the similar Multi Agent Simulationmethod (see appendix I.4. However based on the de-scriptions of these models, these seem more aimedfor data mining and monitoring instead of hypothe-sis/scenario development.

The MIVD uses the quanti-tative Regression- and thequalitative Analogies anal-yses for their trend analy-ses

ParagraphE.9.2

These two are the most commonly used trend analysesmethods in all industries [45, p.126-131] and Prof.dr.Rudesindo Nunez Queija told they used quantitativemethods for trend analyses recently (see appendixH.4.2.1.1.

Scientific research torisk management is a lotgreater than the scientificresearch to intelligencestudies

Paragraph6.6.1

Scientific risk studies are a lot older [3] than scientificintelligence studies [H.5.2.3], which implies the bodyof knowledge of risk studies is also a lot greater.

There is generally DeepUncertainty present withinStrategic Geopolitical In-telligence

Paragraph6.5

Because only little intelligence can be gathered abroadit is unlikely to have reliable intelligence on every keyvariable in a foreign geopolitical system.

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A.3 Definitions

The rest of this appendix shows the definitions of terms we have adopted or created, includingthe arguments for the definitions.

A.3.1 Definition of a Complex Adaptive System

“an adaptive network exhibiting aggregate properties that emerge from the local interaction amongmany agents mutually constituting their own environment”

Below is the above definition of a Complex Adaptive System (CAS) per aspect explained, fullyquoted from Cederman’s book Emergent actors in world politics [24, p.51-p.52]:

Aspect 1: A CAS exhibits emergent properties, by which we understand phenomena that “(i)can be described in terms of aggregate-level constructs, without reference to the at-tributes of specific [micro-level agents]; (ii) persists for time periods much greaterthan the time scale appropriate for describing the underlying micro-interactions; and(iii) defies explanation by reduction to the superposition of ’built in’ micro-propertiesof the [CAS]”1. Based on analytical nontransperancy, this definition of emergenceemphasizes the importance of not hard-wiring the desired outcomes into the pro-cess2. (. . . ) Emergent properties may refer to any temporal or spatial patterns inthe agents’ behavior, their internal models, or their internal and external structure.A particularly important type of emergence pertains to the formation of composite,hierarchical actor1.

Aspect 2: A CAS presupposes local interaction among agents rather than globally managed be-havior. The emphasis of local-level rules draws heavily on the bounded-rationalityparadigm in organization theory3. Instead of assuming agents equipped with unlim-ited calculative capacity and complete knowledge of their strategic environment, thefocus on local interactions postulates actors driven by rules of thumb that help themsatisfice. Out of this web of local relations, macrolevel patterns emerge. Thus thesesystems are fundamentally self-organizing4.

Aspect 3: A CAS features a large number of agents. (. . . ) Computer science suggests thatthe properties of large computational networks differ fundamentally from the toyproblems studied by game theory and mainstream artificial intelligence. In their studyof macro level “phase shifts” in computational ecologies, Huberman and Hogg (1987)5

assert that “as local parameters are varied they may show sudden changes in overallperformance which cannot be generally inferred from investigations of correspondingsmall-scale systems”. Like wave phenomena in physics, emergent patterns of this

1Lane, D. A. 1992. “Artificial Worlds of Economics.” Santa Fe Institute Working Paper 92-09-048. Page 3.2Holland, J. H., et al. 1987. Induction: Processes of Inference, Learning, and Discovery. Cambridge, Mass.:

MIT Press. Page: 3503Simon, H. A. 1955. “A Behavioral Model of Rational Choice.” Quaterly Journal of Economics 69:99-118.

Cohen, M. D., March, J. and Olsen, J. P. 1972. “A Garbage Can Model of Organizational Choice.” AdministrativeScience Quaterly 17:1-25.

4Hayek, F. A. 1973. Law, Legislation and Liberty: Rules and Order. Chicago: Chicago University Press.5Huberman, B. A. and Hogg, T. 1987. “Phase Transition in Artificial Intelligence Systems.” Artificial

Intelligence 33:155-71. Page 170

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type usually require a large number of microlevel elements6. In addition, the largesize of CAS models reinforces the previous point about local interaction since it isunrealistic and impractical to expect the agents to devise elaborate action plans withrespect to all other agents. In complex systems, game-theoretic approaches sufferfrom a combinatorial explosion of strategies that can only be avoided by reliance onbounded rationality7. It is also worth noting that despite its being related to CASresearch, chaos theory focuses on behavior of simple rather than of complex systems8.

Aspect 4: A CAS is adaptive because of the microlevel agents’ capacity to change their behavioror because of ecological effects that influence the development of the entire system.The system evolves over time, usually far from any global equilibrium, and producesperpetual novelty9. In some cases, the agents’ internal models also allow for anticipa-tion, which means that the agents try to anticipate the consequences of their actions,but this is not a constitutive feature of a CAS. Although their internal models maybe quite elaborate in some cases, as in a game of chess it is impossible to predict thefinal outcome. CAS systems are far removed from the state of common knowledge inthe game-theoretic sense10. The limitations imposed on internal models correspondto the focus on bounded rationality. Friedrich Hayek stresses that the “necessaryand irremediable ignorance on everyone’s part of most of the particular facts whichdetermine the actions of all the several members of human society”. (. . . ) Thus thereis no choice to model the acquisition of knowledge explicitly11. Modeling long-termand large-scale social processes rests on agents endowed with dynamic internal modelswhose choices and preferences may evolve over time12.

Note that all references made in the elaboration on the definition of Complex Adaptive Systemsfrom Cederman are not included into the main bibliography of this thesis. Since we have notaccessed these sources.

6Hayek, F. A. 1967. Studies in Philosophy, Politics and Economics. Chicago: University of Chicago Press.Page 25

7Scharpf, F. W. 1991. “Games Real Actors Could Play: The Challenge of Complexity.” Journal of TheoreticalPolitics 3:277-304

8Kellert, S. H. 1993. In the Wake of Chaos: Unpredictable Order in Dynamical Systems. Chicago: ChicagoUniversity Press. Page 5

9Holland, J. H. 1992. Adaption in Natural and Artificial Systems: An Introductory Analysis with Applicationsto Biology, Control, and Artificial Intelligence. Cambridge, Mass.: MIT Press. Page 184

10Aumann, R. 1976. “Agreeing to Disagree.” Annals of Statistics 4:1236-39.11Simon, H. A. 1986. “Rationality in Psychology and Economics.” In The Behavioral Foundation of Economic

Theory. Journal of Business, supplement, ed. Hogarth, R. M. and Reder, M. W., 209-24.North, D. C. 1990.Institutions, Institutional Change and Economic Performance. Cambridge: Cambridge Uni-versity Press.

12Lindgren, K. 1992. “Evolutionary Phenomena in Simple Dynamics.” In Artificial Life II, ed. Langton, C.G., Taylor, C., Farmer, J. D. and Rasmussen, S. Redwood City, Calif.: Addison-Wesley.

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A.3.2 Definitions of Geopolitical Analyses

Geopolitical Analysisa study of geographical and political dynamics

Strategic Geopolitical Analysisa study of geographical and political dynamics at the national and international level

Strategic Geopolitical Intelligencea study of geographical and political dynamics in order to form policy and/or military plans atthe national and international level

Tactical Geopolitical Intelligencea study of geographical and political dynamics in order to plan military operations of task forces

Operational Geopolitical Intelligencea study of geographical and political dynamics in order to plan the execution of military campaignsor operations in a local area

Strategic Geopolitical Sciencea study of geographical and political dynamics in order to study phenomena in the realm ofinternational relations at the national and international level

Our definitions, formulated above, is based on the general concept of Geopolitical Analysis, threelevels of analysis and the two applications of the analysis that are considered in this thesis. Beloware the four aspects defined in more detail. The reader can deduct the definitions of general-,tactical- and operational- analyses and science automatically from the definitions above.

Aspect 1: “Geopolitics is the analysis of the interaction between, on the one hand, geographicalsettings and perspectives and, on the other hand, political processes. The settingsare composed of geographical features and patterns and the multilayered regions thatthey form. The political processes include forces that operate at the internationallevel and those on the domestic scene that influence international behavior. Bothgeographical settings and political processes are dynamic, and each influences and isinfluenced by the other. Geopolitics addresses the consequences of this interaction.”We chose this definition of Cohen [34, p.12] because this definition of GeopoliticalAnalysis is in line with the Complex Adaptive Systems view.

Aspect 2: Intelligence is in this thesis the main considered application of Geopolitical Analyses.Intelligence in general is defined by the US department of defense and NATO as:“The product resulting from the collection, processing, integration, evaluation, anal-ysis, and interpretation of available information concerning foreign nations, hostile orpotentially hostile forces or elements, or areas of actual or potential operations. Theterm is also applied to the activity which results in that product”[83, JP 2-0] [76,2-I-6].

Aspect 3: The level of a Geopolitical Intelligence can be one of the following three [ibid.]:

• “Strategic Intelligence is Intelligence required for the formation of policy andmilitary plans at national and international levels.”

• “Tactical Intelligence is Intelligence required for the planning and conduct oftactical operations” [83, JP 2-01.2], which are operations on a “level of war at

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which battles and engagements are planned and executed to achieve militaryobjectives assigned to tactical units or task forces” [ibid. JP 3-0].

• “Operational Intelligence is Intelligence that is required for planning and con-ducting campaigns and major operations to accomplish strategic objectives withintheaters or operational areas.” [ibid. JP 2-0], which are delineated “geographicareas in which military operations are conducted” [ibid. JP 3-0].

Aspect 4: Science is also an application of Geopolitical Analyses, and is also executed at threedifferent levels. Within International Sciences, the one we consider in our thesis, areGeopolitical Analyses executed to objectively study certain phenomenon to under-stand that phenomenon better [H.3.2.1.1].

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A.3.3 The DIKW pyramid and the definitions of Data, Information and In-telligence

DIKW stands for Data, Information, Knowledge and Wisdom. For the pyramid we have pre-sented in figure E.4 in paragraph E.5, we changed knowledge into intelligence and wisdom intopolicy. This adaption is based on Bakker his adaption of the DIKW pyramid [8, p.11], whopositioned intelligence and policy in the pyramid. Furthermore we focused on the intelligencelayer and made a distinction between General intelligence and Intelligence on critical indicators.The concept of the layer structure remains the same as in the traditional DIKW pyramid. Thestructure of the layers show that in a converging matter; data creates information, informationcreates intelligence and intelligence creates policy. The definitions on Data, Information andIntelligence are presented below.

DataMultiple discrete, objective facts or observations, which are unorganized and unprocessed andtherefore have no meaning or value because of the lack of context and interpretation.

InformationOrganized or structured data, which has been processed in such a way that the information nowhas relevance for a specific purpose or context, and is therefore meaningful, valuable, useful andrelevant.

IntelligenceConfidential knowledge resulting from the collection, processing, integration, evaluation, analy-sis, and interpretation of available information concerning a warning problem, required for theformulation of strategy, policy, and military plans and operations.

The definitions of Data and Information are quoted from the work of Rowley et.al. [96, p.6]and is self explanatory. We chose these definitions because they are modern formulations of theData, Information, Knowledge and Wisdom pyramid that we used. The definition of Intelligenceshould also be self-explanatory, but is constructed based on three different sources. We elaboratebelow per aspect of Intelligence, where it is based on and why we included it in the definition.

Aspect 1: The confidentiality aspect is one of the two distinctions knowledge layer in the tradi-tional pyramid, and the intelligence layer we replaced it with [8, p.11].

Aspect 2: To indicate that intelligence is also a “product of resulting from the lower layers”(Data and Information) we adopted partially the definition of Intelligence from theUS Department of Defense [83, JP 2-0]

Aspect 3: As the US Department of Defense definition also indicates, intelligence has a certainfocus. The focus in the intelligence processes we studied, is defined by the WarningProblem [77, p.16].

Aspect 4: The formulation of strategy, policy, and military plans and operations are the goalsadopted from the definitions of all the types of Geopolitical Analyses, presented inA.3.2. That these goals require a forward looking character of information, is thesecond distinction between knowledge and intelligence layer [8, p.11].

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Appendix B

State-of-art description

First we will discuss the traditional, quantitative, qualitative, analysis methods used/developedto study strategic geopolitical systems for International Relations science. Then we will discussin the same matter the analysis methods applied in practice by intelligence services. After thiswe will discuss why we expect, already in the exploration phase of this thesis, that Agent-Basedmodeling can improve the traditional analyses methods. An overview of this, can be seen below:

B.1 State-of-art of Strategic Geopolitical Science . . . . . . . . . . . . . . 122

B.2 State-of-art in Strategic Geopolitical Intelligence . . . . . . . . . . . 124

B.3 Striking the golden mean with Agent-Based modeling . . . . . . . . 124

Note though that we are fully dependent on public literature and interviews, which is generallynot so rich when it concerns intelligence practices, so the state-of-practice might be incomplete.

B.1 State-of-art of Strategic Geopolitical Science

B.1.1 Quantitative methods in Strategic Geopolitical Science

Within Strategic Geopolitical Science are statistics the most common applied quantitativemethod. Statistics are often, and mainly, applied to test theories based on trends of manycases [16, p.129-130] [H.3.2.1.1]. The requirement of many, comparable, cases of statistics makesthe tool only useful to focus on overall trends, or general behaviors and phenomena. Theseoverall trends or findings on general behaviors and phenomena can be used however as inputdata for case specific simulation models. Simulation modeling methods are within StrategicGeopolitical Sciences generally applied to analyze specific geopolitical systems, these models aremainly based on Game Theory and public- and rational choice theories [116]. Examples aresimulation models on the second Strategic Arms Limitation Talks (SALT II) [115], the 1990Middle East Crisis [21], Potential US intervention in Peru [74], the spread of nuclear weaponstechnology [60], etc.

However there are three main problems of traditional (rational) geopolitical modeling methods[24, p.32-36]1. The first two problems are the assumptions of Fixed and Given Actors and

1With his dissertation in 1997 Prof.dr. Lars-Erik Cederman is one of the pioneers to introduce Agent-Based

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Common and Complete Knowledge required to apply traditional game theory or rational choicesimulations. Under these assumptions, actors do not change their preferences or identity throughtime, are fully rational and they know everything on the identity, preferences, and resources of allthe other actors. This is especially unrealistic when considering volatile states, where changingpreferences of actors can emerge into new aggregate actors who through revolution can evenreplace an actor. The third problem of traditional game theorist is their desire to identifyequilibria but then one assumes equilibria exist at all in a system. Even when an equilibriumdoes exist then the historical processes should be fast enough to reach that equilibrium beforeits environment changes and the equilibrium itself will be changed.

Besides Agent-Based modeling, which we will discuss after the next paragraph, there is alsothe System Dynamics method that is able to cope with most of the above problems. SystemDynamics is based on major differential equations, using stocks and flows, representing all thefactors under study and their interrelations [54]. Examples of System Dynamics models usedfor Strategic Geopolitical Science are models on counterinsurgency policy effects [4, 71], modelson terroris population dynamics [9, 65, 71], understanding state stability [33], etc. The problemon this method however, is that it requires quite some knowledge on the structure of the systemunder study and has to assume that this system structure is static. This makes it impossible foran analysis to continue after the system has been through a revolution and therefore by itselfnot effective to perform Strategic Geopolitical Analyses on volatile states.

B.1.2 Qualitative methods in Strategic Geopolitical Science

Case specific geopolitical analyses are generally performed through Narration [112, p.4(4)]. Nar-ration is a Grounded Theory research method that uses logical reasoning based on past eventsthat have affected a certain case, the experience of the analyst and general International Rela-tions theories to arrive at conclusions [17, p.165-290]. This is a more flexible method that doesnot force the analyst to look for equilibria or consider actors to be fixed and given with commonand complete knowledge. Which is useful for people who, like us, consider geopolitical systemsto be a Complex Adaptive System. However, ironically, reasoning from the Complex AdaptiveSystems paradigm especially requires a modeling methodology. This is due to the inherent com-plexity and context dependence of Complex Adaptive Systems. The logic and strict reasoningrequired for modeling helps analysts to maintain coherence and consistency in the analysis of aComplex Adaptive System [ibid.p.30], or limit the negative effects of psychological phenomenalike Groupthink. Furthermore a model can help the analyst to formulate precise and testabletheories. Finally, and maybe most importantly, a computer can overcome the limitations of thehuman mind to comprehend the high number of interrelations present in Complex AdaptiveSystems. It is even possible to create Serious Games out of simulation models like Agent-Basedmodeling [38, p.219-220], which can incorporate human behaviors in the model and helps tobetter get the insights of the study across to non-experts.

modeling into geopolitics. His dissertation became an influential work in all future endeavors to implement Agent-Based modeling into geopolitics. Besides being cited in many other studies, his dissertation received the EdgarS. Furniss Book Award [27]. This is why we feel comfortable adopting parts of his line of reasoning.

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B.2 State-of-art in Strategic Geopolitical Intelligence

B.2.1 Quantitative methods in Strategic Geopolitical Intelligence

Quantitative methods applied in Strategic Geopolitical Intelligence generally concern data min-ing and statistical analyses. These methods are essential due to the large size, the heterogeneousnature and uncertain reliability of the data sets available to intelligence services [H.4.2.1.1].Other quantitative methods, including the ones discussed in paragraph B.1.1 are likely appliedin practice by intelligence services around the world2. However this is likely to be done by justa few intelligence services and probably not very extensively [H.4.2.1.1] [H.4.2.1.2.3] [H.6.2.2.4].

Note though that quantitative methods are more broadly used for tactical and operational analyses[H.4] [H.6]. Differences between the levels of analysis are explained in appendix A.3.2.

B.2.2 Qualitative methods in Strategic Geopolitical Intelligence

Also within intelligence services is Narration a very important tool in their Strategic GeopoliticalIntelligence operations. This qualitative method is however just a part of a bigger analyticalframework. Most commonly used methods, besides narration, are Strengths, Weaknesses, Op-portunities and Threat (SWOT) analyses and categorization frameworks [112, p.1(3)-8(4)] toforce the analyst think out of the box and to be transparent about all assessment decisions. Tobe transparent during analysis is a very important aspect of an assessment within intelligenceservices since, if research process was correct, the analyst can then not be blamed for assessmentsthat turn out to be incorrect [H.6.2.2.5]. This is directly a main argument for intelligence servicesto increase the usage of quantitative methods since they, like argued in paragraph B.1.2, help theanalyst maintain coherence and consistency and formulate more precise and testable theories.Which should be significantly for the Analysis of Competing Hypotheses, which is commonlyapplied by intelligence services within NATO to test and evaluate multiple hypotheses [ibid.p.1(5)-8(5)] [77, p.101]. Finally, again as in paragraph B.1.2, we want to stress that quantitativeanalyses using a computer can overcome the limitations of the human mind to comprehend thehigh number of interrelations present in Complex Adaptive Systems.

B.3 Striking the golden mean with Agent-Based modeling

To strike the golden mean between rigid modeling and escaping to narration, many InternationalRelation scientists now suggest to apply Agent-Based modeling for Strategic Geopolitical Science[ibid.] [84, p.368] [28, p.4] [92]. An important argument of them is that Agent-Based modelingprovides the possibility to analyze the patterns and regularities in a system in an inductivebottom-up approach [24, p.53]. By focusing on the low level interactions that causes patternsand regularities to emerge, one uses generative science to model his perspective on the real worldby letting the model grow. This means one does not have to identify or understand the globalinterdependencies in the system to be able to grow a model that exhibits the effects of global

2Based on speculation by experts interviewed it is expected that the more advanced and resource rich serviceslike the US’s CIA and the Israeli Mossad apply these quantitative methods. These are speculations made byUS Col.Frederic Borch III (Ret.), Prof.dr. Rudesindo Nunez Queija, NL Capt. Onno Goldbach and Dr. Giliamde Valk. The speculations have not been noted in the interview reports in appendix H since these reports onlycontain non-speculative statements

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interdependencies [14, 7]. Growing a model provides the analyst with better understanding of thesystem under study [46, p.51]. Furthermore growing an Agent-Based model provides the analystthe analyst with a computer model of a real world system that includes path dependencies andcan include concepts like bounded rationality and changing preferences. This opposed to thequantitative modeling methods presented in Paragraph B.1.1 that all take a deductive top-downapproach. However opposed to the qualitative approaches, with an Agent-Based model onedoes has a quantitative computer model to his disposal. With such a model to the analysthis disposal, he can; maintain coherence and consistency, formulate more precise and testabletheories, and can identify patterns and regularities emerging through more complex interactionsa human mind can comprehend.

Note though that an Agent-Based model will only generate possible patterns and regularities butthat we will keep depending on the human mind for recognizing and interpreting these behaviors,abilities a computer inherently lacks3. So Agent-Based modeling should always be consideredas just a tool supporting a larger analysis framework using other qualitative (and quantitative)methods.

3A nice example indicating this interdependency between humans and computers when analyzing complexity,is one Dr.ir. Igor Nikolic mentions in his lectures at the TU Delft; “We rely on computers to store databases offaces but only recently we managed to train advanced computers in such a way they can recognize a face, whereasa baby recognizes the face of its mother as soon it can see”[79].

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Appendix C

The basics of understanding how anAgent-Based model works

This appendix explains the basics of understanding of how Agent-Based models work and howit is been used in the thesis. The presented explanation is a summary of quotations of Damet.al. their book on Agent-Based models [38, p.57-67] and a lecture by Dr.ir. Nikolic [79] onAgent-Based models. An overview of all the explained concepts in this appendix is presentedbelow:

C.1 Anatomy of an Agent-Based model . . . . . . . . . . . . . . . . . . . . 126

C.2 Agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

C.3 State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

C.4 Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

C.5 Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

C.6 Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

C.7 Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

C.1 Anatomy of an Agent-Based model

Figure C.1 shows the anatomy of an Agent-Based model. All the terms mentioned in this figurewill be separately explained in the next sections, the final section will also explain the conceptof Time in an Agent-Based model.

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Figure C.1: Anatomy of an Agent-Based model

C.2 Agent

All Agents have the following properties:

• Encapsulated, meaning that they are clearly identifiable, with well-defined boundaries andinterfaces;

• Situated in a particular environment, meaning that they receive input through sensors andact through effectors;

• Capable of flexible action, meaning that they respond to changes and act in anticipation;

• Autonomous, meaning that they have control both over their internal state and over theirown behavior; and

• Designed to meet objectives, meaning that they attempt to fulfill a purpose, solve a prob-lem, or achieve goals.

Basically Agent is a persistent thing which has some state we find worth representing, and whichinteracts with other agents, mutually modifying each others states.

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C.3 State

States are:

• Stuff that Agents know or has (including memory);

– Can be private or public;

– Can be static or dynamic; and

– Can depend on the Rule.

• State of an agent is a composite of internal, local and global interactions.

C.4 Rules

Rules are the internal models of Agents that describe, according to which mechanics, how statesare translated to actions, or new states. The rules are usually based on an assumption ofrationality or bounded rationality of the agents. Rules can be static or dynamic, and maydepend on the internal, local and environmental states. Examples of often used types of internalmodels that can function as Rules in an Agent:

• Rule based : nested if-then-else-structures;

• Multi Criteria Decision Making : Options and weights;

• Inference engines: Expert systems, facts(States) and decision heuristic (decision trees);

• Evolutionary computing : Using genetic algorithms to find a optimal solution in a largesolution space; and

• Machine learning : Using neural networks to train Agents to recognize patterns and act onthem

C.5 Actions

Based on States, Rules and/or other Agents an Agent will perform (or not perform) some actionthat can affect Other Agents, Own state, Own rule and/or Environment. The whole set ofobservable actions of an Agent, comprises his Behavior.

C.6 Environment

An Environment is what the Agent is in, it provides the agents with information and structure.Basically everything that is not an Agent, but is relevant is the Environment. The Environmentaffects the Agent, and the Agent can affect the Environment. Agent-Based models can simulatetruly complex systems when an Environment is an Agent himself among other Environments,nested in an even bigger Environment. There are four basic structures an Environment can be:

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• Soup: In a Soup all Agents are equally likely to interact with all other Agents. Thisbecause the organization of Agents is completely random or all Agents interact with allother agents;

• Space: In a Space are only the Agents interacting that are within the same neighbor-hood, Agents have a certain distance they consider their neighborhood and can reposi-tion themselves through the Space according to their Rules. This space can be physicalwhere distance can be measured in geometric units, which makes it possible to integrateAgent-Based models with Geographic Information Systems. However the space can alsobe considered more meta-physical where the distance for example between a rich Agentand a poor Agent can be measured in monetary units;

• Small-world networks: A Small-world network is a network of Agents that is after eachchange to the system constantly reorganized according to short average path length char-acteristics. This results in a network where Agents are not directly connected to all otherAgents, but are indirectly connected via in as little as possible steps to all other agents;and

• Scale-free networks: A scale-free network have like Small-world networks short averagepath length characteristics but they also have hubs that can dramatically speed up thetimescales in the model. These hubs stem from power law degree distributions that dis-tributes connections, probability to interact, or popularity among Agents which makessome Agents highly connected while the vast majority have few connections.

C.7 Time

Three aspects of time in Agent-Based model are essential to understand how an Agent-Basedmodel operates:

• Discrete time: While reality takes place in real, continuous, time, agent-based modelsare forced to happen in the discrete time of computers. All conventional computers workwith timed instruction clocks, performing rounds of operations within each time step.This reflected by the use of a Tick as the smallest unit of time. Simulations can play withdiscrete time by redefining how much time a Tick is meant to represent, with no theoreticallower or upper limits;

• Assumption of parallelism: Although real world complex adaptive systems are massivelyparallel, computers hardly can do more than one task at a time. In order to representthe parallelism of the real world with a serial processing device, all actions of Agents arescheduled to occur one after the other, but are assumed to happen at the same time.The disjoint between what actually happens and what is assumed to happen can createsignificant problems. To limit that no Agent has unrealistic more benefit than others to actbefore another one, the sequence of which Agent acts when is determined pseudo-randomlyper Tick. Furthermore the modeller can choose that a tick takes place in multiple parts,first all Agents execute type X actions, then type Y actions, etc.; and

• Scheduler : The scheduler is the central controller of the simulation that progresses theticks and ensures that all actions are executed. Most commonly, this involves randomisingthe iteration order of agents at each step.

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Appendix D

The basics of understanding IDEF0schematics

This appendix explains the basics of understanding IDEF0 schematics, and how it is been used inthe thesis. The presented explanation is a summary of a directly adopted section of the originaldocumentation of the US Air Force Program for Integrated Computer Aided Manufacturing [53,p.17-46], which developed the IDEF0 method. An overview of all the explained concepts in thisappendix is presented below:

D.1 The process boxes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

D.2 Inputs and outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

D.3 Controls and mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . 131

D.4 Decomposition of diagrams . . . . . . . . . . . . . . . . . . . . . . . . 131

D.1 The process boxes

Boxes on a diagram represent processes. Processes show what must be accomplished withoutidentifying any other necessary aspect such as needs or means. Processes are described by anactive verb phrase written inside the box. Each box on a diagram is numbered in its lower rightcorner, in order from 1 to at most 6. Such processes occur over periods of time.

D.2 Inputs and outputs

The side of the box at which an arrow enters or leaves shows the arrow’s role as an input oran output. From left to right (input to output), a process transforms data. Data may beinformation, objects, or anything that can be described with a noun phrase.

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D.3 Controls and mechanisms

A control describes the conditions or circumstances that govern the process. The roles of inputand control are different. The distinction is important to understanding the operation of systems.Every process box will have at least one control arrow. The bottom of a box is reserved toindicate a mechanism, which may be the person or device which carries out the process. Theinput and output shows WHAT is done by the function, the control shows WHY it is done, andthe mechanism shows HOW it Is done.

D.4 Decomposition of diagrams

A model is a series of diagrams with supportive documentation that breaks a complex subjectinto its component parts. The initial IDEF0 diagram is the most general or abstract descriptionof the whole system. This diagram shows the A0 process that can be broken down into theA0 diagram that shows the details of each major process as boxes. These boxes can be brokendown into still more diagrams, until the system is described to any desired level of detail. Eachdetailed diagram is the decomposition of a box on a more general diagram. At each step, thegeneral diagram is said to be the ”parent” of the detailed diagram. A detailed diagram is bestthought of as fitting ”inside” a parent box. Node numbers are used to indicate the position ofany diagram or box in the hierarchy. For example, A21 is the diagram that details box 1 on theA2 diagram.

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Appendix E

Full description of the StrategicGeopolitical Intelligence process

This appendix shows the complete description of the Strategic Geopolitical Intelligence process.As in the main thesis it is described in IDEF0 diagrams. The main diagram is presented infigure E.1 and shows the basics of how IDEF0 diagrams should be read, for a more elaborateexplanation on the IDEF0 method we refer to appendix D. Below is every process elaboratelyexplained and every section and paragraph refers to the process ID numbers that are indicatedin the IDEF0 diagrams.

E.1 Main IDEF0 diagram

The main diagram is presented in figure E.1. The figure shows the basics intelligence processof Intelligence gatherers and Intelligence analysts turning Data and General intelligence intoIntelligence reports, according to NATO doctrine [77] [99] and guided by an Intelligence task setup by the mandated ministers1 [113, art.7e] or NATO agreements [H.7.2.8].

Inputs

Mechanisms

Outputs

Controls

A0-B0

Strategic Geopolitical Intelligence

Intelligence reportInformation


Data

Intelligencetask

NATO doctrine



Figure E.1: IDEF0 diagram of the Strategic Geopolitical Analysis process

1The mandated ministers for foreign analyses are, according to Dutch intelligence & security law, the Dutchprime minister in accordance with the minister of Defense [113, art.7e].

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E.2 (A0) Strategic Geopolitical Intelligence

The first decomposition of the IDEF0 diagram is presented in figure E.2 and it shows in the col-ored frames, the two different types of Strategic Geopolitical Intelligence methods the NATO pre-scribes: Near term assessments and Mid/long term assessments [112, p.3-10 (1)] [99] [H.6.2.2.2].Near term assessments are aimed on situation monitoring and crisis detection. Mid/long termassessments are aimed to pro-actively determine policy options in order to develop the propercapabilities to contain future crises [I.5]. The positioning of these goals within crisis managementat the strategic geopolitical level is presented in table E.1. The crisis states in the table are appli-cable to both interstate conflicts and intrastate conflicts that (potentially) affect NATO memberstates [ibid. p.8]. Basically the two assessment methods are executed as two independent sets ofprocesses, the only interaction is that the intelligence gathered for the mid/long term assessmentis also used as general intelligence input to set up near term assessments [H.7.2.1]. Below willfirst the near term assessment set of processes explained (A1-A3) and then the mid/long termassessment set of processes.

Table E.1: Overview of the overall crisis management process, adopted from the NATO hand-book for Early Warning [77, p.5].

Crisis states

Escalation De-escalation

PeaceDisagree-

mentConfront-

ationArmedconflict

Builddown

Stability

Cri

sis

Man

agem

ent

Acti

vit

ies Situation

monitoringX X X X X X

Crisisdetectionsupport

X X

Containment X X X X

Disengagement X X X

Peacebuilding X X

E.3 (A1-A3) Near term assessment processes

The blue section of figure E.2 shows the three main processes of the near term assessment.The figure shows that an Intelligence collection plan is developed to guide the Gathering ofintelligence, as well that sets of Scenarios and Critical indicators that are developed to guide theAssessment of intelligence [99, p.3] [112, p.10 (1))] [77, p.11]. These three controls help to focusthe Intelligence- gathering and assessment processes to be more efficient with resources. Thissignifies Indicator based intelligence from classical intelligence [99, p.4] [H.6.2.2.2]. All processesare executed in a linear fashion without any explicit iterations between the processes. Thewhole set of processes starts again when a new intelligence task is provided. All the individualprocesses (A1, A2 and A3) are further elaborated in the next three paragraphs.

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Near term assessment processes

A2

Gathering intelligence for

near term assessment

A3

Assessing intelligenceIntelligence

on critical indicators

Near term intelligence

report



A1

Setting up near term assessment


Intelligence task

Warning problem

Set of Critical indicators

Set of Scenarios

Intelligence collection plan

Mid/long term assessment processes

B1


hypothesesB3


B2


assessment



Mid/long term intelligence report

Plausible hypotheses


NATO doctrine

NATO doctrine

NATO doctrine


Data




Data

Figure E.2: A0 diagram of the Strategic Geopolitical Intelligence process

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E.4 (A1) Setting up near term assessment

As can be seen in figure E.3, there are four types of processes to create scenarios, criticalindicators and an Intelligence collection plan [99, p.3] [112, p.10]. Each type of process isseparately explained.

A11

Defining warning problem

A12

Developing n number of scenarios

A131

Determining x number of critical

indicators for scenario 1

A14

Developing intelligence

collection planWarning problem

Scenario 1

A132


indicators for scenario 2

Scenario 2

A13n


indicators for scenario n

Scenario 3

x critical indicators for

scenario 1


scenario 2


scenario n

Intelligence collection

plan





InformationIntelligence

Intelligencetask

NATO doctrine

NATO doctrine

NATO doctrine

Figure E.3: A1 diagram of the Strategic Geopolitical Intelligence process: Setting up Near termassessment

E.4.1 (A11) Defining warning problem

First a warning problem needs to be defined to delineate the Strategic Geopolitical Intelligenceprocess [77, p.16]. Such a warning problem is the same as a traditional Problem definitionthat we know from Hard systems thinking like Systems analysis and Operational research [37,p.28-29]2. NATO doctrine prescribes that the Warning problem definition should consider thefollowing five questions [77, p.16]:

• “Who is the actor?”

• “What action is of concern (. . . )?” Is this a threat, risk or concern? What is the an-ticipated end state assessed to be? If this situation were to play out, what potential newsheadline would be of greatest concern to the decision maker?”

• “When/How is this behavior to take place?”

• “Where is this action taking place (e.g. internal instability, external aggression or transna-tional threat arenas?)”

• “Why is this action taking place?”

2For example, developing a problem definition is also the first step in an Agent-Based modeling study [38,p.74-75].

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An example of a Warning problem, developed by the Dutch defense intelligence service: “TheKorean Peninsula Warning problem is defined as the potential threat of regional instability causedby North Korean military activities in the mid-term to unify the Korean Peninsula under com-munist rule which could affect the interests of the Alliance” [99, p.20] [112, p.9(1)]. Such aWarning problem is based on the available General intelligence from the mid/long term assess-ments [H.7.2.1].

E.4.2 (A12) Developing scenarios

Depending on the available resources assigned to a certain intelligence study, n number of Sce-narios are developed. Such Scenarios are hypothetical sequences of possible future events thatwill lead to a certain end state [98, p.1]. For every every Near term assessment a group of expe-rienced experts develops through brainstorm three scenarios. To get a broad view on possiblescenarios there are the following three types of Scenarios developed [H.7.2.3]:

1. A scenario based on the present trend

2. A worst-case scenario

3. A scenario that would require a paradigm shift in the system under study

E.4.3 (x13) Determining critical indicators for each scenario

Again depending on the available resources assigned to a certain intelligence study, x number ofCritical indicators are determined per Scenario. An indicator is a single event in the sequenceof possible future events that make a Scenario [98, p.1]. A Critical indicator is one of thoseevents that indicate a significant change in the level of threat3 and allows the analyst to make,change or modify an assessment [99, p.12]. The NATO has set the following criteria for Criticalindicators [ibid. p.11]:

• “Critical indicator is forward looking: they are future events”

• “Critical indicator is collectible, early, reliable, diagnostic, and unambiguous”

• “The set of critical indicators per Scenario covers Military, Political, Social, and Economicconsiderations (sometimes Science and Technology as well)”

• “The set of critical indicators provides clues that a Scenario is occurring”

Examples of critical indicators, developed by the Dutch defense intelligence service, are [ibid.p.19]:

• Unemployment

• Civil disorder

• Sudden key leadership changes

• Military forces cooperation with government

3Threats are considered by the NATO to be a product of the intentions, capabilities and the actions of an“opponent” [99, p.7].

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E.4.4 (A14) Developing intelligence collection plan

The Intelligence collection plan is what the analysts use to formally instruct the Intelligencegatherers to monitor the occurrences of Critical indicators [H.6.2.2.5]. The shape of the In-telligence gathering process is a trade-off of the available resources assigned to the intelligencestudy, the Critical indicators that need to be monitored and the capabilities of the NATO in theregion of interest. Here it is important to make a selection of Critical indicators based on theirusefulness to monitor. Usefulness is determined by the product of the probability that a crisiswill occur if event materializes, the probability the event will be detected and the uniqueness ofthat event for a specific Scenario [77, p.100].

E.5 (A2) Intelligence gathering

As can be seen in figure E.2, the Intelligence gathering process (A2) is providing Intelligenceon critical indicators to the Intelligence assessment process (A3). The Intelligence on criticalindicators is based on Data and General intelligence. The position of these terms are explainedin the next sub paragraph, followed by a sub paragraph on intelligence gathering disciplines,and a sub paragraph on the assessment of intelligence integrity.

E.5.1 Hierarchy of knowledge

There is a hierarchy in Data, Information, General Intelligence and Intelligence on critical indi-cators, this is represented in an adapted “DIKW Pyramid” of Ackoff [2] [95] in figure E.4. Thetask of the Intelligence gatherer is to collect, process, integrate, evaluate, analyze and interpreta certain layer and converge it into its aggregate layer [2] [83, JP 2-0].

For readers unfamiliar to the concept of the pyramid or readers who require the definitions ofData, Information and Intelligence are referred to Appendix A.3.3.

Intelligence on critical indicators


Information

Data

Intelligence from other intelligence studies

Data from other intelligence studies

CLASSINTdata

OSINTdata

HUMINTdata

IMINTdata

SIGINTdata

Information from other intelligence studies

Figure E.4: Adapted DIKW Pyramid

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E.5.2 Intelligence gathering disciplines

As the arrows indicate in figure E.4, the external input of data, information and intelligenceinto the gathering process can be obtained from other intelligence studies by the intelligenceservice. However, generally there is additional data required to create additional informationand intelligence. In that case; data is actively gathered using the CLASSINT, HUMINT, IMINT,OSINT and SIGINT intelligence gathering disciplines4 [73, p.38-39]. Short definitions and someexamples on the disciplines are presented below, to indicate the types of data an intelligenceservices can gather:

• CLASSINT is a category of data, information and intelligence that is shared by cooperatingorganizations; [73, p.38].

• HUMINT is “a category of intelligence derived from information collected and providedby human sources” [76, p.2-H-5]:

– “Clandestine acquisition of photography, documents, and other material” [29];

– “Debriefing of (foreign) nationals who travel or live abroad” [ibid.].

• IMINT is a category of intelligence derived from the collection, mapping and interpretationof photographs from the air to the ground[80]:

– Satellite imagery and aerial imagery by planes or unmanned aircraft systems [73,p.37];

– Note: IMINT allows for Geospatial intelligence (GEOINT) that combines geospatialinformation with imagery’s in GIS databases [ibid.];

• OSINT is a category of “intelligence derived from publicly available information, as well asother unclassified information that has limited public distribution or access”[76, p.2-O-2]:

– “The Internet” and “traditional mass media (e.g. television, radio, newspapers, mag-azines)”; [31]

– “Specialized journals, conference proceedings, and think tank studies” [ibid.];

– Publicly available “Geospatial information (e.g. commercial maps and imagery prod-ucts)” [ibid.].

• SIGINT a category of intelligence derived from the “collection and exploitation of signalstransmitted from communication systems, radars, and weapon systems as signals intelli-gence” [30]:

– Information gathered by intercepting communications [ibid.];

– Information collected using radars and other weapons systems [ibid.].

E.5.3 Integrity of intelligence

Before conclusions are made based on the content it is important, especially within the intelli-gence sector, to assess the integrity of the gathered intelligence [H.4.2.1.1]. The NATO intelli-

4Many other ***INT disciplines are defined in varying literature but the Dutch defense intelligence service(MIVD) considers the presented categories as the main five and the rest are sub categories [73, p.38-39]

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gence services assesses the intelligence according to the STANAG 2022 NATO doctrine[H.7.2.5],that prescribes to categorize intelligence on the following two criteria [75]:

• Reliability of source, rated ordinal from A to E; and

• Credibility of intelligence, rated ordinal from 1 to 5.

So intelligence from a completely reliable source that is very credible is coded A1, the oppositeis coded E5. If no assessment on a criteria can be made, then a sixth category, F or 6, is used[ibid].

E.6 (A3) Intelligence assessment

Besides a log that makes all assumptions, decisions and trade-off during an analysis explicit, thenear term assessment consists out of three different types of reports:

Tier 1 report: Executive summary;

Tier 2 report: NATO intelligence warning matrix; and

Tier 3 report: Warning problem and critical indicators list.

E.6.1 (Tier 3) Warning problem and critical indicators list

The tier 3 report indicates the Warning problem and shows per critical indicator an assessmenton a four point ordinal scale:

• Routine;

• Abnormal;

• Significant;

• Extreme; and

• (Unknown).

Whether a Critical indicator (e.g. unemployment) is routine or abnormal, is judged by a groupof experts based on the gathered intelligence on that indicator. During the assessment theexperts do take into account the consequences of their assessment on an indicator. The lineof reasoning of the experts to make a certain judgement has to be made explicit to assuretransparency [H.7.2.5]. An NATO example of such a report, without the annex containing thelines of reasoning, is presented in figure E.5 [77, p.20].

E.6.2 (Tier 2) NATO intelligence warning matrix

Based on the list of assessments of each indicator, the NATO intelligence warning matrix canbe built. This tier 2 report provides summarizes per geographical region the internal/externalstatus of; governance, socio-economics and security forces. Such reports are for senior managersthat have to manage resources in the ever changing landscape [ibid. p.15]. An NATO exampleof such a report is presented in figure E.6 [ibid. p. 19].

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Figure E.5: Capture of tier 3 report of a near tear assessment on the fictional Kiloland


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E.6.3 (Tier 1) Executive summary

Finally a high-level executive summary for decision-makers is developed. An NATO example ofsuch a tier 1 report is presented in figure E.7 [ibid. p.18]. It shows a brief text indicating thecritical indicators that were spotted, a map and an expected trend of the overall crisis level. Thesame symbols and four point ordinal scale for the assessments is used as in the tier 3 report.The intelligence service also tells in the brief text what the likely implications of these eventsare for the future. These likely implications are, just like the trend assessment, based on howmuch the occurred events / spotted critical indicators are in line with the developed scenariosfrom where the critical indicators stem from.

E.7 (B1-B3) Mid/long term assessment processes

Figure E.8 shows the mid/long term assessment processes, captured from the A0 diagram offigure E.2. It shows that the three main processes of the near term assessment have similarequivalents in the mid/long term assessment. However there are many differences. In mid/longterm assessments the intelligence gathering process is continues and focuses on general indi-cators based on NATO doctrine and the individual best practices of the intelligence gatherers[H.7.2.6]. The gathered intelligence is then used to generate a set of new hypotheses. A hy-pothesis in mid/long term assessments are, similar to a scenario in near term assessments; afalsifiable hypothetical sequence of possible future events that will lead to a certain end state

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[H.7.2.7]. From the set of new generated hypotheses a selection of Plausible hypotheses is made,all implausible hypotheses will be iteratively refined into a plausible hypothesis (see section E.8)[112, p.3 (1)]. Then specific intelligence on the Plausible hypotheses is used in the Analysis ofcompeting hypotheses to determine which Plausible hypotheses are most likely to be true. Incase insufficient intelligence is available to reject or accept hypotheses in this final process, moreintelligence on these hypotheses is gathered [H.7.2.6]. So in the whole mid/long term assessmentprocess; first intelligence is gathered to develop many Plausible hypotheses (exploration) andthen intelligence is gathered to determine which of the Plausible hypotheses are most likely to betrue (validation) [ibid.]. The reports that follow from the mid/long term assessment can rangefrom short updates for short term policy to elaborate analyses for long term policies like capa-bility development in the region [H.7.2.8]. Similar to the near term assessment sub paragraphsabove, the next sections will elaborate on each of the three main processes B1, B2 and B3.

B1


hypothesesB3


B2


assessment



Mid/long term intelligence reportPlausible

hypotheses


NATO doctrine

NATO doctrine

NATO doctrine

Data





Near term assessment processes (A1 & A2)

Figure E.8: Capture of the B1-B3 Mid/long term assessment processes out of the A0 diagram

E.8 (B1) Developing n number of plausible hypotheses

As can be seen in figure E.9 the development of Plausible scenarios can be decomposed intothree processes executed in a linear fashion with one iteration loop. The three processes willbe explained below, as well as the distinctions between the three types of Plausible hypotheses;Trends, Wild cards and Scenarios.

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B11

Generating hypotheses

B12

Selecting plausible hypotheses

B13

Develop hypotheses based

on critical unknowns

Hypotheses

To be refined hypotheses


Critical unknowns

Trend hypotheses

Scenariohypotheses

Intelligenceanalyst

Intelligenceanalyst

Intelligenceanalyst

Intelligencetask

NATOdoctrine

NATOdoctrine

Wild cardhypotheses

Three types of plausible hypotheses

Figure E.9: B1 diagram of the Strategic Geopolitical Intelligence process: Developing n numberof plausible scenarios

E.9 (B11) Generating hypotheses

The generation of hypothesis is also done using a brainstorms by experts. There are generallyfive strategies an intelligence service applies to generate hypotheses [H.7.2.7] [62, 32-42] [112,p.3 (1)]:

• Applying theory;

• Comparison with historical situations;

• Situational logic; and

• Data immersion

• Wild cards5.

It is desirable that all four strategies are used to get a broad perspective on the case at hand[ibid. p.42]. However analysts generally lack the will or time to apply all four strategies. So thestrategies generally follow the habits and preferences of the analysts [ibid.]. Knowing that thestrategy has an effect on the perspective of the analysis, the analyst has to take into accountthat different perspectives between an analyst and a policy maker will affect the communicationbetween the two [ibid.]. Below are, in separate sub paragraphs, all four strategies explained:

5This strategy is added to the traditional four of Heuer [62, 32-42] and is based on the fact that Wild Cardhypotheses are used by the Dutch defense intelligence service (MIVD) [112, p.3 (1)] [H.7.2.4].

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E.9.1 Applying theory

Applying theory is the strategy of using scientific (international relations) theory and generalizeit on the case of interest. So theoretical effects which are identified by scientists, using e.g.multiple case studies or scientific modeling, are then taken into account by the intelligenceanalyst in the development of hypotheses [62, p.34-36]. Example of such a theoretical effect is:“The economic development and massive infusion of foreign ideas in a feudal society leads topolitical instability” [ibid.].

E.9.2 Comparison with historical situations

Comparison with historical situations is done by analysts to identify the critical path that led upto the current situation. This is used to base the hypotheses on. The identification of the criticalpaths is based on identified events in the same case, or based on similar situations and eventsin other cases [ibid. p.38-40]. For this the NATO intelligence services uses the quantitativeRegression- and the qualitative Analogies analyses6.

E.9.3 Situational logic

Situational logic is one of the most common strategies used by the intelligence analyst to generatehypotheses [62, p.32]. It is also one of the two strategies that is truly intelligence driven [H.7.2.7].This strategy regards the case as a unique situation with its own unique logic. Hypotheses inthis strategy are based on the cause-effect relationships, means-end relationships and the goalsof actors identified by the analyst and the intelligence obtained [62, p.32-34]. To support this,methods like Strenth, Weakness, Opportunity and Threat (SWOT) analyses are often used [112,p.1(3)-8(4)].

E.9.4 Data immersion

This fourth strategy is also intelligence driven [H.7.2.7] and aims to let data speak for themselves,without fitting it into a preconceived pattern [62, ibid. p.32, p.40]. The analysts attempts toobtain this by studying the data and identifying the patterns that emerge from it. These patternsare then used to develop hypotheses [ibid.].

E.9.5 Wild cards

Next to the (semi-)structured strategies to develop hypotheses there is an extra strategy usedby intelligence services to attempt to think out of the box7. The Wild card hypotheses aregenerated by group brainstorms of experts and are hypotheses that, if to be true, requires aparadigm shift in the system under study. This is similar to the third type of Scenario in theNear term assessment (A12 Developing n number of Scenarios) E.4.2.

6This is an assumption based on the similarity between this strategy and trend analyses, which can be doneboth quantitatively (Regression analyses) and qualitatively (Analogy analyses) [45, p.126-131]. For a furtherelaboration on this assumption we refer to appendix A.2

7This strategy seems to aim to identify Black Swans, a type of event we discussed in paragraph 3.7.2.

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E.10 (B12) Selecting plausible hypotheses

Based on the generated hypotheses of the previous process (B11) the available intelligence iscategorized [62, p.43]. Then Critical intelligence is identified. Critical intelligence has quacontent, certainty and reliability, the power of accepting or rejecting a hypothesis8. Then thegenerated hypotheses of the previous process (B11) are assessed on whether they are in line withthe Critical intelligence. There are four types of outcomes on this assessment process [112, p.3(1)]:

• The Critical intelligence is in line with the hypothesis ⇒ Trend hypothesis;

• The Critical intelligence rejects the hypothesis ⇒ To be refined hypothesis; or

• There is no Critical intelligence supporting or rejecting the hypothesis, but hypothesis wasdeveloped as a Wild card ⇒ Wild card hypothesis

• There is no Critical intelligence supporting or rejecting the hypothesis and hypothesis wasnot developed as a Wild card ⇒ identification of Critical unknowns.

If there is no Critical intelligence supporting or rejecting a certain hypothesis and that hypothesiswas not generated as a Wild card in the previous process (B11), then this there is criticalintelligence missing (Critical unknowns). The process of selecting secenarios will stop as soonas there are sufficient Plausible hypotheses and Critical unknowns identified [62, p.43] [55].Generally this results in about one to three Trend hypotheses, one or two Wild card hypothesesand varying numbers of Critical unknowns [H.7.2.10].

E.11 (B13) Develop hypotheses based on critical unknowns

To keep the broad perspective on hypotheses, there are Scenario hypotheses developed. Thesehypotheses are not based on available intelligence, which can provide a limited view on the case,and are not as radical as Wild card hypotheses. Scenario hypotheses are developed based onlogic, rather than on available data [112, ibid. p.3 (4)]. Scenario hypotheses are often developedthrough Narration, as discussed in the state-of-art appendix in paragraph B.1.2, but is alsodone using Axes [ibid. p.4(4)]. This structured Narration method, developed by former Shellemployees [93] [97], is explained by example in figure E.10. It follows these four steps [45,p.145-151]:

Step 1: Arrange all the Critical unknowns along a level of impact axis and a level of uncertaintyaxis (black dots in left diagram in figure E.10);

Step 2: Select two or three Most important critical unknowns by identifying the critical un-knowns that are both highly uncertain and have a high impact;

Step 3: Construct a Scenario space by using the Most important critical unknowns as axes in adifferent diagram (right diagram in figure E.10); and

Step 4: Develop one or two Scenario hypotheses, using Narration, in categories that are consid-ered to be the most interesting [H.7.2.10].

For example in category B of figure E.10, the Narrated hypothesis would be along the lines ofthe regime using chemical weapons whilst some military divisions have already defected.

8Think of the integrity assessment of intelligence in paragraph E.5.3.

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Legend:

Leve

l of

imp

act

Level of uncertainty

Will regime use chemical weapons?

Low

Low

High

High

Will militaryremain loyal to

regime?

Will there be mass migration to bordering

states?

Will tourism flow stop?

Will there be cold weather that disrupts

infrastructure?

Regime will use chemical weapons

Regime will not use chemical weapons

Mili

tary

will

rem

ain

loya

l to

reg

ime M

ilitary will

top

ple regim

eA B C

D E F

G H I

Most im

portant

critical unknowns

Criticalunknown

XScenario category

Figure E.10: Example of Scenario hypotheses development using Axes

E.12 (B2) Gathering intelligence for mid/long term assessment

As can be seen in figure E.8, the Intelligence gathering process (B2) is providing Intelligenceon plausible hypotheses to the Analysis of competing hypothesis process (B3). Essentially thisprocess is the same as the intelligence gathering process in Near term assessment (A2). Thecrucial difference however is that this process is continuously monitoring usual indicators addedwith information the intelligence gatherer judges useful. Although the intelligence gatherer willtake into account the developed Plausible hypotheses, he will not only focus on these like inthe near term assessment process. There is only an explicit focus on specific hypotheses incase the Analysis of competing hypotheses cannot be conclusive and additional intelligence onthe Plausible hypotheses is required [H.7.2.6]. The gathering disciplines and classifications forintelligence are the same as the intelligence gathering process in Near term assessments (A2)explained in paragraph E.5.

E.13 (B3) Analysis of competing hypotheses

After Developing n number of plausible hypotheses (B1) there are generally about ten Plausiblehypotheses that are considered. However only for about three to five Plausible hypotheses arepolicies or capabilities developed [H.7.2.10]. So before the intelligence analyst can report to thepolicy maker what the likely future events will be, he has to make a selection. This selection takesinto account all available intelligence and compares the validity of each Plausible hypothesis.The process prescribed by NATO doctrine [77, p.101] also originates from Heuer, he determinedthe following eight steps for the Analysis of competing hypotheses[62, p.97]:

Step 1: “Identify the possible hypotheses to be considered. Use a group of analysts with dif-ferent perspectives to brainstorm the possibilities” (processes preceding the Analysis ofcompeting hypotheses process);

Step 2: “Make a list of significant evidence and arguments for and against each hypothesis”;

147

Step 3: “Prepare a matrix with hypotheses across the top and evidence down the side. Analyzethe diagnosticity of the evidence and argumentation that is, identify which items aremost helpful in judging the relative likelihood of the hypotheses” (an example of such amatrix is provided in table E.2);

Step 4: “Refine the matrix. Reconsider the hypotheses and delete evidence and arguments thathave no diagnostic value”;

Step 5: “Draw tentative conclusions about the relative likelihood of each hypothesis. Proceedby trying to disprove the hypotheses rather than prove them”;

Step 6: “Analyze how sensitive your conclusion is to a few critical items of evidence. Considerthe consequences for your analysis if that evidence were wrong, misleading, or subjectto a different interpretation”;

Step 7: “Report conclusions. Discuss the relative likelihood of all the hypotheses, not just themost likely one”; and

Step 8: “Identify milestones for future observation that may indicate events are taking a differentcourse than expected”.

The example matrix of Step 3, presented in table E.2, indicates that it can show that somehypotheses are more likely than others but that it will not always present one true hypothesis.Both hypothesis 1 and 3 can be true. Only hypothesis 2 seem to be rejected. All the judgementsmade to develop such a matrix and decide which hypotheses are valid, are executed by groupsof experts. The reasoning behind all these judgements, like every assumption, decision andtrade-off, should be documented and taken into account when reporting [H.7.2.5]. The formatof the report that follows is not standardized by NATO doctrine and depends on what the policymaker desires.

E.14 Verification of described intelligence processes

Since NATO nor any related intelligence services has been directly involved in this thesis ithas not been possible to really validate the results of this chapter. However we did verify withour source who provided us with the information on the intelligence processes, whether ourdescription above is accurate. The process is presented and explained to Dr. Giliam de Valk,who is an expert on intelligence methods and has a close academic relation with the DutchDefense intelligence services [H.5.1.1.1]. Based on the presentation he confirmed that the figuresE.1, E.2, E.3 and E.9 are an accurate description of the Strategic Geopolitical Intelligence processas executed by NATO state intelligence services [H.8].

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Table E.2: Example of an Analysis of competing hypotheses matrix

Plausible hypotheses:

Hypothesis 1Regime withdraws from Nuclear

non-proliferation treaty and develops nuclearweapon capability

Hypothesis 2Regime develops nuclear energy in line with

Nuclear non-proliferation treaty

Hypothesis 3

Regime will withdraw from Nuclearnon-proliferation treaty and uses nuclear warrhetoric without actually developing nuclear

weapon capability

Evidence / intelligence H1 H2 H3

Uranium enrichment facilitiesare in operation

++ - -

Regime bans UN inspectionfrom some facilities

++ - ++

Regime obtains weaponsystems that can be used as

nuclear weapon deliverysystems

++ - +

Regime makes publicstatements that it will

“punish” all foreigninvolvement with nuclear

weapons if necessary

++ - ++

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Appendix F

Value criteria explanation

Below are all the criteria explained. Since the criteria are intended to compare different analysismethods, we explained them in such a way that these criteria only reflect the analysis method.This aspect will become more clear in the explanations of the individual criteria below. Alsonote that many of the criteria are interdependent, hence the Iron triangle1. An overview of allthe explained value criteria in this appendix is presented below:

F.1 Operation costs of intelligence gathering . . . . . . . . . . . . . . . . 150

F.2 Failure costs of intelligence gathering . . . . . . . . . . . . . . . . . . 150

F.3 Duration of intelligence gathering . . . . . . . . . . . . . . . . . . . . 150

F.4 Required integrity of the gathered intelligence . . . . . . . . . . . . . 150

F.5 Operation costs of an analysis . . . . . . . . . . . . . . . . . . . . . . . 150

F.6 Failure costs of an analysis . . . . . . . . . . . . . . . . . . . . . . . . . 151

F.7 Duration of an analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

F.8 Internal validity of an analysis . . . . . . . . . . . . . . . . . . . . . . 151

F.9 External validity of an analysis . . . . . . . . . . . . . . . . . . . . . . 151

F.10 Reliability of an analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 151

F.11 Objectivity of an analysis . . . . . . . . . . . . . . . . . . . . . . . . . 151

F.12 Operation costs of reporting . . . . . . . . . . . . . . . . . . . . . . . . 152

F.13 Failure costs of reporting . . . . . . . . . . . . . . . . . . . . . . . . . . 152

F.14 Duration of reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

F.15 Relevance of a report to intelligence task . . . . . . . . . . . . . . . . 152

F.16 Credibility of a report . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

F.17 Transferability of a report . . . . . . . . . . . . . . . . . . . . . . . . . 152

F.18 Dependability of a report . . . . . . . . . . . . . . . . . . . . . . . . . 153

F.19 Confirmability of a report . . . . . . . . . . . . . . . . . . . . . . . . . 153

1This is another reason why it is hardly possible to use Multi criteria decision analyses to determine whichanalysis method is on overall better than another one [45, p.53-55].

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F.1 Operation costs of intelligence gathering

Operation costs of intelligence gathering are the costs to get sufficient intelligence in the rightformat that is required by the analysis method. For example analysis methods that require ahigh amount of non-public intelligence and/or is only able to handle e.g. numerical data setsprovided in some obscure file-format, do not score well on this criterion.

F.2 Failure costs of intelligence gathering

Failure costs of intelligence gathering is the total risk of investing money in gathering intelligencebut fail to find sufficient intelligence in the right format required by the analysis method. Forexample analysis methods that require specific types of intelligence that is generally not publiclyavailable, score not well on this criterion.

F.3 Duration of intelligence gathering

Duration of intelligence gathering is the time it takes to gather sufficient intelligence in the rightformat required by the analysis method. So for example analysis methods that require a highamount of intelligence that all needs to be transformed manually in a certain desired format, donot score well on this criterion.

F.4 Required integrity of the gathered intelligence

The integrity of the gathered intelligence is the level of trustworthiness of the intelligence requiredby the intelligence method. This level of integrity of gathered intelligence can make an importantdifference in making the right or wrong conclusion. However it depends on how sensitive theanalysis method is to the integrity of intelligence, on how significantly wrong the conclusionswill become. For example analysis methods that will yield completely wrong conclusions, oruseless conclusion, when the integrity of intelligence cannot be assured, do not score well on thiscriterion.

Note that there are two types of wrong conclusions; false positives and false negatives. Withinstatistics these wrong conclusions are called respectively Type-I and Type-II errors and withinintelligence these wrong conclusions are called respectively α-chances and β-chances [111, p.66-67].

F.5 Operation costs of an analysis

Operation costs of an analysis are the costs to process the intelligence into insights that arerequired for reporting. For example analysis methods that require expensive technologies orhighly specialized analysts, do not score well on this criterion.

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F.6 Failure costs of an analysis

Failure costs of an analysis is the total risk of investing money in an analysis that is not able toprovide useful results. For example analysis methods that has high operation costs and is verylikely to produce inconclusive conclusions, do not score well on this criterion.

F.7 Duration of an analysis

Duration of an analysis is the time it takes to process intelligence into insights required forreporting. For example analysis methods that are very extensive and require a lot of attentionto details, do not score well on this criterion.

F.8 Internal validity of an analysis

The internal validity of an analysis is the extend to how the analysis is executed and how itwas intended to be executed. So analysis methods that are very prone to making errors withoutnoticing them, do not score well on this criterion.

F.9 External validity of an analysis

The external validity of an analysis is the extend to how much the results of the analysis are inline with the real world. So analysis methods that study a real world system in such a way thatit does not reflect that real world system sufficiently to make accurate conclusions, do not scorewell on this criterion. For a discussion on the different Strategic Geopolitical Analysis methodsand how suitable they are for studying volatile states, we refer to the results on sub question 1in chapter 3.

F.10 Reliability of an analysis

The reliability of an analysis is the extend to which it is possible to replicate the same analysisand yield the same results. For example analysis methods that are unstructured, do not scorewell on this criterion.

F.11 Objectivity of an analysis

The objectivity of an analysis is the extend to which the bias or the unique perspective of theanalyst, is not reflected in the conclusions. For example analysis methods that depend to a greatextend on the insights and intuition of the analyst, do not score well on this criterion.

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F.12 Operation costs of reporting

Operation costs of reporting are the costs to present the conclusions of the analysis in such away that they are understandable to the policy maker who requested the report. For exampleanalysis methods that provide complicated results, only understandable to highly specializedanalysts, that need to be transformed into easy to read policy reports, do not score well on thiscriterion.

F.13 Failure costs of reporting

Failure costs of reporting is the total risk involved of the reader misunderstanding the reportwith negative consequences. For example analysis methods that are not able to provide under-standable reports do not score well on this criterion.

F.14 Duration of reporting

Duration of reporting is the time it takes to present the conclusions of the analysis in such a waythey are understandable to the policy maker who requested the report. For example analysismethods that provide complicated results and take a lot of time to transform into easy to readpolicy reports, do not score well on this criterion.

F.15 Relevance of a report to intelligence task

Relevance of a report to intelligence task is the extend to how much the conclusions of thereport are in line with the initial intelligence task, this includes the analytical accuracy (i.e.the extend wherein conclusive conclusions are drawn). With the intelligence task we mean theset of questions where the client / policy maker hopes to get answered. For example analysismethods that do not have a clear structure that follows from the intelligence task, allows theanalyst easier to provide answers to questions that were not asked and no answers to questionthat were asked. Such analysis methods do not score well on this criterion.

F.16 Credibility of a report

Credibility of a report is the extend to how much the policy maker trusts the findings providedby the analysis, this is independent from whether the findings are accurate or not. For exampleanalysis methods that do not have a clear structure on which can be documented and/or usetools that are unfamiliar to the average policy maker, do not score well on this criterion.

F.17 Transferability of a report

Transferability of a report is the extend to how useful the results on a certain case are alsoapplicable to another case. If interpreted in a less strict way, one can also think of whether the

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analysis method itself is easy to apply to many different types of cases or security problems. Soanalysis methods that are only useful for very specific situations that rarely occur, do not scorewell on this criterion.

Note though that within security domain, every problem can costs human lives and therefore hasto be assessed. So when the method is very rigid and can only used for an unique type of problemthat rarely occurs, the method is still extremely valuable when it is clearly the most suitable oneto assess that unique type of problem H.4.2.3.2.

F.18 Dependability of a report

Dependability of a report is the extend to which the conclusions of the report remain valid underchanging circumstances. For example analysis methods that base their conclusions on very littleevents and (thus) are very sensitive to the occurrence of other events, do not score well on thiscriterion.

F.19 Confirmability of a report

Confirmability of a report is the extend to which all the actions performed by the analyst canbe tracked based on the information provided in the report. For example analysis methods thatare unstructured and do not force the analyst to document all analysis decisions, do not scorewell on this criterion.

Note that the confirmability criterion strongly relates to the reliability criterion because when onewants to replicate an analysis it is very useful to know what actions the analyst has performedin the original analysis. However when all actions of an analyst can be tracked, so the analysiswas highly confirmable, it might still be impossible to replicate the analysis and yield the sameresults. This is caused because it is very hard to analyze the same things twice because thingschange constantly [51] and the same things are perceived differently by different analysts [78,p.43].

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Appendix G

Computational actions of the formalmodel

This chapter shows the computational actions of every agent per activity in line with the activitydiagram in figure 11.2 presented in chapter 11. These are some guidelines for reading throughthe computations:

• The letters i, j, k, l, a and the number 1 are used to separate different agents or objects inthe reasoning. Accross actions, these values do not have a meaning;

• All the computations are based on assumptions, which are explained throughout the doc-ument below. The assumptions are not per s chosen to be realistic but more to show thereader that soft reasoning can be translated into code;

• ψ is used as a constant float parameter with a domain from 0 to 1. It is often used asa weight factor to determine the strength of certain causal relations but also to indicatethresholds for certain actions. Every ψ has an different value, unless indicated by ψa;

• φ is used as a variable float parameter that can be described by the use in every probabilitydistribution desirable, with a domain from 0 to 1. Including probabilities is a way to coverdynamics that are not or can not be modelled.

• η is a float parameter with a domain from 0 to 1 that represents the input of the envi-ronment into the model. The user can choose for every environmental variable to make itconstant or variable. However because the environment can especially not be modeled, soit is advised to use variable values for this parameter like φ

• In order to simulate parallel actions it is sometimes important to store variables as currentor new. Current values are the values used in the active time step, new values are set tobe current values at the end of each time step.

An overview of all the computations is presented below:

G.1 Govern controlled regions . . . . . . . . . . . . . . . . . . . . . . . . . 155

G.2 Govern own mil. forces . . . . . . . . . . . . . . . . . . . . . . . . . . . 156

G.3 Govern terrorists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

G.4 Diplomacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

G.5 Terrorist attack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

155

G.6 Migration decision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162

G.7 Internal citizen dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . 162

G.8 Wage war with other mil. force . . . . . . . . . . . . . . . . . . . . . . 164

G.9 Manage security forces . . . . . . . . . . . . . . . . . . . . . . . . . . . 168

G.10 Request provisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170

G.11 Evaluate relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171

G.12 Desertion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171

G.13 Evaluate region attribute values . . . . . . . . . . . . . . . . . . . . . 172

G.14 Migration network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173

G.15 Migrating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173

G.16 End time step . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173

G.1 Govern controlled regions

The dynamics are based on the assumption that the socio-economic wealth in the regions undercontrol of a state i, has a positive causal relation to the level of socio-economic wealth of thatstate i and the other way around. The strengths of the causal relations vary dependent on aprobability distribution.

G.1.1 Determine change in socio-economic wealth of regions

• Calculate change of socio-economic wealth of regions controlled by state i

– Calculate the deviation of the current socio-economic wealth of state i with the norm(0.5)

G.1.2 Change socio-economic wealth in own region

• If “Region is controlled by state i”

– Then: Calculate new socio-economic wealth in region

∗ Add(

(change of socio-economic wealth of state i * φ) + η)

to current socio-

economic wealth in region

– Else: Go to next action

G.1.3 Update socio-economic wealth of own state

• Calculate change of Ssei (δSsei)

– Calculate the average Rse of all te regions under control

– Calculate the deviation of the calculated average with the norm (0.5)

• Calculate Ssei(t)

156

– Add(δSsei ∗ φ) + Φ

)to Ssei(t− 1)

G.2 Govern own mil. forces

The dynamics affecting the moral of a state i (Smi) and the moral of all mil. forces withallegiance to state i (Mmi) are assumed to be similar to the socio-economic dynamics betweena state and its controlled regions. The distribution of army size and resource allocation, areassumed to be done proportionally to the requests.

G.2.1 Update moral of own state

• Calculate change of moral of state i

– Calculate the average moral of all te mil. forces with allegiance to state i

– Calculate the deviation of the calculated average with the norm (0.5)

• Calculate new moral of state i

– Add(

(change of moral of state i * φ) + η)

to current moral of state

G.2.2 Determine change of own mil. force moral

• Calculate change of the moral of mil. forces that have an allegiance to state i

– Calculate the deviation of the current moral of state i with the norm (0.5)

G.2.3 Change moral

• If “Mil. force has allegiance to state i”

– Then: Calculate new mil. force moral

∗ Add(

(change of the moral of mil. forces that have an allegiance to state i * phi)

+ η)

to current mil. force moral


G.2.4 Determine army size allocation

• If “Sum of all desired army size by mil. forces with allegiance to state i < Current armysize of state i”

– Then: Relative fulfillment = 1 AND Create new mil. force agent with:

∗ Allegiance to state = State ID of state i

∗ Army size = (army size of state i - sum of all desired army size by mil. forceswith allegiance to state i)

157

∗ Desired army size = 0

∗ Engaged in war = False

∗ Mil. force ID = MAX(mil. force ID) + 1

∗ Moral = moral of state i

∗ Resources = Average resources of all mil. forces with allegiance to state i

∗ Security task = false

∗ Location = Region within Mali border is True and which is closest to the averagecoordinates of all mil. force with allegiance to state i and with security task False

∗ Also: Deduct average resources of all mil. forces with allegiance to state i, fromresources of state i

– Else: Calculate relative fulfillment of desired army size by state i

∗ Current army size of state i / Sum of all desired army size by mil. forces withallegiance to state i

• Set new army size of state i = η

G.2.5 Change army size


– Then: Calculate new mil. force army size

∗ Current army size of mil. force j * Relative fulfillment of desired army size bystate i


G.2.6 Determine resource allocation

• Calculate available resources for mil. forces

– If “Amount of terrorist cells with an allegiance to state i = 0”

∗ Then: Current resources of state i

∗ Else: Current resources of state i * (1 - Terrorist sponsor of state i)

• Calculate relative fulfillment of desired resources by state i

– Choose minimal value between 1 and(

Available resources for mil. forces of state i /

Sum of all desired sources by mil. forces with allegiance to state i)

• Set new resources of state i

– Choose maximum value between 0 and(

Available resources for mil. forces of state i

− Sum of all desired sources by mil. forces with allegiance to state i)

158

– Add η to maximum value

G.2.7 Change resources


– Then: Calculate new mil. force resources

∗ Current resources of mil. force j * Relative fulfillment of desired resources bystate i


G.3 Govern terrorists

It is assumed that every state can find a terrorist cell with the same cause as theirs, if theychoose to use terrorist cells. The resources that were made available for terrorist cells will bedistributed to the terrorist cell with the same cause (terrorist cell with an allegiance to state i).

G.3.1 Determine terrorist resources allocation

• Calculate available resources of state i for terrorist cell

– Current resources of state i - Available resources for mil. forces of state i + η


• If “Terrorist cell has allegiance to state i”

– Then: Set new terrorist cell resources to available resources of state i for terrorist cellAND go to Terrorist attack

– Else: Go to next terrorist cell

G.4 Diplomacy

It is assumed that two states will become allies based on the moral of a state, shared interestand the relationship level. The moral of a state influences whether a state feels he needs helpor has confidence it can act alone. Shared interests can either be the desire to have immediatepeace or to destroy one or more other states. The relationship level is affected by conflicts inthe past and the relationships of favored or hated cultures (the stronger the favor or hate, themore important that relationship is to the interstate relationship). Furthermore, according toa certain probability distribution, the interstate relationship will increase every time step bothstates are in an alliance

159

G.4.1 Update interstate relationships

• Determine changes in interstate relations

– Calculate new interstate relation

∗(

(1 - Incongruity of cultural relations * ψ) * (1 - Incongruity of interstate

relations * ψ ) * ψ)

+ (η - 0.5) + (Allied * φ)

· Incongruity of cultural relations =

(Abs

(( Relationship level state i and

culture 1 / ψa) - ( Relationship level state j and culture 1 / ψa))

+ Abs(

(

Relationship level state i and culture n / ψa) - ( Relationship level state j

and culture n / ψa)))

/ (Amount of state culture relationships / ψa)

· Incongruity of interstate relations =

(Abs

(( Relationship level state i and

state 1 / ψa) - ( Relationship level state j and state 1 / ψa))

+ Abs(

(

Relationship level state i and state n / ψa) - ( Relationship level state j and

state n / ψa)))

/ (Amount of state culture relationships / ψa)

• Determine alliances

– If “Moral of state i AND Moral of state j < ψ; AND relationship level between statei and k AND relationship level between state j and k < ψ (check for all possibleinterstate relationships); AND the minimum relationship level between state i or itsallies and state j or its allies > ψ”

∗ Then: Set allied to True (also for all other states in alliance)

∗ Else: “If allied True AND Moral of state i > ψ; AND(

relationship level between

state i and k AND relationship level between state j and k > ψ (check for allpossible interstate relationships); OR the minimum relationship level betweenstate i and state j or its allies < ψ”

· Then: Set allied to False (including to all allies in alliance)

· Else: Go to next action

G.5 Terrorist attack

It is assumed that terrorist cells choose randomly a region to attack, under the limitations thatthey will not choose regions that are under control by the state i to whom they have an allegianceto, or to regions under control by allies of state i. The success of an attack is assumed to bedependent of the security of the target region and the ratio of mil. force resources in the regionand the resources available to the terrorist cell. If the attack is successful then the impact onthe region and on the mil. force in that region depends on the amount of resources the terroristcell had available and a certain probability function.

160

G.5.1 Decide which region to attack

• Check availability of attack regions

– If “Region control by state i or by any of that state allies < ψ

∗ Then: Set availability for terrorist attack to True

∗ Else: Set availability for terrorist attack to False

– If “Amount of regions with value True for availability for terrorist attack = 0”

∗ Then: Go to migration decision

∗ Else: Randomly pick region with availability for terrorist set to True AND setavailability for terrorist attack of all other regions to False

G.5.2 Determine success of attack

• If “Available for terrorist attack of region i = True”

– Then: Determine success of attack

∗ Success probability =(

Current terrorist resources / total resources of mil. forces

that have not the same allegiance to state i or to one of the state allies)

*(

1 -

Security of target region)

* ψ

∗ If “Success probability ≥ φ”

· Then: Go to next action

· Else: Set available for terrorist attack for all regions to False AND go tomigration decision

– Else: Go to next region

G.5.3 Determine effect of attack to mil. force army size in region

• Calculate loss of army size of mil. forces in attack region that have not the same allegianceto state i or to one of the state allies

– Terrorist resources * φ


• If “The region where mil. force j is in has available for terrorist attack = True AND

Allegiance to state of terrorist cell 6=(

Allegiance to state of mil. force OR any of its state

allies)

”

– Then: Calculate new mil. force army size

∗ New army size of mil. force j - loss of army size of mil. forces in attack region

161

– Else: Go to next mil. force

G.5.5 Determine effect of attack to mil. force resources in region

• Calculate loss of resources of mil. forces in attack region that have not the same allegianceto state or to one of the state allies



• If “The region where the mil. force is in has available for terrorist attack = True AND

Allegiance to state of terrorist cell 6=(

Allegiance to state of mil. force OR any of its state

allies)

”

– Then: Calculate new resources of mil. force j

∗ New resources of mil. force j - loss of resources of mil. forces in attack region

– Else: Go to next mil. force

G.5.7 Determine effect of attack to socio-economic wealth of region

• Calculate loss of socio-economic wealth in attack region


G.5.8 Change socio-economic wealth

• If “Available for terrorist attack in region = True”

– Then: Calculate new socio-economic wealth of attack region AND go to next action

∗ Current socio-economic wealth - loss of socio-economic wealth in attack region


G.5.9 Determine effect of attack to security of region

• Calculate loss of security in attack region


G.5.10 Change security

• If “Available for terrorist attack in region = True”

– Then: Calculate new security in region AND go to next action

162

∗ Current security - loss of security in attack region


G.6 Migration decision

It assumed that depending on the attraction level of a certain region, a citizen chooses accordingto a certain probability distribution to migrate or not.

G.6.1 Update migration desire

• If “attraction level in region < ψ”

– Then: determine whether citizen wants to migrate

∗ If “φ > ψ

· Then: Set migration desire = True

· Else: Go to next citizen

– Else: Go to next citizen

G.7 Internal citizen dynamics

The citizen culture relation is assumed to be increasing depending on its socio-economic wealth,however it deteriorates due to negative difference of socio-economic wealth with that culture intheir region as well as a negative difference in the favored citizen state relationship. All onlydepending on other citizens of cultures in the same region. Furthermore it is assumed thatthe relation between citizen and state is affected when the state controls/governs the region ofthe citizen. This relationship increases or decreases depending on difference with the norm ofthe security perception of the citizen and the socio-economic wealth of the citizen. Finally arethe security perception and socio-economic wealth of the citizen increasing or decreasing to thesecurity and the socio-economic wealth of the region. The speed of change depends on howstrong the positive relationship between state and the citizen his culture is. If this relationshipis negative then the citizen his security perception and socio-economic wealth will decreasedepending on a certain probability distribution.

G.7.1 Update relations to other cultures

• Calculate new citizen a culture b relationship

– Current citizen a culture b relationship + (current socio-economic wealth of citizen a* ψ) - (negative difference of socio-economic wealth between citizen a and culture bin region * ψ) - (negative difference in the favored citizen state relationship betweencitizen a and culture b in region) + η

∗ Calculate negative difference of socio-economic wealth between citizen and cul-ture in region

163

· Maximum value between(

0 and (Average socio-economic wealth of citizens

of culture b in region - socio-economic wealth of citizen))

∗ Calculate negative difference in the favored citizen state relationship betweencitizen a and culture b in region

· Maximum value between(

0 and (Average citizen state relationship between

citizens of culture b and state (that has the highest state citizen relation

value for citizen a - the highest state citizen relation value for citizen a))

G.7.2 Update relations to states

• Determine state i that controls the region where citizen is in

– If “Maximum state region control value > ψ”

∗ Then: State with maximum state region control value is state i who controls theregion

∗ Else: No owner AND continue to update security perception

– Calculate new state citizen relation of region owner

∗ Current state i citizen a relation * (current security perception of citizen a / 0.5)* (socio-economic wealth of citizen a / 0.5) + η

G.7.3 Update security perception

• If “Maximum state region control value > ψ”

– Then: If “average mil. force culture relation of mil. forces in region with culture ofcitizen a > ψ

∗ Then: Calculate new security perception of citizen a

· Current security perception of citizen a +(

maximum value between (0 and

security in region - security perception of citizen a) * state citizen relation of

state in control and citizen b * ψ)

-(

minimum value between (0 and security

in region - security perception of citizen a) * (1 - state citizen relation of state

in control and citizen b) * ψ)

∗ Else: Calculate new security perception of citizen a

· Current security perception of citizen a - (1 - state citizen relation of state incontrol and citizen b * φ)

– Else: Calculate new security perception of citizen a

∗ Current security perception - φ

164

G.7.4 Update socio-economic wealth

• If “Maximum state region control value > ψ”

– Then: If “average mil. force culture relation of mil. forces in region with culture ofcitizen a > ψ

∗ Then: Calculate new socio-economic wealth of citizen a

· Current socio-economic wealth of citizen a +(

maximum value between (0

and socio-economic wealth in region - socio-economic wealth of citizen a) *

state citizen relation of state in control and citizen b * ψ)

-(

minimum value

between (0 and socio-economic wealth in region - socio-economic wealth of

citizen a) * (1 - state citizen relation of state in control and citizen b) * ψ)

∗ Else: Calculate new socio-economic wealth of citizen a

· Current socio-economic wealth of citizen a - (1 - state citizen relation of statein control and citizen b * φ)

– Else: Calculate new socio-economic wealth of citizen a

∗ Current security perception - φ

G.8 Wage war with other mil. force

G.8.1 [Decision block] Is mil. force providing support?

• If “Mil. force providing support to any mil. force = True”

– Then: Go to request provisions


G.8.2 [Decision block] Is mil. force security tasked?

• If “Mil. force security tasked = True”

– Then: Go to manage security forces


G.8.3 [Decision block] Is mil. force engaged in war?

• If “Mil. force engaged in war = True”

– Then: Go to update war progress


165

G.8.4 Update hostile armies in range

• If “Mil. force k is within vision ≤ ψ of mil. force j; AND allegiance to state of mil. forcek 6= allegiance to state of mil. force j or any of it state allies; AND mil. force is in regionwith within Mali border = True; AND mil. force state relationship between mil. force jand state to whom mil. force k is allied to < ψ”

– Then: hostile interforce relation between mil. forces j and k = True

– Else: hostile interforce relation between mil. forces j and k = False

G.8.5 [Decision block] Are hostile armies too strong?

• If “(

Current army size of mil. force j + (sum of current army sizes of all allied mil. forces

within range ≤ ψ that have all providing support and security tasked set to False))

* (ψ

/ 0.5) - (sum of current army sizes of all hostile mil. forces within range ≤ ψ) > ψ

– Then: Go to manage security forces


G.8.6 Update war progress

• Determine power difference

–(

Accessibility to mil. force j of region where mil. force k is * (ψ / 0.5))

*(

Army

size ratio * (ψ / 0.5))

*(

Moral ratio * (ψ / 0.5))

*(

Resource ratio * (ψ / 0.5))

∗ Calculate army size ratio

·(

Current army size of mil. force j + (sum of current army sizes of all allied

mil. forces within range ≤ ψ that have security tasked set to False AND (all

providing support = False OR providing support to mil. force j = True)))

/

(sum of current army sizes of all hostile mil. forces within range ≤ ψ)

∗ Calculate moral ratio

·(

Average current moral weighted over the current army sizes of mil. force

j and all allied mil. forces within range ≤ ψ that have security tasked setto False AND (all providing support = False OR providing support to mil.

force j = True)))

/ (Average current moral weighted over the current army

sizes of all hostile mil. forces within range ≤ ψ)

∗ Calculate resources ratio

·(

Current resources of mil. force j + (sum of current resources of all allied

mil. forces within range ≤ ψ that have security tasked set to False AND (all

providing support = False OR providing support to mil. force j = True)))

/

(sum of current resources of all hostile mil. forces within range ≤ ψ)

166

• Determine result of attack round

– If “Abs(

Power difference * (φ / 0.5))> ψ

∗ Then: Set war engagement status to False AND “If power difference > 1

· Then: Attacker goes to target region AND sets its control in region to ψAND defender withdraws; AND set previous governor of region to allegianceof defending mil. force

· Else: Defender sets control in region to ψ AND attacker withdraws

· Withdraw by merging with nearest allied mil. force (add army size andresources to nearest allied mil. force; AND set moral of nearest allied mil.force to average moral weighted over army size; AND remove losing mil.force) If no allied mil. force exist then move agent as far away within visionψ from any other mil. forces.

∗ Else: Set war engagement status to True AND set all controls in region to mini-mum value between (ψ and state region control)

G.8.7 Update providing support status

• Providing support to attacker

– If “(Mil. force l is within range ≤ ψ of mil. force j; AND Mil. force l is allied to mil.force j; AND mil. force j has war engagement state = True; AND mil. force l hassecurity tasked = False; AND (mil. force l has providing support to any mil. forceleq mil. force j) = False”

∗ Then: Set providing support by mil. force l to mil. force j = True

∗ Then: Set providing support by mil. force l to mil. force j = False

• Providing support to defender

– If “(Mil. force l is within range ≤ ψ of mil. force j; AND Mil. force l is allied to mil.force j; AND mil. force j has war engagement state = True; AND mil. force l hassecurity tasked = False; AND (mil. force l has providing support to any mil. forceleq mil. force j) = False”

∗ Then: Set providing support by mil. force l to mil. force j = True

∗ Then: Set providing support by mil. force l to mil. force j = False

G.8.8 Update army sizes based on war progress

• If “power difference” > 1

– Then: Calculate new army size of attacking army

∗ New army size of attacking army -(

(2 - power difference) * φ)

– Calculate new army size of defending army

167

∗ New army size of defending army +(

(power difference - 1) * φ)

• Else: Calculate new army size of attacking army

– New army size of attacking army -(


• Calculate new army size of defending army

– New army size of defending army -(

(power difference) * φ)

G.8.9 Update mil. force resources based on war progress


– Then: Calculate new resources of attacking army

∗ New resources of attacking army -(


– Calculate new resources of defending army

∗ New resources of defending army +(


• Else: Calculate new resources of attacking army

– New resources of attacking army -(


• Calculate new resources of defending army

– New resources of defending army -(


G.8.10 Update mil. force moral based on war progress


– Then: Calculate new moral of attacking army

∗ New moral of attacking army -(


– Calculate new moral of defending army

∗ New moral of defending army +(


• Else: Calculate new moral of attacking army

– New moral of attacking army -(


• Calculate new moral of defending army

– New moral of defending army -(


168

G.8.11 Determine effect of war to interstate relations

• Calculate decrease in interstate relation between state of attacker and state of defender

– Abs(Power difference - 1) * φ + ψ

G.8.12 Change interstate relations

• Calculate new interstate relation between state of attacker and state of defender

– Deduct decrease in interstate relation from new interstate relation between state ofattacker and state of defender

G.8.13 Determine effect of war to socio-economic wealth in region

• Calculate decrease in socio-economic wealth in region of war

– φ

G.8.14 Change socio-economic wealth

• Calculate new socio-economic wealth in region of war

– Deduct decrease in socio-economic wealth in region of war from new socio-economicwealth in region

G.8.15 Determine effect of war to security in region

• Calculate decrease in security in region of war

– φ

G.8.16 Change security

• Calculate new security in region of war

– Deduct decrease in security in region of war from new security in region

G.9 Manage security forces

It is assumed that there is some norm value of amount of citizens in a region. The more citizensneed to be secured in a region, the higher the desired security. The desired security is assumedto be lower when the socio-economic wealth in a region is high, which will give less inter citizentensions.

169

G.9.1 Update desired security

• Set current desired security = Amount of citizens in region * ψ* 0.5 / socio-economicwealth

G.9.2 [Decision block] Is mil. force in war OR provides support?

• If “Mil. force in region has engaged in war = True; OR mil. force in region with providessupport to any other mil. force = True”

– Then: Go to request provisions


G.9.3 [Decision block] Is mil. force security tasked?

• If “Mil. force security task in region = True”

– Then: Determine excessive army size for allocation to state

– Else: Go to is army size ≤ desired security?

G.9.4 Determine excessive army size for allocation to state

• Calculate excessive army size of mil. force j

– Maximum value between 0 and (New army size of mil. force in region - current desiredsecurity in region)


• If “Mil. force j has allegiance to state i”

– Then: Calculate new state army size AND go to request provisions

∗ New army size of state i + excessive army size of mil. force j

– Else: Check allegiance with other state

G.9.6 [Decision block] Is army size ≤ desired security?

• If “Army size of all allied mil. forces in region AND security task = True ≤ desiredsecurity”

– Then: Go to update security task

– Else: Go to create new mil. force with security task

170

G.9.7 Update security task

• If “Another mil. force in region has security task = True”

– Then:

∗ Add new army size and resources of mil. force j to security tasked mil. force

∗ Set new moral of security tasked mil. force to average moral weighted over armysize

∗ Remove mil. force j

∗ Go to request provisions

– Else: Set security task of mil. force j = True; AND go to request provisions

G.9.8 Create new mil. force with security task

• If “Another mil. force in region has security task = True”

– Then:

∗ Move new army size of mil. force j + security tasked mil. force - desired security,to security tasked mil. force in region

∗ Set new moral of security tasked mil. force to average moral weighted over armysize

∗ Move (new army size of mil. force j + security tasked mil. force - desired securityto security tasked mil. force in region) * resources of mil. force j / desired security,to security tasked mil. force

– Else: Create new mil. force k with same values besides

∗ New army size of mil. force k = New army size of mil. force j - desired security

∗ New army size of mil. force j = Desired security

∗ New resources of mil. force k = (Army size of mil. force k / desired security) *new resources of mil. force j

∗ New resources of mil. force j =(

(1 - Army size of mil. force k) / desired security)* new resources of mil. force j

∗ Security task of mil. force j = True

G.10 Request provisions

G.10.1 Update desired army size

• If “Security task = True”

171

– Then: Maximum between 0 and (desired security in region - army size of mil. forcej))

– Else: Maximum between 0 and (total hostile army size within vision ≤ ψ - total alliedarmy size within vision ≤ ψ)

G.10.2 Update desired resources

• Determine desired resources

– Maximum value between 0 and (resources of mil. force j - average resources of allmil. forces in Mali)

G.11 Evaluate relations

G.11.1 Update relations to cultures

• New culture citizen relationship between culture 1 and citizen a

– Current culture citizen relationship between culture 1 and citizen a * (Culture staterelation of culture 1 and owner state of mil. force j * ψ / 0.5) * (1 - Culture staterelation of culture a and state that was previous governor of region * ψ / 0.5)

G.11.2 Update relations to states

• New mil. force state relation

– Current mil. force state relation * (Delivered resources * ψ / 0.5 / Requested re-sources) * (Delivered army size * ψ / 0.5 / requested resources) * (states total armysize * ψ / 0.5 / avg. army size of world) * (moral of mil. force j * ψ / 0.5 / avg.moral of all mil. forces)

G.12 Desertion

G.12.1 Update allegiance

• If “mil. force moral < ψ OR mil. force relation with own state < ψ”

– Then: Move all army size and resources to state with maximum(

(Moral of state *

ψ) + (army size of all mil. forces with allegiance to state * ψ) + (resources of all mil.forces with allegiance to state * ψ); AND destroy agent

• Else: If “Mil. force state relation to own state + ψ < Maximum mil. force state relationof mil. force j”

– Then: Decrease mil. force state relation to own state by φ; AND Change allegianceto state with maximum mil. force state relation

172

– Else: Go to evaluate region attribute values

G.13 Evaluate region attribute values

G.13.1 Update socio-economic wealth

• Calculate new socio-economic wealth in region

– New socio-economic wealth in region + (Current socio-economic wealth in region *average socio-economic wealth in region within vision ≤ ψ / 0.5) - Current socio-economic wealth in region + η

G.13.2 Update security

• Calculate new security in region

– New security in region +(

(Current security in region * ((1- (amount of new citizens

/ amount of citizens in region)) * ψ / 0.5) * ((1- (amount of parted citizens / amountof citizens in region)) * ψ / 0.5) * (MAX(state control) * ψ / 0.5) * (army size of

mil. force in region / desired security)* ψ / 0.5)

- Current security in region + η

G.13.3 Update accessibility

• If “Maximum control in region > ψ”

– Then: Calculate new accessibility between region and state that controls region

∗ Current accessibility * army size of security tasked mil. force in region / desiredsecurity in region * ψ / 0.5 + η

– Then: Calculate new accessibility between region and culture a

∗ Current accessibility * army size of security tasked mil. force in region / desiredsecurity in region * state culture relation between state that controls region andculture a * ψ / 0.5 + η

– Else: Go to update attractiveness

G.13.4 Update attractiveness

• Calculate new attractiveness of region to culture a

– Current attractiveness of region to culture a * Avg. socio-economic wealth in region* Avg. security perception in region/ Avg. social-economic wealth in world / Avg.security perception in world * control in region / avg. control in region * ψ + η

173

G.13.5 Update control

• Calculate new control of region

– Current control of region * army size in region / desired security * current control ofregion * ψ / 0.5

G.14 Migration network

G.14.1 Update edges in migration network

• Calculate value of edges in migration network for culture a

– Link all regions within range of region into network

– Value of edge = MAX(avg. control of state between both nodes) * accessibility toculture

G.15 Migrating

G.15.1 [Decision block] Migration desire?

• If “citizen has migration desire = True”

– Then: Go to direct migration


G.15.2 Direct migration

• If “Maximum value of (attraction level of region * edge1 * edge n to get there) attractionlevel current region * ψ) < ψ

– Then: Don’t move citizen

– Else: Move to MAX(attraction level of region * edge 1 * edge n to get there) via edge1; AND deduct socio-economic wealth of citizen by φ

G.16 End time step

• Set all new values to current

• Normalize all relation values1 in such a way the sum of all relation levels for a certainvalue equals exactly 1.

• Set every parameter float value ¡ 0 to 0; AND every parameter float value > 1 to 1

1relation values are all the values of attributes that are not part of an object or an agent, but as indicated inthe UML class diagram in figure 11.1 in relation classes.

174

Appendix H

Interview reports

H.1 Interview reports structure

All the interview reports presented in the rest of the appendix follow a consistent structure.Every report shows at the top an overview of the name(s) of the interviewee(s), date andlocation of interview, goal of interview and the applied interview method(s). Then a profileper interviewee is provided followed an explanation based on that profile of why and how weselected the interviewee. Then the report of the interview itself is presented and structured inparagraphs per goal of that interview. A final paragraph on general notions is always added tonote relevant findings for the study which are not in line with any of the interview goals. Allthe paragraphs are split up in sub-paragraphs as much as possible per topic, to make it easier inthe main report to refer exactly to the specific interview results. Finally to be transparent andmake this study confirmable, we provided all the available raw data on the interview as well.This is split up into the documents or presentation hand-outs that indicate the questions asked,and into the written or typed notes of that interview. An overview of all the interviews in thisappendix is presented below:

H.1 Interview reports structure . . . . . . . . . . . . . . . . . . . . . . . . 174

H.2 Interview with US Col. Frederic Borch III (Ret.) . . . . . . . . . . . 175

H.3 Interview with Dr. Niels van Willigen . . . . . . . . . . . . . . . . . . 180

H.4 Interview with Prof.dr. Rudesindo Nunez Queija& NL Capt. Onno Goldbach . . . . . . . . . . . . . . . . . . . . . 195

H.5 Interview with Dr. Giliam de Valk and Mr.drs. Willemijn Aerdtspt.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209

H.6 Interview with Dr. Giliam de Valk pt.2 . . . . . . . . . . . . . . . . . 214



175

H.2 Interview with US Col. Frederic Borch III (Ret.)

Date:18 December 2012

Location:Campus The Hague - Centre for Terrorism and Counterterrorism

Goals:Explore relevant concepts of state stabilityExplore possible cases for case studyExplore US vs NL perspectives on foreign affairs

Interview method: Semi-structured Interview

H.2.1 Background of interviewee

H.2.1.1 Profile

Fred Borch was a Visiting Professor and Fulbright Scholar from September-December 2012 atthe Centre for Terrorism and Counter-terrorism (CTC) at Campus The Hague of the LeidenUniversity. He taught ”Crime and Terrorism: The American Perspective” in the CTC curricu-lum. Mr. Borch has two degrees in history (B.A (Davidson College (USA)) and M.A. (Universityof Virginia (USA)). He also has an M.A. in National Security Studies (U.S. Naval War College(USA). Mr. Borch also is a Juris Doctor (University of North Carolina (USA)) and has two otherlaw degrees (LL.M, International and Comparative Law, Vrije Universiteit Brussel (Belgium)& LLM, Military Law, The Judge Advocate General’s School (USA). His area of expertise iscriminal law and international law relating to terrorism. While serving as a career U.S. Armyofficer, Mr. Borch was the first Chief Prosecutor for the military tribunals at Guantanamo Bay,Cuba from 2003 to 2004. Currently he is active as a Regimental Historian & Archivist at USArmy.

This profile is adopted from the Leiden University’s website [110] with a few additions from hisLinkedIn [13] and some adaptions to correct for spelling errors and explain abbreviations.

H.2.1.2 Reason for selection

In this phase of exploring the general concept of state stability and possible cases, it was preferredto speak with an expert who has a wide experience with different types of conflict situations(state instabilities) in a wide range of states. Although his expertise is in international law,his long experience in the United States army as an international attorney and RegimentalHistorian & Archivist made Fred Borch suitable to discover dynamics affecting state stability indifferent types of states. In addition to this, he is a useful source to explore different perspectivesin the international security arena. Not only because his course ”Crime and Terrorism: TheAmerican Perspective” but also because he is an United States national with academic expertiseand professional experience in international relations.

176

H.2.1.3 How the interviewee was identified

In our search for an expert with the relevant experience discussed above we expected to findsomeone at or via a university that lectures in international security. Via Dr. Coen van Gulijk,the first supervisor of this study, we discovered that Fred Borch was a visiting Professor at theLeiden University.

H.2.2 Report on the interview

H.2.2.1 Explore relevant concepts of state stability

It seems that the distribution of the wealth in relation to the type of governing regime has acrucial role in the stability of a state. Whenever the middle class becomes poorer, and thereforethe lower classes increases in size, they will revolt in a democratic regime. They will demandnew governance who is better able to provide them all with an increased wealth. However aslong as the middle class is significant in size and wealthy they will not revolt in a democraticregime, because they believe that their desires for freedom is fulfilled through voting and otherdemocratic institutions.

On the other hand there within autocratic regimes the opposite happens. Whenever the middleclass becomes poorer, and therefore the lower classes increases in size, the populace is easy tocontrol by autocratic policies. One doesn’t care about freedom of speech as long as they haveto worry about getting food and their own security. It is in these kind of nations when wealthincreases, that the middle class increases in size who are getting higher educated and gettingmore aware of the other freedoms, like freedom of speech and therefore start to revolt.

For these decision rules of the populace one can assume that everyone has the same desires,think of the four freedoms of Roosevelt or the Maslow pyramid. Although the priorities differper culture per individual, some of the freedoms can only be desired if other ones are fulfilled(hungry people don’t care as much about freedom of speech). Furthermore the general politicalview of the populace generally strongly depends on what party (political ideology) provides thatindividual with the best job stability, income stability or income growth.

H.2.2.2 Explore possible cases for case study

Based on the above generic behaviors it would be interesting to study the following cases forthe following reasons:

H.2.2.2.1 Argentina South America is generally off-radar but it is a very interesting con-tinent to study, since dynamics are so diverse but still so similar. It can be very educational.Argentina is a democracy and currently has a 25% inflation, being an export driven country,which destroys the middle class and seems to head for another bankruptcy. The government iseven reverting to dictatorial practices by jailing economists. This reminds to the terrible thehistory this nation already had to deal with; terrorism and death squads. One can fear that thegovernment falls back in his own pattern. Plus that old feuds has never been solved but justcovered by the wealth Argentina suddenly had, but which seems to disappear again.

177

H.2.2.2.2 Chile Similar to Argentina, Chile has a dictatorial past but is currently a democ-racy. Within Chile the middle class is also shrinking but there is no inflation (yet), probablydue to the copper. This shows the importance of resources in nations like these.

H.2.2.2.3 China & Russia Both nations are practically autocratic, communistic regimes.Where the middle class is increasingly wealthy and increasingly desires for more secondaryfreedoms.

H.2.2.2.4 India The middle class is increasing in wealth and the nation is a democracy, sofollowing the hypothesis this state should be stable. But besides the validity of the hypothesis,one can question how democratic India is, how wealth is really distributed and whether theeffects of over population wont easily burst the overall increase of wealth.

H.2.2.2.5 Arab spring: Syria - Turkey Not sure whether Syria is interesting to studysince it is already in revolt and currently in a stalemate. It is also not that interesting to thewest, besides that the killings itself should stop. The west is only concerned for spill overs toTurkey or Iraq. Turkey on the other hand is very important to the west, being a NATO allyand a neighbor to the EU. Plus that within this secular nation the role of religion (Islam) isincreasing. Note that the Arab spring started as a secular revolt but in the end that the Islamicsare plucking the fruits, with for example the Muslim brotherhood taking over Egypt.

H.2.2.2.6 Iran This case is very interesting since the western nations would like to see arevolt here against the Ayatollah and the government. In this autocratic nation one should thinkthat the west should support wealth development of the middle class. But what happens; thewests economically blocks Iran, which seems counterproductive if the hypothesis we determinedin this interview is true.

H.2.2.3 Explore US vs NL perspectives on foreign affairs

An important difference between the two nations (or between the US an Europe in general) isthat US citizens demand 100% security from their government, and that in Europe a certainrisk of insecurity is accepted. This is a logical causation from the fact that the US hardly everhad to fight inter-state war on his own land, being secured by two oceans. This is also why 9/11had such a major impact in the US.

Another important difference in security perception is due to the fact that the US is a worldhegemon when it comes to military strength and that states in Europe are very dependent onother nations (including the US). This means that the smaller of these European nations, likethe Netherlands, have fairly little to say in world affairs and have to be happy with “a seat atthe table”. However the US cannot afford not to listen to an ally like the Netherlands whenthey see problems rise, or can indicate that certain policy options should be taken.

H.2.2.4 General notions for study

It might also be interesting to focus on old cases and re-simulate the revolution and makequalitative reflections on when the model can be used or not. Ideas for suitable “old” cases are;

178

Iran (1979), South Africa (Apartheid) and the fall of the USSR.

H.2.3 Raw data interview

H.2.3.1 Agenda

Meeting Fred Borch – 18-12-2012

Introduction to current state of study:

• Research question:

o Can the qualitative geo-political risk assessments supported/replaced by Agent-Based

modeling?

o Explanation Complexity

o Arguments of limits of human brain and computers.

• How I was thinking to model

• Example of ABM in NetLogo.

Questions:

• Difference between the views on International Security of US and NL?

o US is already developing this methodology but we aren’t.

• Any idea what questions generally are asked to institutes like Control Risk, RAND, etc.?

o Predict event

o Stability (what is stability and instability?)

• Pre-requisites of case:

o Interesting for possible cooperating institute, so Dutch interest in case?

o Public information to deal with confidentiality.

o Need to comprehend complexity

o Was thinking of:

� Syria

� Afghanistan

o Was wondering of:

� Pakistan

� Iran

� Jordan

• Suggestions to contact others within Leiden University?

Goals:

• Exploring the concepts of state stability

• Exploring possible cases for case study

• Explore US vs NL perspectives on foreign affairs

Figure H.1: Agenda of interview with US Col. Frederic Borch III (Ret.)

179

H.2.3.2 Typed notes

Meeting with Fred Borch – 18-12-2012 – Case exploration

Important dynamics

• Stability of societies depend on the wealth of the middle class.

o Poor middle class revolts to democratic regime.

o Rich middle class revolts to autocratic regime to desire freedoms.

� Some say even the $10.000 - $12.000 per capita income per year is the threshold

of a society to revolt.

o The diamond Wealth of middleclass vs Institutional design of nation.

• Assumption: Everyone is the same in their desires:

o The four freedoms of Roosevelt

� Free of wants (no hunger)

� Free of fear (be safe ánd feel safe)

� Freedom of religion

� Freedom of speech

o Also consider the Maslow pyramid

o The priorities differ per culture / individual, and some can only be desired if others are

fulfilled. Hungry people don’t care about freedom of speech.

• Political views strongly depend on wealth, voting for the party that provides the best job stability

/ income stability or growth.

NL vs US

o Views towards international security

� US: Citizens demand 100% security.

� NL and Europe: Accepts a certain risk of insecurity but is extremely dependent of

other nations.

o Roles in international security

� US: Most dominant (western) actor in influencing world affairs.

� NL: Is happy with a seat at the table, but the US does listens to allies.

• Revolutions come when there are rising expectations – think of the spill overs in the arab spring.

• Autocratic nations do well with poor people, autocratic regimes will get punished when

oppressing (the richer becoming middle-class) by economic blockades. So what happens in Iran

is counterproductive, if one can prove the validity of “the diamond”.

Cases

• Argentina

o South America off-radar

o 25% inflation / year � destroys middle class

o Jailing economists

o Seems to head for another bankruptcy

Figure H.2: Typed notes of interview with US Col. Frederic Borch III (Ret.)

180

H.3 Interview with Dr. Niels van Willigen

Date:29 January 2013

Location:Leiden University - Faculty of Social Sciences

Goals:Explore traditional state stability analysesExplore relevant concepts in conflict areas


H.3.1 Background of interviewee

H.3.1.1 Profile

Dr. Niels van Willigen is associate professor international relations. He graduated with a historydegree in 2001 at the University of Leiden. After graduation he took part in a NATO researchprogram where he studied European security- and defence policy. After 2002 he was a Phdstudent at, and since 2007 he teaches at the Institute of Political Science of the University Leiden.In 2009 he received his Doctors degree by his dissertation on international policy in Bosnia &Herzegovina and Kosovo. His research interests are: theories of international relations, conflict-and security studies and international law. Among others he lectures on international politicsand MA-courses on international weapons control and state building.

This profile is our translation of the Dutch profile on the Leiden University’s website [109].


The general expertise of Dr. Niels van Willigen on conflict- and security studies made himan interesting source to discuss traditional state stability analyses. His experience in lecturingin international weapons control and state building, made him a useful source to explore thedynamics of the post-war state stability we analyzed for our case.


In our first sketches of our model of Mali to explore possible dynamics we thought of the dynamicwherein a region has phases of war, state building and autonomy. When searching through theprospectuses of the universities for identifying interviewees for their knowledge on intelligencemethods (see sections H.4.1.3 and H.5.1.3), we discovered that in 2009-2010 the university ofLeiden provided a course called War, Peace and Statebuilding taught by Dr. Niels van Willigen[108]. An academic lecturing a course on (almost) the same dynamic we had identified, directlydrew our attention.

181


Based on the two goals of this interviews, we have separated our findings in two paragraphs. Athird paragraph on general notions for study is added to report on the insights we have gatheredrelevant for our study but outside the scope of our intended goals.

H.3.2.1 Explore traditional state stability analyses

During this discussion we have talked about the different types of state stability analyses one candistinguish and on the state stability analysis process itself. Below in the first two paragraphswe present the two types of analyses and in the final two paragraphs we will report respectivelyon the information gathering process and the assessment process.

H.3.2.1.1 Objective research studies Generally this is the work for academia, where thephenomena relating to state stability itself are studied. Such studies are often comparative casestudies of a few or a wide range of cases analyzed using respectively qualitative or quantitative(statistics) methods. Sometimes there is also objective research on a single case, like the one westudied, where policies are evaluated. However these studies hardly can refrain from providingsome normative conclusions. The main difference such research provides compared with theoperational studies, discussed below, is that it evaluates policy options with more available timeand disconnected from political influences. This makes these evaluations more objective.

H.3.2.1.2 Normative operational studies These kind of studies are generally executedby institutions that advices public and/or private policy makers directly on their policies. Suchadvices are normative because it assumes the perspective of the policy maker and advices thepolicy maker in what option is best for the policy maker itself (or according to its norms).Furthermore such studies are operational because they are case specific and part of the decisionmaking process, which makes such studies also time constrained. Policy makers are especiallykeen on such studies if the products are; possible future lines of events or what-if policy optionevaluations.

H.3.2.1.3 Information gathering The types of information gathered/desired are stronglydependent on the goal of the study, however usually it includes demography, interests of actorsand considered policy options of all actors. Sources for such information are mainly commonliterature, surveys and interviews. Depending on the institute executing the study.

H.3.2.1.4 Assessment process There are different types of study paradigms that resultin different assessment methods. A dominant paradigm / tradition is the Positivism traditionwhich applies statistical methods, case studies for process tracing and it assumes that there areequilibria / true stability. On the other hand there is there are the critical security studies,originating from post-modern science, which doesn’t assume that there can be a true stabilitybecause there are always underlying dynamics at work. An example of such a dynamic is theinfluence of gender perspective. Generally one could say that such research is mainly societalcritique but doesn’t really provides tools to support decision makers. The tools they do provideare for example discourse analysis, where they try to identify some of the underlying dynamics.

182

H.3.2.2 Explore relevant concepts in conflict areas

After presenting the phases and dynamic classes as can be seen in H.3.3.1, he identified thefollowing dynamics as part of the dynamic classes; Every phase consists out of a ruler thatowns a certain area and tries it to make it autonomous through carrots and sticks. Autonomyis when the local populations becomes loyal to the ruler, or at least cooperates. Some peopleare voluntary loyal, others are convinced to be so through sharing the spoils of war, providingjobs and security or just because the war would then stop (carrots). Others are convincedthrough force and indoctrination (sticks). Besides this rational way of achieving the goal ofautonomy and legitimacy, the ruler’s actions are also incentivized by revenge (including thatof the Malian state / military). Furthermore there are some standard dynamics/approaches toachieve post-war stability; gain the hearts and minds of the people, monopoly on violence to thestate, integrate opposition warriors into own army.


Dr. van Willigen remarked briefly that he feels that Agent-Based modeling would be in thepositivism paradigm of methods. As interviewer we have noted that Agent-Based modelingdoes have strong post-modernistic tendencies due to its focus on underlying dynamics, notionsof observer dependency and acknowledgment that there are unlikely to be stable equilibria.This provided him with the opinion it is probably a bit in the middle of the two paradigms.Furthermore he had some important notions on the definitions. One should be careful withsaying old- and new wars, this is the distinction respectively between the state to state wars andthe wars with non-state actors as we know them since the cold war has ended. Furthermore warsare a type of conflict, for our research we need to carefully define the terms war and conflictand distinct between the two throughout our thesis. Finally the term state stability can beregarded as a quite normative term. There is negative peace / stability, which is a status-quowhere still armed groups with the will to fight each other are still present, and there is a morepostitive peace / stability where none of the groups are armed, or willing to attack, or evenexistent anymore. Ironically as western outsiders we would desire positive peace but to preventone group wiping out the other group we can only settle for negative peace / stability.

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H.3.3.1 Presentation slides

1

1Interview with Domain expert Dr. van Willigen

31-1-2013


DelftUniversity ofTechnology

Agent-Based model of State StabilityInterview with Domain expert Dr. van Willigen


1.Introducing study

Figure H.3: Presentation of interview with Dr. van Willigen (1/9)

184

2


Introducing study

• Systems Engineering, Policy Analysis & Management

• Safety Science traditionally developing safety management

systems (incl. Risk assessments)

• Safety Science department’s increasing interest in security

• Participation of Safety Science in the Safety, Security & Justice

minor with Leiden at Campus the Hague

• Due to my personal interest, amplified by LKY, they gave me

space to execute this study and explore the role of TU Delft in

International Security

Background of study


Introducing study

• Emergence of new (internal) conflicts and trouble with analysis

• Complex Adaptive Systems – Complexity theory

• Analysis tool: Agent-Based modeling

• Research questions (whether, how and when):

• Research method

• Goal of this interview: gather knowledge on traditional analyses

and on case

Research problem

Figure H.4: Presentation of interview with Dr. van Willigen (2/9)185

3


Introducing study

• Emergence of new (internal) conflicts and trouble with analysis




• Research method

• Goal of this interview: gather knowledge on traditional analyses

and on case

Research problem


2.General questions on applied state

stability analyses

Figure H.5: Presenation of interview with Dr. van Willigen (3/9)186

4


General questions on applied state stability analyses

• What do we want to know?

• For whom are analyses executed?

• Why do we want to know more on state stability?

• Role of analyst?

What, for whom and why?



• Types of sources of information?

• Types of information?

• Types of conclusions?

• Analysis processes?

• Reliability and handling uncertainty?

• Strengths, weaknesses, opportunities and threats?

How?


5



• Information requirements

• Flexibility of types of information• Amount of required information

• Cost of information gathering (time & money)

• Analysis

• Flexibility of application (case, client, goal of analysis)• Cost of analysis (time & money)

• Results

• Fulfillment of analysis goal

• Level of uncertainty• Reliability• Transparency (trust in method & understandable conclusion)

Quality criteria of analysis?


3.Exploring dynamics using the Mali

case


6


Exploring dynamics using the Mali case

• Intervention power is problem owner

• Multiple phases for each area in (and around Mali)

• Conflict• Stabilizing• Autonomy

• Dynamic in area depends per phase and actor presence

• Inter-relation from state level to local level

• Inter-relation from network level to local level

Perspective on case



• Mali state

• Political• Military � Security

• Rebel groups

• MLNA (Toearegs)• Religious extremist(x)• Emerging rebel groups

• Intervention force

• Political• Military � Security

• Local citizens (x)

Actor analysisInterest

Goal

Current situation

Cause of current situation

Instruments to influence

Relevant properties

Relevant resources

Relevant external influences


7



• Local area

• State

• Network

• Areas beyond Mali border

Object analysis

Relevant properties

/

Relevant resources

/

Relevant external dynamics



• Local area

• During conflict phase• Rebel vs Rebel

• Army vs Rebel

• During stablizing phase• Presence of Mali state with Intervention power

• During autonomy phase

• Presence of Mali state

• Presence of religious extremist

• Presence of MLNA

Identification of dynamics in local area


8



• State

• During conflict phase

• During stablizing phase


• Network

• During conflict phase

• During stablizing phase


Identification of dynamics in state/network



Brainstorm


9


4.Wrap up


Wrap up

• General opinion on quantitative methods

• Opinion on Complex Adaptive Systems & Agent-Based modeling

• View on Mali conflict and its future

• Inter-relation with own work

• Etc.

Discussion and ideas on study


H.3.3.2 Written notes

Figure H.12: Written notes (in Dutch) of interview with Dr. van Willigen (1/3)

193

Extra sheets

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194

General questions on applied state stabilitv analyses

What, for whom and why?

Wh at do we want to know? '^{j/jr-vifVj.

For which t ime frame?

For whom are analyses executed?

• Why do we want to know more on state stability?

Role of analyst?

How?

Types of sources of information?

-Types of informatie ion?

Types of conclusions?

Types of processes? ^^(TDAJuXn^A^rJhrZd^^ T&tc^^S^ (g^ü oizJ^ ip^ZCTQ^ iTZO/^^j ^'^-TjljrMu) D<^:rC<r^U/Zot(l^^^

Reliability and handling uncertainty?

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Evaluating criteria

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H.4 Interview with Prof.dr. Rudesindo Nunez Queija& NL Capt. Onno Goldbach

Date:6 February 2013

Location:University of Amsterdam - Roeterseiland complex building J/K

Goals:Explore knowledge on current quantitative analysis methods and information limitationsExplore thoughts on Agent-Based modeling approachValidate quality criteria of analysis methods

Interview method: Semi-structured Interview and Structured Interview

H.4.1 Background of interviewees

H.4.1.1 Profiles

H.4.1.1.1 Prof.dr. Rudesindo Nunez Queija is a full professor at the Korteweg-DeVries Mathematical Institute, an associate professor at the University of Amsterdam and aresearcher at the Centre for Mathematics and Informatics (CWI). His main research interestsare in stochastic operations research, in particular queueing theory, and applications in theperformance analysis of computer communication systems and road traffic. In the recent pasthe has worked on (1) stability criteria and optimization for bandwidth sharing networks (2)scaling techniques for multidimensional random Markov processes, (3) scalability and efficiencyof distributed file sharing systems, (4) heavy-traffic scaling, and (5) analysis of queues withfluctuating and temporary unstable load conditions. At the recently opened Ad de Jonge centrefor Intelligence and Security studies of the University of Amsterdam he lectures QuantitativeAnalysis Techniques for Intelligence Services. This course, which is part of the IntelligenceStudies minor, is developed in cooperation with Dutch Defense Intelligence & Security School(DIVI).

This profile is adopted from the Centre for Mathematics and Informatics website [49] with theaddition on the course he lectures from the studyguide website of the University of Amsterdam[82].

H.4.1.1.2 Capt. Onno Goldbach has no public profile on the internet. All we knowfrom the interview itself is that he developed and lectures the course on Quantitative AnalysisTechniques for Intelligence Services together with Prof.dr. Rudesindo Nunez Queija, and worksat the Dutch Defense Intelligence & Security School. This school provides courses, trainingsand training support on intelligence and security. Secondary to intelligence and security theylecture on military geography, electronic warfare, psychological warfare and languages. Besideslecturing and training they contribute to the (joint) doctrine development and policy supporton all defense related knowledge areas. They also fulfill assignments on behalf of the minister ofDefense on (inter) national cooperation with civilian and/or military institutes and coordinateinterpreter services throughout all the defense institutes.

196

This profile is based on the introductions made during the interview and the description of theDutch Defense Intelligence & Security School is our translation of the school’s profile from theDutch Defense website [41].


The two interviewees are selected because they lecture the course Quantitative Analysis Tech-niques for Intelligence Services, which is the only one of his kind in the Netherlands. Theoverlap of this course with this study is big in the sense that Agent-Based modeling is a quan-titative method and that this study explores whether it can be applied to assess state stability,for which the services of intelligence agencies are required. Although initially only Prof.dr.Rudesindo Nunez Queija was contacted, the course coordinator, it was very helpful that he wasable to arrange a meeting where Capt. Onno Goldbach could also be present. In this way wereable to gain both academic and military applied insights.


In our search for experts that know how currently intelligence processes are executed and whoare available/allowed to talk about it, we aimed to identify academic lecturing about theseintelligence processes. Going through the prospectuses of the universities that lecture in securitywe discovered the course Quantitative Analysis Techniques for Intelligence Services and thatProf.dr. Rudesindo Nunez Queija lectures this course [82].


Per topic we divide per paragraph by what Prof.dr. Rudesindo Nunez Queija and Capt. OnnoGoldbach told us.

H.4.2.1 Explore knowledge on current quantitative analysis methods and informa-tion limitations

H.4.2.1.1 Prof.dr. Rudesindo Nunez Queija The course on Quantitative Analysis Tech-niques for Intelligence Services is created with the bigger goal of bringing quantitative analysisskills into intelligence analysts. There is a general awareness that quantitative methods can adda lot to the qualitative methods currently applied. For now the quantitative methods appliedare mainly data-mining and statistics with the main goal to filter information and assess use-fulness and reliability of available information. As one can expect this is very important withinintelligence operations. Increasingly statistics is also used to identify trends and some gametheory is applied but this is only on non-cooperative games.

197

H.4.2.1.2 Capt. Onno Goldbach

H.4.2.1.2.1 What do intelligence services want to know in cases like Mali As mil-itary intelligence we would like to know as much as possible on everything, from intentions tocapacities of individuals or groups. But also past activities to get impressions on what theirfuture activities will be. Furthermore it is useful to understand how local groups operate, sowe know whether and how we want/can support or attack these groups. But it is importantto realize that there are two levels within the Dutch military intelligence, there is the strategicintelligence which is executed by the MIVD and the more tactical/operational intelligence exe-cuted by the military itself (the latter is where the interviewee has experience in). The MIVDis also the organization that is more interested in long term military policies and thereforeanalyses state stability, and takes into account the role of resources and demography. This toassess the security of where the Dutch military is and to develop counter insurgency measures.Both intelligence services work tightly together; to indicate they often are in the same room andindividuals even take over each other his duties if required.

H.4.2.1.2.2 How do intelligence services gather information in cases like Mali?The type of sources used by intelligence agencies are all the types of sources the NATO identi-fied. Generally the quantitative data comes from standard qualitative analyses translated intonumbers by the analyst itself. Some experiments show however that there can be quantitativedata gathering from the field as well. An example of what he tried, were registering traffic flowswhich is technically possible but sadly no support for by the executing parties because soldiersand their superiors wouldn’t consider such activities “cool” enough. Such change in informationgathering requires a change in mindset on all levels, similar to all organizations. Furthermoreone should realize that in a conflict zone it is even harder than normal to gather informationby interviews etc. This is especially important when one wants to apply game theory and onemust determine the pay-offs for every actor.

H.4.2.1.2.3 How do intelligence services assess information in cases like Mali? Toassess the gathered information quantitatively generally programs like Excel and Access are usedfor statistical purposes. Furthermore, increasingly there are social network analyses applied. Butthe course on quantitative methods is kind of ahead of practice in the Dutch military when itcomes to applying methods like Game Theory.

H.4.2.2 Explore thoughts on Agent-Based modeling approach

H.4.2.2.1 Prof.dr. Rudesindo Nunez Queija Agent-Based modeling is very promisingfor this application and it is clear that it is important to develop such methods further. Howeverthere are some important hurdles that the development of this method should take before it willbe used in practice. First of all; the method has to be sold to practitioners, which means thatresearch is required that shows the usability and the value of this method. Other hurdles arehandling the problems of deducting the decision rules from actors, which is especially difficult inconflict zones and are very determining for the behavior of the system. Finally the results of anAgent-Based modeling study are tricky to interpret. The approach of future scenario analysesand considering the bandwidths of the different policy options and treating them as plausibleresults, to know where we stand now and where it can go in the future, is very useful and a

198

good way to handle the complexity of the system. However still one should be very careful tounderstand that generated scenario’s similar to the real world, can still result in very differentbehaviors due to the sensitivity of the complex system to all values and rules inserted into themodel.

H.4.2.2.2 Capt. Onno Goldbach Although being unfamiliar to the method, after theexplanation, it was clear that the concept definitely has potential and looks interesting. Themethod seems realistic and the results would be useful in practice. But for more insights onthe method it might be a good idea to refer to TNO Defense department who are working onthe development of dynamic analysis methods, not sure though they also work on Agent-Basedmodeling.

H.4.2.3 Validate quality criteria of analysis methods

H.4.2.3.1 Prof.dr. Rudesindo Nunez Queija The criteria are clear and it seems logicalto base them on quality criteria of scientific research methods.

H.4.2.3.2 Capt. Onno Goldbach Agrees with Prof.dr. Rudesindo Nunez Queija butwants to note that the flexibility/transerability criteria is important but not paramount insecurity. Within the security domain, every problem has to be assessed. So when the methodis very rigid and can only used for an unique type of problem that rarely occurs, the method isstill extremely valuable when it is the only one or the most valid one to assess that unique typeof problem.


H.4.2.4.1 Prof.dr. Rudesindo Nunez Queija Whereas Agent-Based modeling is hardlyapplied in practice in (international) security, is already been applied in practice within eco-nomics. To analyse the steps Agent-Based modeling has to go through before it can be imple-mented, it might be interesting to consider the evolution Agent-Based modeling made withinthe field of economics (from science to practice). Furthermore, that this method is hardly/notapplied in the Netherlands or according public literature elsewhere, it doesn’t mean that othernations do apply it. Especially from security oriented nations like the US and Israel one cansuspect they have developed this method already, so focus on scientists from these nations onthis topic.

H.4.2.4.2 Capt. Onno Goldbach Consider reading through the NATO doctrines on in-telligence, they are comprehensive and quite strictly adopted by the agencies.

199



1

1Interview with Domain Experts

8-2-2013


DelftUniversity ofTechnology

Agent-Based model of State StabilityInterview with Domain Experts


1.Introducing study

Figure H.15: Presentation of interview with Prof.dr. Rudesindo Nunez Queija & Capt. OnnoGoldbach (1/8)

200

2


Introducing study

• Systems Engineering, Policy Analysis & Management

• Safety Science traditionally developing safety management

systems (incl. Risk assessments)

• Safety Science department’s increasing interest in security

• Participation of Safety Science in the Safety, Security & Justice

minor with Leiden at Campus the Hague

• Due to my personal interest, amplified by LKY, they gave me

space to execute this study and explore the role of TU Delft in


Background of study


Introducing study

• Post-Cold War conflicts: War & Rebuilding




• Research method

• Maingoal of this interview: gather knowledge on current analysis

methods and information limitations

• Secondary goal: gather first thoughts on new approach

Research problem


201

3


2.General questions on applied state

stability analyses



• What is it what we want to know?

• Types of goals?

• Types of conclusions?

• Why do we want to know it?

• Role of analyst?

What and why?


202

4



• Analysis processes?

• Types of sources of information?

• Types of information?

• Reliability and handling uncertainty?

How?



• Information requirements

• Flexibility of types of information

• Amount of required information

• Cost of information gathering (time & money)

• Analysis

• Transferability of method to other goals, problem owners, etc.

• Cost of analysis (time & money)

• Results

• Fulfillment of analysis goal

• Level of uncertainty in generated results

• Validity of results (Credibility)

• Transparency

• Trust in method (Dependability)

• Understandable conclusion to present to problem owner

• Confirmability (degree of neutrality of researcher)

Quality criteria of analysis?


203

5


3.Reflection on Agent-Based model

concept


Gov.

Gov.

Gov.

Animation explaning the concept of the model


204

6


Required input informationType of dynamics “classes”


Required input information

• Mali state

• Government

• Military � Security

• Rebel groups

• MLNA (Toearegs)

• Religious extremist(x)

• Emerging rebel groups

• Intervention force

• Political

• Military � Security

• Population group (x)

Actor analysisInterest

Goal

Current situation

Cause of current situation

Instruments to influence

Relevant properties

Relevant resources

Relevant external influences


205

7


Required input informationType of dynamic classes per phase


Gov.

Gov.

Animation explaning the concept of the model


206

8


Results

• Generation of plausible future scenario’s per policy option

• Identification of type of emergent system behaviors

What will the model contribute to assessment?

X

TimeNow “Peace”


Discussion

• Impression of concept

• Additional value to existing methods – is something similar in use?

• What does it miss?

Reflection on Agent-Based model concept


207


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Figure H.23: Written notes (in Dutch) of interview with Prof.dr. Rudesindo Nunez Queija

208

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6 W 7 cLe-inSAn cwt^i

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Figure H.24: Written notes (in Dutch) of interview with Capt. Onno Goldbach

209

H.5 Interview with Dr. Giliam de Valk and Mr.drs. WillemijnAerdts pt.1


Location:University of Amsterdam - Roeterseiland complex Building G

Goals:Explore knowledge on current qualitative analysis methods and information limitationsExplore thoughts on Agent-Based modeling approach



H.5.1.1 Profiles

H.5.1.1.1 Dr. Giliam de Valk Dr. Giliam de Valk obtained his PhD at the University ofGroningen on the quality of the Dutch intelligence. He is a board member of the NetherlandsIntelligence Studies Association (NISA), and vice-chair of the European Security IntelligenceFoundation (ESIF). He is specialized in Methodology, Methods & Techniques and the historyof intelligence- and security services. Furthermore he lectures from the Ad de Jonge Centrum:Secret Practices, Qualitative Analysis Methods and an honours course in Intelligence Studies.During interviews it became clear he works closely with the Dutch Defense intelligence services.

H.5.1.1.2 Mr.drs. Willemijn Aerdts Mr.drs. Willemijn Aerdts obtained an Master ofLaw degree in International Public Law and the Master of Arts degree in International Relationsat the Utrecht University. Among others, she used to work for the Dutch ministry of ForeignAffairs. Currently, she is also the co-chair of the Worldconnectors and a Global Shaper atthe World Economic Forum. She is specialized in terrorism and international organizations.Furthermore she lectures from the Ad de Jonge Centrum: Secret Practices, Qualitative AnalysisMethods and an honours course in Intelligence Studies.

Both profiles are adopted from the Ad de Jonge center website [40] with a few adaptions to fitthe formatting of this thesis.


The two interviewees are selected because they lecture courses on Qualitative Analysis Methodsin the Intelligence minor of the Ad de Jonge Center. In order to find the right position of a newanalysis method, like agent-based modeling, in the intelligence analysis structure we are alsointerested in qualitative analysis methods. Furthermore the background of both interviewees inacademic intelligence studies would also help us guide us in academic literature on the generalintelligence methodology.

210


In our search for experts that know how currently intelligence processes are executed and whoare available/allowed to talk about it, we aimed to identify academic lecturing about theseintelligence processes. Going through the prospectuses of the universities that lecture in securitywe discovered the course Quanlitative Analysis Techniques for Intelligence Services and that Dr.Giliam de Valk and Mr.drs. Willemijn Aerdts lectures this course [81].


Basically the main goal of gathering knowledge on current qualitative analysis methods werehardly met. Instead the interviewees changed it in a brainstorm to;

• Informing and showing the interviewees the potential of Agent-Based modeling for intelli-gence analysis practices;

• Determine exactly what knowledge gaps need to be filled by the interviewee for this study;and

These two objectives have been fulfilled and a brief report on both goals is presented below.

H.5.2.1 Informing and showing the interviewees the potential of Agent-Basedmodeling for intelligence analysis practices

After showing the presentation and explaining the concepts of both complex adaptive systemsand agent-based modeling, both interviewees indicated to be interested in the development of thismethod and felt it had quite some promise in the intelligence community. This was confirmed bythe fact that both interviewees suggested future cooperation in the development of the analysistool (during and after this study).

H.5.2.2 Determine exactly what knowledge gaps need to be filled by the intervie-wee for this study

After discussing our ideas on what Agent-Based modeling can do as a future scenario generator,the interviewees explained in brief how currently future threat scenarios are generated and usedby intelligence agencies. Sketches of the intelligence process involving scenario exploration areseen in figure H.26. In the next interview Dr. de Valk will provide us with a brief lecture onthis type of analysis, called Warning Intelligence and the related documentation of the NATOand the Dutch Defense Academy.


The concept of intelligence studies; scientific research to intelligence practices is quite new andthe interviewees feel that there is still a lot that intelligence services can learn from academicswhen it comes to their analysis methods. Furthermore current intelligence practices that involvescenario exploration, aim to identify relevant warning indicators. The scenario explorationshould be strong to help us identify weak signals, a term coined by the Hart Rudmann commis-sion in 1999 [35]. The interviewees furthermore suggested us to consider the Tit for Tat model

211

of Kevin Dutton [44], since that seemingly closely relates to Robert Axelrod’s The evolutionof Cooperation [7]. Finally the interviewees suggested to consider the concept of personalityanalyses as part of relevant dynamics in a model.



The same slides were used as with the interview with Prof.dr Rudesindo Nunez Queija and NLCapt. Onno. Basically the goals and the set-up of the interview was the same, only the focusdiffered from quantitative to qualitative. The slides can be found in paragraph in the previousinterview report.

212


D(L I / A L K

Qrjri/jvuyO^'Ur^

Figure H.25: Written notes of interview with Dr. Giliam de Valk and Mr.drs. Willemijn Aerdtspt.1

213

H.5.3.3 Capture of whiteboard notes

Figure H.26: Whiteboard notes made by Dr. Giliam de Valk during interview

214

H.6 Interview with Dr. Giliam de Valk pt.2


Location:University of Amsterdam - Science Park Building 904

Goals:Receive documentation on Warning Intelligence Receive guiding explanations on Warning Intel-ligence

Interview method: Unstructured interview


See for the background on Dr. Giliam de Valk, paragraph H.5.1.1.1.


H.6.2.1 Receive documentation on Strategic Geopolitical Intelligence

This interview was mainly a brief lecture on intelligence analyses involving scenario exploration.Dr. Giliam de Valk provided us with documentation on these analysis methods, especially onthe Warning Intelligence method. This documentation originates from a lecture by the DutchDefense Academy and the MIVD for the post-grad minor Intelligence & Security; The GenericEarly Warning Handbook of the NATO; and slides from lectures that Dr. Giliam de Valk usedfor his course on Qualitative Analysis Methods at the University of Amsterdam.

H.6.2.2 Receive guiding explanations on Warning Intelligence

H.6.2.2.1 Distinctions between the two Dutch Intelligence & Security services .We summarized the main distinctions between the two services in table H.1.

Table H.1: Summary of the main distinctions between the two Dutch Intelligence & Securityservices

MIVD AIVD

Defense Intelligence & Security Service General Intelligence & Security ServiceOffensive oriented Defensive oriented

Little information gathering Intensive information gatheringForeign operations Domestic operations

H.6.2.2.2 Near term analysis One can define a classical approach for near-term analysiswherein scenarios are developed based on the gathered intelligence data. This is opposed tothe new adopted Early Warning of NATO where the gathering of data is based on developed

215

scenarios. Early Warning is an indicator based intelligence method and is currently being usedby the MIVD for their near term assessments.

H.6.2.2.3 Drawbacks of Warning Intelligence There are three main drawbacks of thecurrent implementation of Warning Intelligence:

• Warning Intelligence takes into account maximum around 5 scenarios;

• Warning Intelligence doesn’t allow for de-warning indicators; and

• Warning Intelligence is almost strictly a qualitative method.

H.6.2.2.4 Quantitative analyses in intelligence studies For long term analyses on anoperational and tactical level, are quantitative methods increasingly applied. Most dominantlynetwork analyses. However quantitative methods are in practice not/hardly being used at astrategic level.

H.6.2.2.5 Distinction between the data collector and the analyst This distinctionis essential throughout the entire intelligence cycle. The analyst provides the gatherer withassignments, and the data of the gatherer influences the analyses of the analyst. All decisions tochoose to gather specific types of data should be transparent and well funded. This is essentialbecause not the least when assessments prove to be incorrect, then the analyst is not liable forthe incorrect assessment.


When scenarios are created one can assess the uncertainty and the impact of such scenario tomaterialize. The product of these two aspects determines the threat of this scenario. This isvery similar to assessing risk scenarios (product of probability and impact), however one shouldcarefully distinct between threat scenarios and risk scenarios.


H.6.3.1 Documentation on Warning Intelligence

Despite that the documentation used is marked unclassified, we are not allowed to providethe full documentation as raw data due to the copyright involved. To show we do have thesedocuments to our disposal, we provided scans of the covers.

216

Warning Intelligence

step 1 • Problem definition: what (key issue) are we warning

about? Step 2

• Create scenarios which show different ways the problem can develop

Step 3 • Determine (collectible) indicators for each scenario,

these will tell which scenario is unfolding Step 4

• Develop a collection plan based on the indicators -Can HUMINT/IMINT/OSINT/SIGINTfind the info?

nii!ifl!aHi.ifl!ia

Figure H.27: Cover of the lecture slides on military intelligence by the Dutch Defense Academyand the MIVD

217

Euro-Atlantic Partnercfhip Council

Co/ideil de Partenariüt Euro-Atlantique

NATO/EAPC/PFP UNCLASSIFIED

Releasable to Mediterranean Dialogue Countries

30 October 2001 DOCUMENT EAPC(COEC)D(2001)2

COUNCIL OPERATIONS AND EXERCISE COMMITTEE IN EAPC/PFP FORMAT (COEC(EAPC/PFP))

GENERIC EARLY WARNING HANDBOOK

NOTE BY THE CHAIRMAN

References: (a) Generic Crisis Management Handbook (EAPC(COEC)D(99)1); (b) Generic Inventory of Preventive Measures

(NACC/PfP(COEC)D(96)1; (c) Generic Catalogue of Military Response Options

(NACC/PfP(COEC)D(97)3; (d) Generic Manual of Precautionary Measures

(2036/SHOJW/004/98); (e) Compendium of Crisis Management Structures and Measures

(EAPC(COEC)N(99)4).

1. In light of continuing Interest In the previous generic crisis management documents, the IS and the IMS Warning Secretariat have prepared the attached Generic Early Warning Handbook.

2. The GEWH is an agreed NATO document. The documentation attached has not been agreed by Allies and does not constitute NATO documentation. It is provided for Information.

3. It is provided as part of the continuing process of assisting PfP Partners to develop their own crisis management organisation and procedures and to provide a convenient reference document for Allies, Partners, Mediterranean Dialogue countries and selected international organisations on an essential aspect of conflict prevention and crisis management.

NATO/EAPC/PFP UNCLASSIFIED - 1 -

Figure H.28: Cover of The Generic Early Warning Handbook of NATO

218

'II 2/22/2013

Overzicht

• Definition

• Context intelligence

• Aim & function

• Conditions

• Types

• Exercise (1-5)

Figure H.29: Cover of slides on scenario building

219


t^\\\jD

Figure H.30: Written notes of interview with Dr. Giliam de Valk pt.2

220


Date:22 March 2013

Location:Leiden Central Station - Starbucks

Goals:Verify intelligence processes of NATO state intelligence servicesExplore further insights in the intelligence processes

Interview method: Structured/Semi-structured interview




This interview was based on structured questions to verify aspects of the identified intelligenceprocess. The answers on these questions were used as a lead to explore more about the intelli-gence process.

H.7.2.1 Based on what is an intelligence task updated or an intelligence planupdated? Near term assessment

H.7.2.1.1 Answer The intelligence collection is not being updated, unless mistakes weremade but this hardly happens. The warning problem is only updated when intelligence indicatesnone of the defined scenarios are plausible. After identifying a change in the crisis state, a certainscenario is considered active, then the whole process of near term assessment is restarted.

H.7.2.1.2 Additional insights The intelligence to determine the warning problem followsfrom the mid/long term assessment intelligence.

H.7.2.2 Based on what is an intelligence task updated or an intelligence collectionplan updated? Mid/long term assessment

H.7.2.2.1 Answer This process is continues and does not consider a warning problem nora precise intelligence collection plan.

H.7.2.2.2 Additional insights The focus of the assessment is based on prioritisation bythe mandated Ministers or new information. The focus of intelligence gathering is on usualindicators and the insights of the gatherer.

221

H.7.2.3 How are scenarios determined in near term (does it include; trend, wild-cards & scenario?) and a selection?

H.7.2.3.1 Answer Basically the scenarios are results of group brainstorms of very expe-rienced experts. There are three scenarios developed; one worst case scenario; one scenarioaccording to the current trend; and one scenario that would result in a complete paradigm shiftin the security arena in question.

H.7.2.3.2 Additional insights The level of detail on these scenarios are very low andtherefore quite comprehensive for all materializing scenarios.

H.7.2.4 How are wild cards determined in mid/long term

H.7.2.4.1 answer These are also the results of group brainstorms of very experienced ex-perts of the intelligence service.

H.7.2.5 How is the value of information (I-Aa1) taken into account in Near termassessment?

H.7.2.5.1 Answer Based on group of expert judgements that have to make explicit whythey feel a certain indicator is present or not. They take into account the consequences of theirassessments, including the level of priority of the indicator in question. This is the same for theanalysis of competing hypotheses process.

H.7.2.5.2 Additional insights The coding used within the MIVD is only A1-F6; lettersindicating reliability of source and numbers indicating credibility of information. The KGBsuggested the indicator; distance of source, which according to the interviewee should also beused by the NATO intelligence services. Furthermore he also suggests that it is useful to indicatethe overriding importance of the information for the hypothesis, on a three point ordinal scaleindicated with Roman numbers. Current coding is defined in STANAG (JP 2.0).

H.7.2.6 Where is the data gathering process in Mid/long term assessment?

H.7.2.6.1 Answer During Crucial info and Analysis of Competing Hypothesis (ACH)) anal-yses (see schematic in figure H.34). The Crucial info gathering is part of the continues monitoringof usual indicators added with information the information gatherer judges useful. During theAnalysis of Competing Hypothesis, additional information can be gathered if assessments proveto be inconclusive without certain information.

H.7.2.7 Is there a Warning Problem defined in mid/long term assessment?

H.7.2.7.1 Answer No, the constantly development of hypotheses determine the focus. Whichtechnically are possible falsifiable future stories.

222

H.7.2.7.2 Additional information There are four ways to develop a hypothesis: 1) gen-eralization/trends in cases 2) (scientific) theory 3) situational logic and 4) data immersion. Thelatter 2 are intelligence driven. These distinctions are made by Heuer.

H.7.2.8 Is there a standardized report for mid/long term assessment, like nearterm assessment?

H.7.2.8.1 Answer Mid/long term assessment is a continues process that monitors certainsituations. The management of who monitors what, is done by NATO who assigns areas thatthe different intelligence services need to cover. The reports are then qualitative/quantitativeshort updates. If the analysis is done for own government then they are usually part of anassessment on policy options or required capability analysis, which results in a typical policyreport.

H.7.2.9 Capability development, what types of capabilities?

H.7.2.9.1 Answer The capabilities as meant in the documentations, only involve the capa-bilities of the overall military in certain regions. It does not explicitly involve the intelligencecapability development.


The, about ten, hypotheses developed in the mid/long term assessment are assessed in whethercrucial info can be found and its importance is determined using SWOT. This will result in one-to-three trend hypotheses and one-to-two wild cards. Then critical unknowns identified duringthe crucial info assessment are used to develop an additional one-to-two scenarios as hypotheses.Then a selection of three-to-five hypotheses is made using ACH, which are evaluated and usedto identify capability requirements.

223


H.7.3.1 Written notesB

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224

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Figure H.32: Written notes of interview with Dr. Giliam de Valk pt.3 on mid/long termschematic

225


Date:6 June 2013

Location:University of Amsterdam - Science Park Building 904

Goals:Verify intelligence processes of NATO state intelligence servicesValidate the identified value criteriaValidate the effects of the suggested improvements on the value criteriaExplore potential of proposed design

Interview method: Structured/Semi-structured interview




This interview was aimed to verify and validate all aspects related to the intelligence services.Especially the verification of the intelligence process and the validation of the suggested im-provements and its effects, was important since Dr. Giliam de Valk is our only available sourceon how the intelligence services operate.

H.8.2.1 Is the Strategic Geopolitical Intelligence process, as executed by NATOstate intelligence services, represented correct in the IDEF0 diagrams?

H.8.2.1.1 Answer Dr. Giliam de Valk confirmed that the figures E.1, E.2, E.3 and E.9,in appendix E, are an accurate description of the Strategic Geopolitical Intelligence process asexecuted by NATO state intelligence services.

H.8.2.1.2 Additional insights It appears to be very useful that someone described theprocess in IDEF0 schematics.

H.8.2.2 Are the identified criteria relevant and together comprehensive in valuingan intelligence analysis method?

H.8.2.2.1 Answer Dr. Giliam de Valk confirmed that the criteria presented in figure 5.1in chapter 5, are all relevant and together comprehensive in valuing an intelligence method. Wealso asked him to fill in the online survey so this validation is also represented in the overallvalidation of these criteria.

226

H.8.2.2.2 Additional insights The analytical accuracy is the term that is being used todescribe to how conclusive the conclusions of an analysis are.

H.8.2.3 Are the suggested improvements relevant and lead to the presented valueimprovements?) and a selection?

H.8.2.3.1 Answer Dr. Giliam de Valk confirmed the relevance of the suggested improve-ments, presented in chapter 6 as well as their potential value improvements.

H.8.2.3.2 Additional insights The integration between the two methods, so sharing ofintelligence, is de facto already being done but is not an explicit procedure. Similarly are theprocesses executed iteratively and is there a de facto link between the intelligence service andpolicy makers. However because these aspects are not explicit it strongly depends on the peoplewhether intelligence is indeed shared between near and mid/long term, whether processes areindeed executed iteratively and whether intelligence services directly aid policy development. Italso depends on the professionalism of the NATO state in question. Finally he also notes thatrisk management “thinking” (focus on risks) cannot replace intelligence “thinking” (focus oncritical unknowns), but is a useful combination.

H.8.2.4 What do you think of the proposed process design?

H.8.2.4.1 Answer Dr. Giliam de Valk agreed about the potential of the proposed processdesign and he could see such a process work. He also agreed with the selection of the three typesof risk scenarios: highest risks, highest probabilities and highest consequences. Also the reasonto leave out the paradigm changing scenarios seemed reasonable to him.

H.8.2.4.2 Additional insights Due to the extreme number of scenarios, the critical indi-cators equal the critical unknowns. When de-warning indicators are identified, thus excludingthe possibility of a certain scenario of occurring, it is called a negative warning.


Try to use examples throughout the explanation of the intelligence process designs. Especiallyfor the intelligence updates in the new process, that would be useful. Furthermore read intoSherman Kent his Words of Estimative Probabilities, it might prove useful for the probabilitiesaspects in the intelligence processes.

227


H.8.3.1 Written notesB

ased

on

wha

t is

an

inte

llige

nce

task

upd

ated

or

an in

telli

genc

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ion

plan

upd

ated

? N

ear t

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ass

essm

ent:

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'rO

X'tT

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(^t4

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?/'-\

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n w

hat

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n in

telli

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e ta

sk u

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ed o

r an

inte

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nce

colle

ctio

n pl

an u

pdat

ed?

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/long

ter

m a

sses

smen

t:

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are

sce

nario

s de

term

ined

in

near

ter

m (

does

it in

clud

e; tr

en

d, w

ildca

rds

& s

cena

rio?)

and

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tion?

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rm (

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228

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Figure H.34: Written notes of interview with Dr. Giliam de Valk pt.3 on mid/long termschematic

229

Appendix I

E-mail logs

I.1 Overview of relevant e-mails

In the main thesis and other appendices, there are some references to e-mails received. To beas transparent as possible we provide in this appendix the logs of all e-mails referred to. Eachof these e-mails include an introduction to the context of that e-mail. Below we present anoverview of the e-mail log:

I.1 Overview of relevant e-mails . . . . . . . . . . . . . . . . . . . . . . . . 229

I.2 E-mail of Prof.dr. Cederman of ETH Zurich . . . . . . . . . . . . . . 230

I.3 E-mail of Dr. Andrea Ruggeri of the University of Amsterdam . . 231

I.4 E-mail of Dr. De Valk of Ad de Jonge Centre of Intelligence Studies1/2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233

I.5 E-mail of Dr. De Valk of Ad de Jonge Centre of Intelligence Studies2/2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234

I.6 E-mail of Captain Onno Goldbach of the Dutch Intelligence andSecurity Institute (DIVI) . . . . . . . . . . . . . . . . . . . . . . . . . . 235

I.7 E-mail of Ir. Gerben Bas ofthe Delft University of Technology . . . . . . . . . . . . . . . . . . 236

230

I.2 E-mail of Prof.dr. Cederman of ETH Zurich

I.2.1 Context of e-mail

On 22 january 2013 we have send Prof.dr. Cederman an e-mail presenting the background of thestudy and a summary of the research proposal of this study. Prof.dr. Cederman is the writer ofEmergent Actors in World Politics: How States and Nations Develop and Dissolve [24] a bookvery often referred to in similar studies to ours. The aim was to ask an expert on Agent-Basedmodeling applied to International Security whether our study was indeed filling a knowledgegap and had scientific relevance. Secondary to that goal we hoped that he could provide us withsome directions in the relevant literature.

I.2.2 Content of e-mail

Christiaan Menkveld <[email protected]>

State of Science of ABM on intra-state conflicts

Cederman Lars-Erik <[email protected]> 28 januari 2013 13:13Aan: Christiaan Menkveld <[email protected]>

Dear Mr. Menkveld,

Sorry for the slow response. I'm very pleased to hear that you are interested in applying ABM to conflict casessuch as Mali. I have to confess that since 2005, I have not actively pursued this stream of research, but it has a lotof promise. Attached you will find an early review piece from 2001 as well as a book chapter from 2008 thatfocuses on civil wars. An additional review piece from 2005 covering mostly interstate conflict is also attached.

To find recent applications of ABM to (internal) conflict, you may want to look into what Ravi Bhavnani at theGeneva Graduate Institute of International Studies has produced.

Other people include Ian Lustick and Joshua Epstein.

I wish you the best of luck with your project!

Best regards,

Lars-Erik Cederman[Tekst uit oorspronkelijke bericht is verborgen]

E [email protected]<mailto:[email protected]>

<Letter to Prof Dr Cederman on State of Science of ABM on intra-state conflicts.pdf>

3 bijlagen

TPM_fall01.pdf438K

ExploringGeopolitics.pdf527K

Cederman_In_Kalyvas_et_al_2008pdf.pdf862K

Gmail - State of Science of ABM on intra-state conflicts https://mail.google.com/mail/?ui=2&ik=b6f265cf62&view=pt&q=ced...

1 van 1 28-2-2013 11:49

Figure I.1: E-mail of Prof.dr. Lars-Erik Cederman

231

I.3 E-mail of Dr. Andrea Ruggeri of the University of Amster-dam


In order to get insights into relevant dynamics in conflict zones we contacted among others Dr.Andrea Ruggeri for an interview. After sending the research proposal he send the e-mail aspresented in figure I.2, to which we replied and received another e-mail as presented in figureI.3.



Fwd: Research to modeling state stability TU Delft

Ruggeri, A. <[email protected]> 4 februari 2013 11:39Aan: Christiaan Menkveld <[email protected]>Cc: "Hooft, Paul van" <[email protected]>

Dear Chris�aan,

If I understand correctly this is an MA thesis? I do believe there are several pieces of the literature you are

mssing ( espceilly on the use of ABM in IR/ pol�cla stability). I men�ons few names rthat you should google

and read their work:

-Lars Erik Cederman ( you men�on his old work)

-Nils Weidman

-Ravi Bhavnanini

-Dan Miodownik

-Claudio Cioffi-Revilla

This to start with. If you want, then, we can have a coffee and chat about your project.

Best regards

AR

Andrea Ruggeri

Assistant Professor of International Relations

Department of Political Science/

Amsterdam Institute of Social Science Research

University of Amsterdam

Visiting Address:

Office: 3.23

Gmail - Fwd: Research to modeling state stability TU Delft https://mail.google.com/mail/?ui=2&ik=b6f265cf62&view=pt&q=we...

1 van 2 1-3-2013 12:53

Figure I.2: E-mail 1 of Dr. Andrea Ruggeri

232



Fwd: Research to modeling state stability TU Delft

Ruggeri, A. <[email protected]> 4 februari 2013 14:00Aan: Christiaan Menkveld <[email protected]>Cc: "Hooft, Paul van" <[email protected]>

Hi,

Yes, let ‘s do it in March or whenever you feel the project is more “mature”. I do agree on the need of

combining real cases ( data) with ABM. Nils Weidmann has done something similar using Bagdad. Now, I’d

suggest you to have a look to the new GIS-event data done by Uppsla (h�p://www.pcr.uu.se/research/ucdp

/datasets/ucdp_ged/)

Good work!

AR

Andrea Ruggeri

Assistant Professor of International Relations

Department of Political Science/

Amsterdam Institute of Social Science Research

University of Amsterdam

Visiting Address:

Office: 3.23

Oudezijds Achterburgwal 237

1012 DL Amsterdam

The Netherlands

Office Telephone: +31 20 525 7355

http://home.medewerker.uva.nl/a.ruggeri/index.html

From: [email protected] [mailto:[email protected]] On Behalf Of Christiaan

Menkveld

Gmail - Fwd: Research to modeling state stability TU Delft https://mail.google.com/mail/?ui=2&ik=b6f265cf62&view=pt&q=we...

1 van 2 1-3-2013 12:53

Figure I.3: E-mail 2 of Dr. Andrea Ruggeri

233

I.4 E-mail of Dr. De Valk of Ad de Jonge Centre of IntelligenceStudies 1/2


After the interview with Dr. Giliam de Valk & Mr.drs. Willemijn Aerdts (Appendix H.5), Dr.Giliam de Valk suggested to have a second meeting a week later. In the time between the twointerviews, Dr. Giliam de Valk e-mailed us with an update on relevant information he gatheredin the meanwhile.



aanstaande vrijdag

Valk, Giliam de <[email protected]> 17 februari 2013 16:02Aan: "[email protected]" <[email protected]>Cc: "Aerdts, Willemijn" <[email protected]>

Beste Christiaan,

Ik heb nog e.e.a. voor je uitgezocht. Mogelijk is het volgende al bij je bekend:

- voor de voorgestelde methodiek van agent-based modelling gebruikt het Franse bedrijf DATOPS zoiets al

sinds 2005 in zijn Software genaamd Pericles, waarmee op basis van "agents" indicatoren voor sociale onrust

in samenlevingen worden gesignaleerd.

- verder maakt het bedrijf AUTONOMY progamma's om met behulp van actieve "agents" grote hoeveelheid aan

data te ontsluiten en indicatoren te laten oplichten.

Vrijdag krijg je de prints met informatie over near term analyse.

vriendelijke groet,

Giliam de Valk

Gmail - aanstaande vrijdag https://mail.google.com/mail/?ui=2&ik=b6f265cf62&view=pt&q=G.G...

1 van 1 28-2-2013 12:35

Figure I.4: E-mail of Dr. Giliam de Valk

234

I.5 E-mail of Dr. De Valk of Ad de Jonge Centre of IntelligenceStudies 2/2


Based on the documentation received during the interview reported in appendix H.6, we hadsome additional questions about the types of Strategic Geopolitical Analyses the MIVD executes.This e-mail is a response to these questions.



Korte update en een paar vraagjes - Master thesis Christiaan Menkveld

Valk, Giliam de <[email protected]> 19 maart 2013 16:16Aan: Christiaan Menkveld <[email protected]>, "Aerdts, Willemijn" <[email protected]>

Beste Chris�aan,

Dat is een onderwerp waar defensie zeker belangstelling voor zal hebben!

Bijgaand alvast een kort antwoord, maar misschien is het handig om –als je een keer in Amsterdam bent – er

wat uitgebreider op in te gaan.

Vraag 1, 2 en 3.

De Strategische Geopoli�eke Analyses volgen een ander traject. Dat zijn de ppt’s die ik voor je heb uitgeprint.

Je maakt dan een taxa�e voor de komende 5 – 10 jaar. Doel: je beleidsop�es voor die scenario’s, wild cards en

trends duidelijk te hebben en de capabili�es ontwikkelen om daarmee om te gaan. Je selecteert wild cards en

trents o.g.v. hun impact en waarschijnlijkheid.

Vraag 4.

Near Term. Je ontwikkelt cri�cal indicators om te weten of je in scenario 1, 2 of 3 zit. Over die cri�cal

indicators ga je informa�e verzamelen.

De cri�cla indicators dicteren aldus het intelligence collec�on plan (en niet omgekeerd).

Vraag 5.

A1-F6 en ACH worden gebruikt voor de middellange en lange termijn analyses (= beleid en capabilites

ontwikkelen), niet de near term (Warning Intelligence). Rou�ne, Abnormaal, Significant of Extreem komen

daarom pas in de near term om de hoek kijken: dan is er pas sprake van ac�onable intelligence, daarvoor nog

niet.

Ik hoop dat dit het een beetje duidelijker maakt.

Anders spreken we snel af.

Vriendelijke groet,

Giliam

Gmail - Korte update en een paar vraagjes - Master thesis Christiaan ... https://mail.google.com/mail/?ui=2&ik=b6f265cf62&view=pt&search...

1 van 2 19-3-2013 18:28

Figure I.5: Second E-mail of Dr. Giliam de Valk

235

I.6 E-mail of Captain Onno Goldbach of the Dutch Intelligenceand Security Institute (DIVI)


Based on our findings that the introduction of Agent-Based modeling will extend the duration ofthe analysis phase, we wanted to place this duration in perspective. Therefore we contacted Cap-tain Onno Goldbach to obtain information on the current duration of the Strategic GeopoliticalIntelligence processes.



Vraag betreffende methoden voor Strategische Geopolitieke Analyses

[email protected] <[email protected]> 14 juni 2013 13:54Aan: [email protected]

Beste Christiaan

Near-term: Assessments ten behoeve van monitoring. Dus het voortdurend in de gaten houden van een

bepaalde regio/staat, wat nu wordt gedaan met Indicator Based Warning Intelligence. Kortom, een

near-term lijkt dan een update van een mid/long term assessment

Near term lijkt me dan vooral data/informatie gestuurd en dus eigenlijk als “push” te

omschrijven is. Maar indien “pull” is dan zou ik dag-week zeggen

Mid/long-term: Assessments ten behoeve van capaciteiten ontwikkeling en beleidsvorming. Wat als het

goed is wordt gedaan door het ontwikkelen van hypotheses over hoe bv de situa�e in de regio om Mali zich

zou kunnen ontwikkelen. Hier worden dan de meest valide hypotheses geselecteerd adhv Analysis of

Compe�ng Hypotheses. Voor die hypotheses worden dan capaciteiten/beleid ontwikkeld. Ik zou maand-

kwartaal inschatten

Graag gedaan!

Onno

Dit bericht kan informatie bevatten die niet voor u is bestemd. Indien u niet de geadresseerde bent of dit berichtabusievelijk aan u is toegezonden, wordt u verzocht dat aan de afzender te melden en het bericht te verwijderen.De Staat aanvaardt geen aansprakelijkheid voor schade, van welke aard ook, die verband houdt met risico'sverbonden aan het electronisch verzenden van berichten.

This message may contain information that is not intended for you. If you are not the addressee or if this messagewas sent to you by mistake, you are requested to inform the sender and delete the message. The State acceptsno liability for damage of any kind resulting from the risks inherent in the electronic transmission of messages.

Gmail - Vraag betreffende methoden voor Strategische Geopolitieke A... https://mail.google.com/mail/?ui=2&ik=b6f265cf62&view=pt&q=on...

1 van 1 24-6-2013 13:09

Figure I.6: E-mail of Captain Onno Goldbach

236

I.7 E-mail of Ir. Gerben Bas ofthe Delft University of Technology


Based on our findings that the introduction of Agent-Based modeling will extend the durationof the analysis phase, we wanted to place this duration in perspective. Therefore we contactedIr. Gerben Bas to obtain information on the duration of developing an operating Agent-Basedmodel based on our formal model in chapter 11.


Figure I.7: E-mail of Ir. Gerben Bas

237

Appendix J

Survey

For this survey we chose to use Qualtrics as our survey tool [89], because this tool is the onlyone we came across that was both free and allowed for more than ten questions. In the followingsection we will present who our respondents are and why they were selected. The final sectionwill present all the results generated by the survey tool.

J.1 The respondents

For the selection of respondents we applied the following criteria for the respondent group inorder to assure that the experts are respectively relevant, experienced, various and likely torespond;

• Respondents are an expert in International Relations, Intelligence services or Safety &Security;

• Respondents are working in the field of interest i.e. not a student anymore;

• About half of the respondents group should have a foreign nationality;

• We have should have had personal contact with the respondant before.

We have contacted about 25 experts fulfilling the above criteria which resulted in 15 completedsurveys. Table J.1 shows the overview experts in alphabetical order with their area of expertise,their most recent two jobs and their nationality.

Note: MA = Master of Arts, IR = International relations, Int = Intelligence services, S&SSafety and security, TU Delft = Delft University of Technology, NUS-LKY = Lee Kuan YewSchool of Public Policy of the National University of Singapore and DIVI = Defense Intelligenceand Security Institute of the Dutch Ministry of Defence.

238

Table J.1: Overview of survey respondents

Name Area Current organization Previous organization Nationality

Drs. Hinke Andriessen S&S TU Delft - Dutch

Prof.dr. Kanti Bajpai IR NUS-LKY Oxford University Indian

NL Capt. OnnoGoldbach

Int DIVI Dutch

Drs. Jan de Graaf IR European Union United Nations Dutch

Jose Guerra Vio MA IR National ChengchiUniversity of Taiwan

Yonsei University(Korea)

Spanish

Reuben Hintz MA IR NUS-LKY US Navy American

Taufik IndrakesumaMA

IR NUS-LKY Indonesian Ministry ofEduction

Indonesian

Dr.ir. Ellen Jagtman S&S TU Delft SWOV Road SafetyResearch Institute

Dutch

Jun Jie Woo Msc IR NUS-LKY S.Rajaratnam School ofInternational Studies

Singa-porean

Drs. Marieke Kluin S&S TU Delft Dutch National PoliceServices Agency

Dutch

Enrique Paredes Int University New SouthWales

- Mexican

Dr. Andrea RuggeriABM-

IR

University ofAmsterdam

University of Essex Italian

Dr. Simone Sillem S&S TU Delft - Dutch

Dr. Giliam de Valk Int University ofAmsterdam

Dutch DefenceAcademy

Dutch

Drs. Thomas de Zoete Int Dutch StateGovernment

- Dutch

239

J.2 Survey results

Below we present per question the results as they were generated by the survey tool. For an inter-active report it is possible to access it online via; https://qtrial.qualtrics.com/WRReport/?RPID=RP2_3aSKcmZoBsKM8wl&P=CP with the following case sensitive password; [ TUDelft ].The intelligence expert specific questions are deleted due to very limited respondents of thisexpertise.

1 1 7.14%

2 0 0.00%

3 11 78.57%

4 0 0.00%

5 2 14.29%

14 100.00%

# Answer Bar Responses %

Strongly disagree

Disagree

Agree

Strongly agree

No opinion on this statement

Total

1 5 3.14 0.90 0.95 14 14

Min Value Max Value Average Value Variance Standard Deviation Total Responses Total Respondents

A quantitative modeling study can provide at least the same results as a trend analysis, if both executed properly

240

https://qtrial.qualtrics.com/WRReport/?RPID=RP2_3aSKcmZoBsKM8wl&P=CP

https://qtrial.qualtrics.com/WRReport/?RPID=RP2_3aSKcmZoBsKM8wl&P=CP

1 1 7.14%

2 8 57.14%

3 3 21.43%

4 1 7.14%

5 1 7.14%

14 100.00%


Strongly disagree

Disagree

Agree

Strongly agree


Total

1 5 2.50 1.04 1.02 14 14


It is possible to assume for the duration of the analysis, that all actors are given

[Given means that no new signif icant actors will emerge or enter the system ]

1 3 21.43%

2 9 64.29%

3 2 14.29%

4 0 0.00%

5 0 0.00%

14 100.00%


Strongly disagree

Disagree

Agree

Strongly agree


Total

1 3 1.93 0.38 0.62 14 14


It is possible to assume for the duration of the analysis, that all actors are f ixed

[Fixed means that the actors will not change goals or preferences ]

241

1 1 7.14%

2 7 50.00%

3 5 35.71%

4 1 7.14%

5 0 0.00%

14 100.00%


Strongly disagree

Disagree

Agree

Strongly agree


Total

1 4 2.43 0.57 0.76 14 14


It is possible to assume for the duration of the analysis, that all actors behave rational

1 3 21.43%

2 6 42.86%

3 5 35.71%

4 0 0.00%

5 0 0.00%

14 100.00%


Strongly disagree

Disagree

Agree

Strongly agree


Total

1 3 2.14 0.59 0.77 14 14


It is possible to assume that for the duration of the analysis, the structure of money f lows, immigration f lows, etc. isstatic

242

1 1 7.14%

2 0 0.00%

3 10 71.43%

4 1 7.14%

5 2 14.29%

14 100.00%


Strongly disagree

Disagree

Agree

Strongly agree


Total

1 5 3.21 0.95 0.97 14 14


Although all quantitative models are wrong, I feel that some prove to be very useful

1 1 7.14%

2 13 92.86%

3 0 0.00%

14 100.00%


Not a relevant criteria

A relevant criteria

A very relevant criteria

Total

1 2 1.93 0.07 0.27 14 14


Operation costs of an analysis

243

1 2 14.29%

2 10 71.43%

3 2 14.29%

14 100.00%



A relevant criteria


Total

1 3 2.00 0.31 0.55 14 14


Failure costs of an analysis

[Failure costs of an analysis is the total risk of investing money in an analysisthat is not able to provide useful results]

1 1 7.14%

2 7 50.00%

3 6 42.86%

14 100.00%



A relevant criteria


Total

1 3 2.36 0.40 0.63 14 14


Duration of an analysis

244

1 0 0.00%

2 8 57.14%

3 6 42.86%

14 100.00%



A relevant criteria


Total

2 3 2.43 0.26 0.51 14 14


Internal validity of an analysis

[The internal validity of an analysis is the extend to how the analysis is executed and how itwas intended to be executed]

1 0 0.00%

2 7 50.00%

3 7 50.00%

14 100.00%



A relevant criteria


Total

2 3 2.50 0.27 0.52 14 14


External validity of an analysis

[The external validity of an analysis is the extend to how much the results of the analysis are in line with thereal world]

245

1 0 0.00%

2 7 50.00%

3 7 50.00%

14 100.00%



A relevant criteria


Total

2 3 2.50 0.27 0.52 14 14


Reliability of an analysis

[The reliability of an analysis is the extend to which it is possible to replicate the same analysisand yield the same results]

1 2 14.29%

2 6 42.86%

3 6 42.86%

14 100.00%



A relevant criteria


Total

1 3 2.29 0.53 0.73 14 14


Objectivity of an analysis

[The objectivity of an analysis is the extend to which the bias or the unique perspective of theanalyst, is not reflected in the conclusions]

246

1 2 14.29%

2 12 85.71%

3 0 0.00%

14 100.00%



A relevant criteria


Total

1 2 1.86 0.13 0.36 14 14


Operation costs of information gathering

1 2 14.29%

2 10 71.43%

3 2 14.29%

14 100.00%



A relevant criteria


Total

1 3 2.00 0.31 0.55 14 14


Failure costs of information gathering

[Failure costs of intelligence gathering is the total risk of investing money in gathering intelligencebut fail to f ind sufficient intelligence in the right format required by the analysis method]

247

1 1 7.14%

2 10 71.43%

3 3 21.43%

14 100.00%



A relevant criteria


Total

1 3 2.14 0.29 0.53 14 14


Duration of information gathering

1 2 14.29%

2 8 57.14%

3 4 28.57%

14 100.00%



A relevant criteria


Total

1 3 2.14 0.44 0.66 14 14


Level of information integrity required by the analysis method to provide useful results

[The integrity of the gathered intelligence is the level of trustworthiness of the informatio requiredby the analysis method]

248

1 1 7.14%

2 13 92.86%

3 0 0.00%

14 100.00%



A relevant criteria


Total

1 2 1.93 0.07 0.27 14 14


Operation costs of reporting

1 2 14.29%

2 8 57.14%

3 4 28.57%

14 100.00%



A relevant criteria


Total

1 3 2.14 0.44 0.66 14 14


Failure costs of reporting

[Failure costs of reporting is the total risk involved of the readermisunderstanding the report with negative consequences]

249

1 1 7.14%

2 9 64.29%

3 4 28.57%

14 100.00%



A relevant criteria


Total

1 3 2.21 0.34 0.58 14 14


Duration of reporting

1 0 0.00%

2 5 35.71%

3 9 64.29%

14 100.00%



A relevant criteria


Total

2 3 2.64 0.25 0.50 14 14


Fulfillment of the goal of the study

[This includes analytical accuracy]

250

1 0 0.00%

2 6 42.86%

3 8 57.14%

14 100.00%



A relevant criteria


Total

2 3 2.57 0.26 0.51 14 14


Credibility of a report

[Credibility of a report is the extend to how much the policy maker trusts the f indings providedby the analysis, this is independent from whether the f indings are accurate or not]

1 4 28.57%

2 10 71.43%

3 0 0.00%

14 100.00%



A relevant criteria


Total

1 2 1.71 0.22 0.47 14 14


Transferability of a report

[Transferability of a report is the extend to how useful the results on a certain case are alsoapplicable to another case or study]

251

1 4 28.57%

2 9 64.29%

3 1 7.14%

14 100.00%



A relevant criteria


Total

1 3 1.79 0.34 0.58 14 14


Dependability of a report

[Dependability of a report is the extend to which the conclusions of the report remain valid underchanging circumstances, i.e. time until the report hits his expiration date]

1 1 7.14%

2 10 71.43%

3 3 21.43%

14 100.00%



A relevant criteria


Total

1 3 2.14 0.29 0.53 14 14


Confirmability of a report

[Confirmability of a report is the extend to which all the actions performed by the analyst canbe tracked based on the information provided in the report]

252

1 11 78.57%

2 3 21.43%

14 100.00%


Yes

No

Total

1 2 1.21 0.18 0.43 14 14


Below we show an overview of all the criteria we have identif ied. Do you feel it is complete?

1. dynamics / fast changing circumstances 2. uncertainty about data quality 3. (uncertainty about) data completeness

-conf identiality of a report/analysis (sometimes this can be very important in international organizations. But itcompletely depends what kind of analysis. Public? Or internal?) -Desirability / political feasibility / timeliness of theoutcomes (sometimes certain conclusions are not what a minister or other senior person wants to hear or the timing isnot right etc. e.g. an analysis might be best done by an external consultancy, but if the public has a tendency todisapprove expensive external companies providing analyses then it may not be possible to hire them - not because ofthe expenses perse but because of the public perception. Another example: some valid analysis might discreditcertain allies, so even if it is all true - then the requesting party might still decide to cancel the reporting) -Was qualityof reporting mentioned? Look both at the costs and benef its of the analyses.

Text Entry

Respondents 2

Statistic Value

Please describe the criterion or criteria that is/are missing according to you

253

Appendix K

Literature search log

K.1 Introducing the Literature search log

To make the research process of this thesis itself as Confirmable as possible, we present herebyour literature search log. The log is split up in four different paragraphs. Section K.2 showsthe log of all the literature we have gathered to set-up the research proposal. Section K.3 showsthe log of all the literature we have gathered for the main thesis. Section K.4 shows the logof all the provided/suggested sources by other experts. Finally section K.5 shows the log ofall news / topicality sources, which is mainly used for the case study. In case a certain sourceled to other accessed sources, via the bibliography or exploring that the same website, then theresulting sources are presented in italic behind the original source. All resulting sources refer tothe main bibliography or, if not cited outside the log, to the non-cited literature list below eachlog. Below we provide an overview of the literature search log:

K.1 Introducing the Literature search log . . . . . . . . . . . . . . . . . . 253

K.2 Literature search log of literature exploration . . . . . . . . . . . . . 254

K.3 Literature search log of main thesis . . . . . . . . . . . . . . . . . . . 257

K.4 Literature log of provided/suggested sources . . . . . . . . . . . . . . 261

K.5 Literature log of news / topicality articles . . . . . . . . . . . . . . . 265

254

K.2 Literature search log of literature exploration

Table K.1: Literature search log of literature exploration

Question of search Date Search words Search engine Relevantresults

What is the state-of-artin Complex Adaptive Sys-tems studies and Interna-tional Security studies?

10-12-2012 ( Complexity theory ORComplex systems ) AND


Scopus andGoogle

[122][24] [68]

[133][145][148][135]

[104] [7][143]

What is the state-of-art insimulations/modeling forinternational security?

10-12-2012 International securityAND ( simulation OR

modeling )

Scopus andGoogle

[84][142][116][60][150]

[115, 21,74]

What is the state-of-art inAgent-Based simulationsin international securitysciences?

10-12-2012 ( ABM OR Agent BasedModeling ) AND

”international security”AND complexity

Scopus andGoogle

[128][125][141][124][32]

Which case studies are in-teresting for the study?

18-12-2012 state stability assessmentAND ( syria OR jordan

OR arab spring ORarabische lente )

Google [114][140][134]

[138][127]

What is the state-of-art ofgeo-political (risk) assess-ments?

19-12-2012 ( geo-political OR geo ORregional ) AND (

assessment OR riskassessment ) AND security

Scopus andGoogle

[153][137][136][132][149]

What is the state-of-art ofstatistics in InternationalRelations?

19-12-2012 statistics in internationalrelations

Google [16]

255

Non-cited literature of above log

[122] D. S. Alberts and T. J. Czerwinski. Complexity, global politics, and national security.Technical report, DTIC Document, 1997.

[123] Robert Axelrod. The Evolution of Cooperation: Revised Edition. Basic Books, revisededition, December 2006.

[124] George A. Backus and Robert J. Glass. An agent-based model component to a frame-work for the analysis of terrorist-group dynamics. Albuquerque, NM: Sandia NationalLaboratories, 2005.

[125] R. Bhavnani, D. Backer, and R. Riolo. Simulating closed regimes with agent based models.Complexity, 14(1):3644, 2008.

[126] Bear Braumoeller and Anne Sartori. The promise and perils of statistics in internationalrelations. In Models, Numbers, and Cases: Methods for Studying International Relations.University of Michigan Press, Michigan, 2004.

[127] I. Briscoe, F. Janssen, and R. Smits. Stability and economic recovery after assad: keysteps for syria’s post-conflict transition. Conflict research unit, Netherlands Institute ofInternational Relations’ Clingendael’, The Hague, November 2012.

[128] Barry Buzan. Regions and powers : the structure of international security. CambridgeUniversity Press, Cambridge etc, 2003.

[129] D. Caldwell. The 1990 middle east crisis: A role-playing simulation. Foreign PolicyAnalysis Notes, 16(2):13–15, 1991.


[131] Jeffrey T. Checkel. Transnational dynamics of civil war. In workshop Mobilizing AcrossBorders: Transnational Dynamics of Civil Warsponsored by the Centre for the Study ofCivil War, Peace Research Institute, Oslo (PRIO) and the School for International Studies,Simon Fraser University. Oslo, Norway, page 2021, 2010.

[132] H. Chen, C. A. Larson, T. Elhourani, D. Zimbra, and D. Ware. The geopolitical web:Assessing societal risk in an uncertain world. In Intelligence and Security Informatics(ISI), 2011 IEEE International Conference on, page 6064, 2011.

[133] L. Cline. Complexity theory and counterinsurgency strategy. Small Wars Journal, March2012.

[134] Clingendael. Fragile state futures: Nine case studies from around the world, December2012.

256

[135] E. Cudworth and S. Hobden. Anarchy and anarchism: towards a theory of complexinternational systems. Millennium-Journal of International Studies, 39(2):399416, 2010.

[136] S. Dalby. Geopolitical change and contemporary security studies: contextualizing the hu-man security agenda. Institute of International Relations, University of British Columbia,2000.

[137] David A. Glancy. Glancy building capacity for geo-political risk analysis. Technical report,The Fletcher School - Tufts University, Medford, 2012.

[138] J. Goodhand. Contested transitions: International drawdown and the future state inafghanistan. Technical report, NOREF, London, November 2012.

[139] Guetzkow. Varieties of simulations in international relations research. Journal of ConflictResolution, 7(4):668–687, January 1963.

[140] Samuel Helfont and Tally Helfont. Jordan: Between the arab spring and the gulf cooper-ation council. Orbis, 56(1):82–95, 2012.

[141] Artificial Intelligence. Agent-based simulation of geo-political conflict. Artificial Intelli-gence (IAAI), 2004.

[142] Paul E. Johnson. Simulation modeling in political science. American Behavioral Scientist,42(10):1509–1530, January 1999.

[143] D. Kowalewski and D. Hoover. International security in the world-system: A model offuture dynamics. International Studies, 37(3):183–225, July 2000.

[144] David. Lane. Artificial worlds and economics. Technical Report 92-09-048, Santa FeInsitute, 1992.

[145] K. E. Lehmann. Unfinished transformation: The three phases of complexitys emergenceinto international relations and foreign policy. Cooperation and Conflict, 47(3):404413,2012.

[146] D Moreno. Potential US intervention in peru: A simulation. Foreign Policy AnalysisNotes, pages 4–5, 1992.

[147] T. B. Pepinsky. From agents to outcomes: simulation in international relations. EuropeanJournal of International Relations, 11(3):367394, 2005.

[148] E. Smith and A. Grisogono. Towards a synthesis of complex adaptive systems theory andeffects based approaches to operations - warfighters to coalitions: Case study of multi-leveladaptation in effects-based operations. Cambridge, September 2006.

[149] S. Spiegel. Regional security and the levels of analysis problem. Journal of StrategicStudies, 26(3):7598, 2003.

[150] Brigid A. Starkey and Elizabeth L. Blake. Simulation in international relations education.Simulation & Gaming, 32(4):537–551, January 2001.

[151] N. N. Taleb and M. Blyth. Black swan of cairo: How suppressing volatility makes theworld less predictable and more dangerous, the. Foreign Aff., 90:33, 2011.

[152] M. Van Leeuwen. De Arabische lenteen geopolitiek. Atlantisch Perspectief: Tijdschriftvoor Internationale Betrekkingen en Veiligheidspolitiek, 35(4):48, 2011.

257

[153] John P. Vanzo. A geopolitical analysis of the configuration israel and palestine. Seattle,September 2011.

[154] Charles Walcott. Simple simulations 2: a collection of simulation/games for politicalscientists. American Political Science Association, December 1980.

[155] Michael D. Ward, editor. Theories, Models, and Simulations in International Relations:Essays and Research in Honor of Harold Guetzkow. Westview Pr (Short Disc), illustratededition edition, September 1985.

K.3 Literature search log of main thesis

Table K.2: Literature search log of main thesis

Question of search Date Search words Search engine Relevantresults

What relevant documentshas Cederman producedsince his book in 1997 [24]

21-1-2013 Cederman Scopus [163] [56][164][28] [166][25][167][165]

What quality criteria canbe applied for scientific re-search methods?

5-2-2013 quality criteria forresearch method

Google [18] [72]

How to set up the structureof my thesis?

5-2-2013 thesis structure Google [178] [39]

How to keep a literaturesearch log

28-2-2013 literature search log Google [186]

How are intelligence analy-sis operations executed?

12-2-2013 ( intelligence ORinlichtingen ) AND (

analysis OR operations )

Scopus andGoogle

[69][5][177] [8][172]

What are the tasks of theMIVD by Dutch law?

5-3-2013 wet inlichtingenveiligheidsdiensten

Google [113]

How to explain the IDEF0method?

14-3-2013 IDEF0 Google (thenvia theIDEF0

Wikipediabibliography)

[53] [58]


258

Table K.2 – Continued from previous pageQuestion of search Date Search words Search engine Relevant

results

What is the position ofintelligence in the Data-Information-Knowledge-Wisdom Hierarchy?

18-3-2013 (intelligence ANDdefnition) OR (DIKWAND pyramid AND

definitions)

Scopus andGoogle

[96] [95][2]

Which intelligence gath-ering principles does theMIVD define?

18-3-2013 HUMINT AND SIGINTAND list AND/OR MIVD

Google [73] [80][29] [31,30]

Can we underline the im-portance of managing ex-pectations of Agent-Basedmodeling?

26-3-2013 misuse of modeling Scopus andGoogle

[85] [22]

259


[156] R.L. Ackoff. From data to wisdom. Journal of Applied Systems Analysis, 16:3–9, 1989.

[157] D. Appell. The new uncertainty principle. Scientific American, 284(1), January 2001.

[158] Jan Bakker. Definitie van inlichtingen - postionering van een eeuwenoude maar nog steedscruciale functie in een moderne omgeving. NISA Intelligence, October 2012.

[159] Alan Bryman, Saul Becker, and Joe Sempik. Quality criteria for quantitative, qualitativeand mixed methods research: A view from social policy. International Journal of SocialResearch Methodology, 11(4):261–276, 2008.

[160] K. Carlson. Strategic planning and models of reality, 2013.


[162] Lars-Erik Cederman. Endogenizing geopolitical boundaries with agent-based modeling.Proceedings of the National Academy of Sciences of the United States of America, 99(Suppl3):72967303, 2002.

[163] Lars-Erik Cederman. Generating state-size distributions: A geopolitical model.Manuscript, Center for Comparative and International Studies, Zurich, Switzerland, 2003.

[164] Lars-Erik Cederman. Modeling the size of wars: from billiard balls to sandpiles. AmericanPolitical Science Review, 97(01):135150, 2003.

[165] Lars-Erik Cederman. Agent-based models of geopolitical processes, June 2004.

[166] Lars-Erik Cederman. Articulating the geo-cultural logic of nationalist insurgency. NewHaven, April 2004. Department of Political Science - Yale University.

[167] Lars-Erik Cederman. Emergent behavior in the international political systems, September2007.


[169] US Central Intelligence Agency. INTelligence: human intelligence, 2010.

[170] US Central Intelligence Agency. INTelligence: signals intelligence, 2010.

[171] US Central Intelligence Agency. INTellingence: open source intelligence (sic), 2010.

[172] L. Cline. Operational intelligence in peace enforcement and stability operations. Interna-tional Journal of Intelligence and CounterIntelligence, 15:179–194, 2002.

260

[173] Robert A. Day. How to Write & Publish a Scientific Paper: 5th Edition. Oryx Press, 5edition, June 1998.

[174] US Air Force. ICAM architecture part II-Volume IV - functioning modeling manual(IDEF0). Technical Report AFWAL-TR-81-4023, US Materials Laboratory, Air ForceWright Aeronautical Laboratories, Air Force Systems Command, Wright-Patterson AirForce Base, Ohio, June 1981.

[175] Luc Girardin and Lars-Erik Cederman. A roadmap to realistic computational models ofcivil wars. In Advancing Social Simulation: The First World Congress, page 59, 2007.

[176] Varun Grover and William J. Kettinger. Process Think: Winning Perspectives for BusinessChange in the Information Age. Idea Group Pub, December 1999.

[177] Barry M. Horowitz and Yacov Y. Haimes. Risk-based methodology for scenario track-ing, intelligence gathering, and analysis for countering terrorism. Systems Engineering,6(3):152–169, 2003.

[178] M. Kastens, S. Pfirman, M. Stute, B. Hahn, D. Abbott, and C. Scholz. How to write athesis, 2013.

[179] Stphane Lefebvre. A look at intelligence analysis. International Journal of Intelligenceand CounterIntelligence, 17(2), 2004.

[180] Yvonna S. Lincoln and Egon Guba. Naturalistic Inquiry. SAGE, April 1985.

[181] MIVD. Jaarverslag MIVD 2011. Technical report, MIVD, Den Haag, 2011.

[182] T. O’Connor. Intelligence collection, 2013.

[183] Orrin H. Pilkey and Linda Pilkey-Jarvis. Useless Arithmetic: Why Scientists Can’t Predictthe Future. Columbia University Press, 2007.

[184] J. Rowley. The wisdom hierarchy: representations of the DIKW hierarchy. Journal ofInformation Science, 33(2):163–180, February 2007.

[185] Jennifer E. Rowley and John Farrow. Organizing Knowledge: An Introduction to ManagingAccess to Information. Ashgate Publishing, Ltd., April 2008.

[186] Capella University. Your literature review plan - literature review tutorial, 2012.

[187] Ministerie van Algemene Zaken, Ministerie van Binnenlandse Zaken en Koninkrijksrelaties,Ministerie van Defensie, and Ministerie van Justitie. Wet op de inlichtingen- en veilighei-dsdiensten 2002 (wiv). Technical Report Wiv 2002, Staatscourant, Den Haag, February2002.

261

K.4 Literature log of provided/suggested sources

Table K.3: Literature log of provided/suggested sources

Provided /Suggested by

Why How Date Results

Dr.ir. IgorNikolic, second

supervisor of thethesis

To provide formalisedguidelines to create an

Agent-Based model

Provided the bookduring supervision

meeting

6-12-2012 [38]

Prof.dr.Lars-ErikCederman

To show state-of-art andguide us through literature

Provided documentsvia e-mail [I.2]

28-1-2013 [26] [28][191]

Prof.dr.Lars-Erik

Cederman andDr. Andrea

Ruggeri

To guide us throughrelevant literature

Suggested the nameBhavnani in theire-mails [I.2] [I.3]

5-2-2013 [205] [92][10] [190]

Dr. AndreaRuggeri


Suggested the nameWeidmann in e-mail

[I.3]

5-2-2013 [209] [202][214] [194][118] [216][215] [196][120] [119][57] [121]

Drs. Thomas deZoete

To provide with details howintelligence analysis is/can

be executed

Provided documentvia e-mail after a call

5-2-2013 [221] [111]

Dr. Giliam deValk


Suggested a set ofinteresting studies

during first interview[H.5]

13-2-2013 [35] [44]

Dr. Giliam deValk

To guide us throughrelevant state-of-practice

Based on contact withexperts in the field hee-mailed [I.4] to show

us two softwareprograms used in

practice that use amethod similar to

Agent-Based modeling

17-2-2013 [86] [6]


262

Table K.3 – Continued from previous pageProvided /

Suggested byWhy How Date Results

Dr. Giliam deValk

To provide us with detailshow currently intelligence

analysis is executed

Provided hard copydocuments during thesecond interview [H.6]

22-2-2013 [99] [77][112]

Dr. Giliam deValk

To indicate the theoreticorigin behind the

categorization of hypothesesin intelligence, he indicated

the name Heuer

This is mentionedboth in his lectures[112, p.3 (1)] as well

as in one of theinterviews with Dr.

Gilam de Valk[H.7.2.7]

22-3-2013 [204][61, 62]

Dr.ir. Coen vanGulijk

To provide a comprehensivesource of knowledge on

security risk management

Suggested duringsupervision meeting

18-4-2013 [103]

Dr. Giliam deValk

To provide us with insightson how probabilities are/should be handled inintelligence processes

Suggested duringvalidation interview

6-6-2013 [203]

263


[188] Autonomy. A different approach, 2013.

[189] Ravi Bhavnani and Hyun Jin Choi. Modeling civil violence in afghanistan: Ethnic geog-raphy, control, and collaboration. Complexity, 17(6):42–51, July 2012.

[190] Ravi Bhavnani and Karsten Donnay. Heres looking at you: The arab spring and violencein gaza, israel and the west bank. Swiss Political Science Review, 18(1):124–131, March2012.

[191] Lars-Erik Cederman. Agent-based modeling in political science. The Political Methodolo-gist, 10(1):16–22, 2001.

[192] Lars-Erik Cederman. Articulating the geo-cultural logic of nationalist insurgency. Order,conflict, and violence, page 242270, 2008.


[194] Lars-Erik Cederman, Nils B. Weidmann, and Kristian Skrede Gleditsch. Horizontal in-equalities and ethnonationalist civil war: A global comparison. American Political ScienceReview, 105(03):478–495, July 2011.

[195] Hart Rudmann Commission. New world coming: American security in the 21st century.Technical report, Departmen of Defense (United States), Washington D.C., September1999.

[196] Kathleen Gallagher Cunningham and Nils B. Weidmann. Shared space: Ethnic groups,state accommodation, and localized conflict1. International Studies Quarterly, 54(4):1035–1054, December 2010.

[197] Koen H. van Dam, Igor Nikolic, and Zofia Lukszo, editors. Agent-Based Modelling ofSocio-Technical Systems. Springer, 2013 edition, October 2012.

[198] Kevin Dutton. The Wisdom of Psychopaths: What Saints, Spies, and Serial Killers CanTeach Us About Success. Scientific American / Farrar, Straus and Giroux, October 2012.

[199] Kristian Skrede Gleditsch and Nils B. Weidmann. Richardson in the information age:Geographic information systems and spatial data in international studies. Annual Reviewof Political Science, 15(1):461–481, June 2012.

[200] Richards J. Heuer. Limits of intelligence analysis. Orbis (Philadelphia), 49(1):7594, 2005.

[201] Richard J. Heuer Jr. Psychology of intelligence analysis. US Government Printing Office,1999.

264

[202] Dominic D. P. Johnson, Nils B. Weidmann, and Lars-Erik Cederman. Fortune favours thebold: An agent-based model reveals adaptive advantages of overconfidence in war. PLoSONE, 6(6):e20851, June 2011.

[203] Sherman Kent. Words of estimative probability central intelligence agency, 1964.

[204] Charles A. Mangio and Bonnie J. Wilkinson. Intelligence analysis: Once again. Technicalreport, DTIC Document, 2008.

[205] D. Miodownik and R. Bhavnani. Ethnic minority rule and civil war onset how identitysalience, fiscal policy, and natural resource profiles moderate outcomes. Conflict Manage-ment and Peace Science, 28(5):438–458, November 2011.

[206] NATO and Euro-Atlantic Partnership Council. Generic Early Warning Handbook, volume(2001)2. NATO, Brussels, 2001.

[207] PRNewswire. DATOPS presentation, 2013.

[208] Bhavnani Ravi, Dan Miodownik, and Jonas Nart. REsCape: an agent-based frameworkfor modeling resources, ethnicity, and conflict. The Journal of Artificial Societies andSocial Simulation, 11(27), 2008.

[209] Sebastian Schutte and Nils B. Weidmann. Diffusion patterns of violence in civil wars.Political Geography, 30(3):143–152, March 2011.

[210] Dutch Defense Intelligence & Security Service and Netherlands Defence Academy. Militaryintelligence: Indicator based warning intelligence, 2012.

[211] Julian Talbot and Miles Jakeman. Security Risk Management Body of Knowledge. Wiley,2nd updated edition, August 2009.

[212] Gilliam Valk de. Dutch intelligence - towards a qualitative framework for analysis : withcase tudies on the Shipping Research Bureau and the National Security Service (BVD).PhD thesis, Rijksuniversiteit Groningen, Groningen, October 2005.

[213] Gilliam Valk de and Willemijn Aerdts. Lectures of the course qualitative analysis methodsof the minor intelligence studies, 2012.

[214] N. B. Weidmann. Geography as motivation and opportunity: Group concentration andethnic conflict. Journal of Conflict Resolution, 53(4):526–543, May 2009.

[215] N. B. Weidmann and M. Duffy Toft. Promises and pitfalls in the spatial prediction ofethnic violence: A comment. Conflict Management and Peace Science, 27(2):159–176,April 2010.

[216] N. B. Weidmann and M. D. Ward. Predicting conflict in space and time. Journal ofConflict Resolution, 54(6):883–901, July 2010.

[217] Nils Weidmann and Christoph Zuercher. How wartime violence affects social cohesion:The spatial-temporal gravity model. In APSA 2011 Annual Meeting Paper, 2011.

[218] Nils B. Weidmann. Violence from above or from below? the role of ethnicity in bosniascivil war. The Journal of Politics, 73(04):1178–1190, August 2011.

[219] Nils B. Weidmann and Idean Salehyan. Violence and ethnic segregation: A computationalmodel applied to baghdad. Int Stud Q, 2011.

265

[220] J. Wucherpfennig, N. B. Weidmann, L. Girardin, L.-E. Cederman, and A. Wimmer. Po-litically relevant ethnic groups across space and time: Introducing the GeoEPR dataset.Conflict Management and Peace Science, 28(5):423–437, February 2011.

[221] T.S. Zoete de. Alpha proof, beta check? (v1.1). PhD thesis, University Leiden, Leiden,December 2012.

K.5 Literature log of news / topicality articles

Table K.4: Literature log of news / topicality articles

News source Topic Period Key words Relevantresults

De Volkskrant Mali 11-1-2013 -13-1-2013

Mali [231] [229]

Het Parool Mali 11-1-2013 -13-1-2013

Mali [230] [243]

NRCHandelsblad

Mali 11-1-2013 -13-1-2013

Mali [240] [239][233]

Trouw Mali 11-1-2013 -13-1-2013

Mali [232] [244][252] [251]

NRC-Next Arab spring 11-1-2013 -28-2-2013

Arab AND ( spring OR syriaOR sahel OR ( arab AND

protest ))

[247] [227][225] [248][257] [242][246] [258]

NRC-Next Mali 11-1-2013 -28-2-2013

mali NO football [236] [254][223] [222][253] [226][238] [224][255] [237][241] [245]

[256]

NRC-Next Other 11-1-2013 -28-2-2013

war OR conflict ORgeo-political OR protests OR

intelligence service

[234] [235][259] [249]

Stratfor:Security

Weekly (Freepart)

Local securitydynamics

21-1-2013 -28-2-2013

mali OR rebels [250] [228]

266


[222] AP and REUTERS. Grondoorlog in mali begonnen. NRC Next, January 2013.

[223] P. Bax. De malinese woestijn is leeg en heet. perfect voor de jihad. NRC Next, January2013.

[224] T. Beemsterboer. Grenzen betekenen niets in de sahara. NRC Next, January 2013.

[225] T. Beemsterboer. Nu de rook is opgetrokken begint de crisis pas echt. NRC Next, January2013.

[226] T. Beemsterboer. Waarom mali wel en syri niet? NRC Next, January 2013.

[227] T. Beemsterboer, C. Roelants, and P. Vermaas. Tweede front in de sahara. NRC Next,pages 4–5, January 2013.

[228] M. Bey and S. Tack. The rise of a new nigerian militant group. Stratfor: Security Weekly,February 2013.

[229] K. Broere. Franse troepen beginnen aan interventie in mali. De Volkskrant, January 2013.

[230] Buitenland. Fransen vechten in mali mee tegen extremisten. Het Parool, January 2013.

[231] Buitenland. Islamitische rebellen boeken zege op leger; mali. De Volkskrant, January 2013.

[232] Buitenland. Rebellen stoten door richting centraal-mali. Trouw, January 2013.

[233] In het nieuws. Franse luchtaanval tegen rebellen mali. NRC Handelsblad, January 2013.

[234] H. Hofland. Oorlog voer je achter je laptop. NRC Next, February 2013.

[235] M. Koning de. Gezond, maar straatarm. NRC Next, February 2013.

[236] K. Lindijer. Afghanistan, maar dan vlakbij huis. NRC Next, January 2013.

[237] K. Lindijer. Alles gaat kapot in timboektoe. NRC Next, January 2013.

[238] K. Lindijer. De vijand is nog onder ons. NRC Next, January 2013.

[239] K. Lindijer. Franse leger moet doen wat bewind mali niet lukt. NRC Handelsblad, January2013.

[240] K. Lindijer. Fransen sturen troepen naar noorden mali. NRC Handelsblad, January 2013.

[241] K. Lindijer. Mali worstelt met aangewakkerde verdeeldheid. NRC Next, February 2013.

[242] S. Nakhoul. Niet even een blokje om. NRC Next, January 2013.

[243] Nieuws. Steun voor frans besluit. Het Parool, January 2013.

267

[244] Podium. Moslimextremisme bloeit, met dank aan de arabische ’Lente’. Trouw, January2013.

[245] C. Roelants. Al-qaeda kiest voor charmeoffensief. NRC Next, February 2013.

[246] C. Roelants. Arabische opstanden: twee stappen vooruit, een terug. NRC Next, February2013.

[247] C. Roelants. De roep om in te grijpen is verstomd. NRC Next, pages 4–5, January 2013.

[248] C. Roelants. Lente is feest voor extremisten. NRC Next, January 2013.

[249] M. Schinkel. Hoe schaliegas de wereld op z’n kop zet. NRC Next, February 2013.

[250] S. Stewart. The unspectacular, unsophisticated algerian hostage crisis. Stratfor: SecurityWeekly, January 2013.

[251] Vandaag. Luchtmacht frankrijk in actie in mali. Trouw, January 2013.

[252] De Verdieping. Mali als dreiging. Trouw, January 2013.

[253] P. Vermaas. Frankrijk vreest voor een langdurige oorlog in mali. NRC Next, January 2013.

[254] P. Vermaas. In parijs is nu een leider opgestaan. NRC Next, January 2013.

[255] P. Vermaas. Oorlog maakt fransen trots. NRC Next, January 2013.

[256] P. Vermaas. Ze zijn hier nog wel even. NRC Next, February 2013.

[257] B. Vermeulen. Altijd wel iemand om tegen te vechten. NRC Next, February 2013.

[258] B. Vermeulen. Het spoor dat alle oorlogen heeft gezien. NRC Next, February 2013.

[259] Y. Vriesinga. Het gaat bergafwaarts met venezuela. NRC Next, February 2013.

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