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2012 43: 85 originally published online 1 February 2011Simulation GamingSebastiaan A. Meijer, Igor S. Mayer, Jelle van Luipen and Natasha Weitenberg

Modes of OrganizationGaming Rail Cargo Management: Exploring and Validating Alternative

  

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1Delft University of Technology, Delft, the Netherlands2ProRail, Utrecht, the Netherlands

Corresponding Author:Sebastiaan A. Meijer, Faculty of Technology, Policy and Management, Delft University of Technology, Jaffalaan 5, 2628 BX Delft, The Netherlands Email: sebastiaan.meijer@tudelft.nl

Gaming Rail Cargo Management: Exploring and Validating Alternative Modes of Organization

Sebastiaan A. Meijer1, Igor S. Mayer1, Jelle van Luipen2, and Natasha Weitenberg2

Abstract

Stakeholders in the Netherlands’ rail cargo sector exhibit strategic behavior that causes irregularity and unpredictability in freight trains. This leads to the suboptimal use of scarce rail capacity. The authors present the results of a research project that used gaming to explore and validate alternative organizational methods for the management of rail cargo capacity with decision makers and subject matter experts from ProRail, the Netherlands’ railway infrastructure manager. Various scenarios for the organization of rail cargo capacity management were played out, tested, and extensively debriefed in three project phases. The gaming sessions demonstrated that open information sharing among stakeholders does not depend on the introduction of price mechanisms and is, indeed, a more effective way of managing capacity. The authors conclude that it is vital to introduce gaming gradually and build up organizational acceptance for this method. However, once acceptance has been achieved, gaming can generate valuable insight into strategic behavior and the performance of sociotechnical infrastructures.

Keywords

allocation, capacity management, complexity, construct validity, external validity, gaming phases, gaming simulation, market mechanism, open information sharing, organizational acceptance, planning alternatives, policy experimentation, policy making, policy research, ProRail, rail cargo, rail transport, real-world impact, scenarios, shared meaning, sociotechnical infrastructures, strategic behavior, trust

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In a densely populated country like the Netherlands, the demand for rail transport is high, but space is at a premium. The Netherlands’ rail infrastructure is one of the most intensively used railway networks in the world. It is therefore important to optimize the use of the existing rail infrastructure. Passenger and freight trains operators in the Netherlands are commercial companies; passenger trains are operated chiefly by Netherlands Railways, a privatized incumbent. Rail cargo on the other hand is operated by a large number of rail cargo companies. They serve many companies that want their cargo shipped by rail, for instance, between Germany and the port of Rotterdam. Rail infrastructure is publicly owned and operated by the Netherlands’ railway infrastructure manager, ProRail. One of the tasks of ProRail is to manage the capacity of the rail infrastructure—by scheduling train paths and managing slots, for example. The tasks and planning procedures of ProRail are laid down in legislation.

The Rail Cargo Capacity Management ProblemIn order to make better use of the existing rail infrastructure, ProRail deemed it necessary to improve the organization and technical systems for the planning and control of rail infrastructure capacity. In 2008, ProRail launched an ambitious project titled Ruimte op de Rails (“Room on the Rails”), which aims to double rail capacity before 2020 using only 10% of the investment budget that was initially thought necessary. The improve-ment of rail cargo capacity management plays a crucial role in this ambition. Rail cargo accounts for only a small percentage of the trains that use the rail infrastructure network every day. However, this small percentage demands a great deal of work on the part of ProRail due to its special characteristics. First of all, freight trains are slow and heavy, so stops should be minimized. Conflicts with other trains, especially passenger trains that run on the same track, occur frequently.

Second, freight trains cannot be fitted well into the ProRail’s yearly planning cycle. We will attempt to explain this complicated process in simple terms. Currently, freight train operators must apply for so-called paths about 1 year in advance. ProRail then attempts to fit all these applications, from both passenger and freight trains, into their schedules. After some months, the results of the scheduling process are fed back to the operators. In the subsequent phase, it is possible to apply for new paths, but these will only be granted when capacity is available and on a “first come, first served” basis. Five days before departure, the planning moves from ProRail’s capacity management department to its control depart-ment. Requests for late changes can still be made but will only be granted when possible. One of the problems is that only a small proportion of freight trains can be planned a year in advance. These are trains that run regularly—every week at a certain time, for example. However, a major proportion of freight trains do not run regularly, but in an erratic fashion, as requests from companies follow economic realities. These trains are included in the yearly scheduling on the basis of best guesses. A few weeks or months before scheduled departure, many requests for adjustments are made. Days and hours before the scheduled departure, the control process can become quite chaotic due to last-minute changes. This ad hoc phase can give rise to planning conflicts as well as unused time slots. In other words, it detracts from the performance of the rail infrastructure as a whole.

Meijer et al. 87

The Use of Gaming

Due to the sociotechnical complexity of rail cargo capacity planning, ProRail felt that gaming simulation could provide an effective method by which to understand the under-lying problems and, more importantly, to explore and validate alternative organizational methods. Since ProRail as an organization was somewhat unfamiliar with the method of gaming, Delft University of Technology (TU Delft) was invited to introduce gaming within ProRail and demonstrate its effectiveness for solving the problems outlined above. Our gaming trajectory was divided into three project phases:

1. Building organizational trust in gaming as an effective method to explore and validate capacity planning scenarios

2. Exploring the more extreme solution scenarios3. Validating the most promising and feasible solution scenarios

OutlineThe outline of this article is as follows. First, we will present the applied research question that we investigated for ProRail and the scientific research question. We will then describe the three gaming phases of the project. Next, we will present the results for each phase. Finally, we will present and discuss the answers to the applied and sci-entific research questions.

Research QuestionsApplied Research Question

The objective of the applied research project was to help optimize the performance of the rail infrastructure by exploring and validating the alternatives for planning rail cargo capacity. ProRail had been studying the problems and potential solutions before the project started and had already generated a number of possible solution scenarios. One of the alternatives was the introduction of market mechanisms to better align the supply of and demand for slots. Another option was the introduction of a flexible pricing system similar to the price mechanisms used for airplane tickets sales, with discounts for early bookers. In short, ProRail’s applied research question was therefore,

Could the introduction of market and flexible pricing mechanisms improve the performance of the rail infrastructure network, and if so, what would the consequences be?

Academic Research QuestionThe project also provided a unique opportunity to study the underlying problems and alternative solutions to capacity planning in infrastructure systems. Capacity management

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is vital to all network-bound infrastructures, such as air and road transportation or energy networks (ten Heuvelhof, Koolstra, & Stout, 2001). In The Next Generation Infrastructures research program, we posit that gaming is an effective method of studying some of the phenomena, such as strategic behavior, that are associated with the sociotechnical com-plexity of infrastructures. At the same time, gaming enhances learning, management, and policy making in complex infrastructures (Bekebrede & Mayer, 2006; Kuit, 2002; Kuit, Mayer, & Jong, 2005; Mayer, 2009; Mayer & Veeneman, 2002).

The ProRail case study provided a unique opportunity to try out and study the effec-tiveness of gaming simulation for real-world policy making in complex infrastructures and take it beyond the level of general learning, training, or teaching. Our scientific question was, therefore,

What is the effectiveness of gaming in studying the nature of complex, socio-technical policy problems in infrastructures and bringing about enhancements?

Gaming for Applied and Scientific Policy ResearchThe use of gaming simulation for testing hypotheses and the ex ante validation of policy options is not uncommon. Studies into the effectiveness of gaming for policy making vary greatly in the way they gather data. Qualitative and quantitative data can be col-lected for different purposes, at different levels, and with different methods of data collection. For instance, data can be collected to determine construct validity or player satisfaction, to acquire experimental data on the in-game performance of players or sys-tems, to gain insight into policy recommendations, or to assess the external validity of the policy lessons. In many cases, the collection of policy-relevant data through gaming is conducted in an ad hoc and single-level fashion (most often in terms of player satis-faction) and seldom in a systematic, multilevel manner. In order to structure our data-gathering approach in the case of ProRail, we developed a simple layered framework (Table 1) that we will use to present and discuss the results.

Level 1At the bottom level (Level 1), we position Internal Construct Validity: Is the simulation game a valid representation of (an aspect of) the system under consideration (in this case, “capacity management”)? In the game design literature, we find that this can be done through expert (face) validation, for instance, preferably with different types of experts (Duke & Geurts, 2004; Kortmann & Harteveld, 2009).

Level 2Once the internal construct validity has been assessed, a simulation game can be used for Experimental or Quasi-experimental Policy Research, or simply Policy Experimen-tation (Level 2 in Table 1). This implies that one or more policy proposals (scenarios) can be tried out in the gaming simulation before the policy proposals are implemented.

Meijer et al. 89

Qualitative and/or quantitative data need to be collected on how the scenarios perform in the simulation game, preferably on the basis of policy research hypotheses (Meijer, 2009; Noy, Raban, & Ravid, 2006; Roelofs, 2000; Meijer, Hofstede, Omta, & Beers, 2008). This can be realized by, for instance, using in-game performance data logged by a computer model (accounting system) and also through systematic, independent observa-tions or a form of cross-impact assessment by the participants during or after the game.

Level 3At this level, the game participants create shared meaning about what the in-game results and their in-game experiences imply for problems and solutions in the real world. This can be called “transfer” and usually takes place during intermediate or final debriefings (Duke, 1980; Duke & Geurts, 2004). All kinds of data-gathering methods (questionnaires, electronic meeting systems, etc.) can be used for “transfer,” but it is usually based on written or audiovisual records of the dialogue facilitated between the players.

Level 4Throughout the game, the participants create a solution space that needs to relate closely enough to the world outside and after the game. At Level 4, the external validity of the

Table 1. Gathering Policy-Relevant Data: Different Levels

Level Title Explanation Data Gathering

5 Real-world impact

Do the insights and results of a game intervention have an impact on the real world in terms of which decisions are made?

Case studies, longitudinal studies, interviews, etc.

4 External validity Are the insights, outcomes of the game a valid (reliable, adequate) representation of the real-world problem and solutions, including for nonparticipants?

Interviews, debriefings with key decision makers, subject matter experts, etc.

3 Shared meaning Interpretation and knowledge construction by participants in the game of the in-game results in terms of the real-world problem and solution.

Debriefing with participants, questionnaires

2 Policy experimentation

Assessment of the in-game performance of the system (e.g., ex ante policy proposals, scenarios)

Qualitative and quantitative in-game data (e.g., computer model output)

1 Construct validity

Is the game a valid (reliable, adequate) representation of (an aspect of) the real-world problem?

Expert/face validation. Triangulation with participants, client, subject matter experts, etc.

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game-based intervention is addressed. This is the point where the players go back to their daily routine and interact in the real world with nonplayers and where the solution space that has been identified in the game needs to be recreated in the real world. Players and nonplayers are immediately confronted with the question of external validity: Did the game-based intervention frame the problem correctly, at the right time, with enough author-ity, and are the conclusions drawn still valid (feasible and trustworthy) given changes in the context? Data can be gathered by means of interviews and follow-up meetings, debrief-ings with player and nonplayer stakeholders, decision makers and subject matter experts.

Level 5At Level 5, the actual impact of the game-based intervention on the real world is assessed. This is usually done by means of reconstructive or longitudinal case studies, using theoreti-cal concepts to refine insight, and documents and interviews to collect data. Examples of such evaluation studies are scarce, but some notable examples are de Caluwé and Geurts (1999), Altamirano and de Jong (2009), and Altamirano, Herder, and de Jong (2008).

Method: Gaming in Three Project PhasesThe joint ProRail and TU Delft team chose a gradual, phased approach that involved introducing gaming in three phases. In the first phase, we needed to build up trust within the ProRail organization and establish whether gaming could really work within this context (Level 1: construct validation). For instance, we wanted to know whether the ProRail stakeholders were actually willing to accept a gaming simulation to produce solutions to their problems and trust the results. In the second phase, we explored four of the more radical scenarios for rail cargo capacity management with the ProRail stake-holders (Levels 2 and 3). In the third phase, we validated three of the most promising and feasible solutions while using a mock-up of a real capacity–planning software tool called DONNA, which was about to be implemented at ProRail (Figure 1; Levels 2, 3, and 4).

Phase 1: Building trustIn Phase 1, TU Delft designed a rough but playable, paper-based prototype game. This modeled the core of the problem but in an abstract fashion without using realistic data

Figure 1. Gaming in the three project phases

Meijer et al. 91

or real train paths. The goal here was to demonstrate to the ProRail stakeholders that gaming can reveal the hidden rules of a complex system. In other words, we aimed to create and test construct validity (Level 1 in Table 1).

In November 2008, the session attracted 15 participants from within ProRail. The outcome of the session was positive, in terms of both the involvement of the participants and the insight gained into the complexity and hidden rules of the system. The gaming approach was accepted without reservations, and the project team was given the go-ahead for the second phase. Meanwhile, the game designers received significant feedback on the initial game design from the participating experts. This feedback was used to construct a second version of the game and also to increase the level of involvement and commit-ment within the organization.

Phase 2: ExplorationThe second game took place in January 2009. The objective was to explore more radical alternatives, such as auctions, through which market and pricing mechanisms could be introduced into the allocation of capacity. In other words, the assessment of this gaming session was aimed at acquiring experimental data about how various solution scenarios might perform (Level 2 in Table 1) and at constructing meaning among the stakeholders about real-world solutions (Level 3 in Table 1).

The gaming simulation remained an abstract, pen-and-paper game using a stylized board to represent the railway network (Figure 2), as it had been in Phase 1. The following were the design requirements for the game in the second phase:

Figure 2. The game board of Session 2

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• playable with experts on train capacity allocation • focus on the interaction process between ProRail and rail cargo transport

companies. • division in yearly planning cycle: adjustments phase (8-2 months) and rolling

phase (5-0 days in advance) • simple network, play at an abstracted level • Short development time

The second game consisted of four scenarios, which were seen as somewhat radical methods of introducing market mechanisms and flexible pricing mechanisms into rail capacity management:

• Baseline scenario: the rail infrastructure manager operates as a black box, with fixed prices.

• Open information scenario: the rail infrastructure manager provides train paths, which the cargo transporters can claim in an open format (still with fixed prices).

• Guided prices scenario: the rail infrastructure manager coordinates train paths with a variable price structure.

• Auction scenario: the rail infrastructure manager auctions train paths to the highest bidder.

A total of 24 participants played the roles of ProRail, four rail cargo transporters (called Mega-, Mono-, Maxi- and MiniRail), and one client. Quantitative in-game data were gathered through an in-game computer system, which was used for accounting, and through questionnaires (which used a 7-point Likert-type scale). Qualitative data were gathered through observations and by recording the debriefing discussions among the players about the results and performance of the four scenarios.

Phase 3: Design of Version 3On the basis of the results from Phase 2, the project team selected the most promising and realistic solutions to design a third version of the game. The project team set the following design requirements:

• a higher level of realism than the previous games—through the introduction of more clients, for example (this increase in realism should enable the exter-nal validation foreseen in Level 4 of Table 1)

• more train paths per day, improving the quantitative analysis • the use of a computer-supported planning tool to shorten the planning cycles • the use of a real planning system based on the DONNA system, which was

recently introduced within ProRail to improve capacity planning. • a sharper distinction between the three phases of planning

Meijer et al. 93

• the introduction of a disincentive to planning trains that arrive too early • the introduction of competition from other modes of transport, such as road

and inland shipping • the introduction of competition for slots with passenger transport

These design requirements led to revisions in the design of the game, with a central plan-ning computer operated by ProRail and two passive computers through which the participants could check the status of the network. The network was similar to the one used in Phase 2 but with six departure stations and six destinations. Three solution scenarios were defined:

• Expensive peak hours. By demanding higher prices during busy hours, Pro-Rail could incentivize freight transporters to avoid these hours.

• Allocate it yourself. The rail cargo transporters cooperate to fill the capacity themselves, with ProRail as facilitator and conflict solver.

• To reserve is to go. Capacity is reserved for freight trains in the annual plan-ning cycle. This can be claimed by rail cargo transporters until a relatively short time before it. However, claiming a path means that you have to run a train in that path and pay for it.

The third gaming session was held in April 2009 with 16 participants who played the roles of ProRail, four rail cargo transporters, three clients (JIT-It, Move-It, and Dump-It) with varying requirements in terms of punctuality, a road transporter and a passenger transporter (see Table 2). Data were gathered at Levels 2, 3, and 4 of Table 1. Levels 2 and 3 used the same sources of data as in the previous game. Level 4 used an extended debriefing after the whole day of gaming.

ResultsSession 1: Building Trust

The objective of the first phase was to build trust in gaming within the organization and assess how and in what form it could be used to explore and solve ProRail’s prob-lems. Both these objectives were achieved to a surprising extent, considering the fact that the problem and solutions were politicized. Initial skepticism was overcome fairly swiftly; the players became involved and indicated that even the first preliminary game prototype offered significant insight into the problems and potential solutions faced by ProRail. On the basis of this positive experience, the second phase of the project was given the “go-ahead.” The construct validity proved adequate for decision makers and experts at ProRail. For the game designers, the session produced useful feedback on playability. One of the most notable pieces of feedback for improving the design of the game was the need to visualize the progression of playing time and simulated time in order to give the players more control of the planning process.

94 Simulation & Gaming 43(1)

Session 2: ExplorationThe objective of the second phase was to explore the effectiveness and implications of four more radical scenarios for capacity management. The main outcomes are represented in Tables 3 and 4. Table 3 shows to what extent the slot requests were effectuated by the players (i.e., to what extent trains departed and arrived on time) as a percentage of the total number of slot requests for each client. Percentages are shown for the individual cargo companies represented in the game (Roles a-d) and the network as a whole.

The results indicate that the baseline scenario (1) and, to a lesser extent, the auction scenario (4) performed the worst in terms of network capacity usage. It should be noted here that the “capacity puzzle” was solvable in the game. In other words, an optimal solution designed by the rail network manager in the game could lead to 100% efficiency and all trains arriving punctually. To a large extent, suboptimal capacity allocation was due to strategic behavior by the companies, such as cargo transporters who requested more paths than they needed or different paths to those needed, in order to optimize their own interests and flexibility.

Table 2. Overview of the Three Games Played in the Three Project Phases

Game ElementPhase 1: Building

TrustPhase 2: Exploration of Extreme Scenarios

Phase 3: Validation of Solutions

Transporters MegaRail, MonoRail, MiniRail, MaxiRail

MegaRail, MonoRail, MiniRail, MaxiRail

MegaRail, MonoRail, MiniRail, MaxiRail

Clients Three, all equal One, centralized Three: JIT-It, Move-It, Dump-It

Network manager Three people, all equal

Seven people: Four planning, one maintenance, one relations manager

Four people: two planning, one maintenance, one relations manager

No. of scenarios 1 4 3Variables on path request form

Round (scenario number)

Round (scenario number)

Round (scenario number)

From location From location From location To location To location To location (= client) Transporter Transporter Transporter Price of path Price of path ClientVariables on client request form

— — Round (scenario number)

From location To location Transporter Client Price (on time) Price (not on time)

Meijer et al. 95

Table 4 presents the results of the discussions with the players after each game round. The table shows that none of the scenarios scored highly on all aspects. The baseline scenario (1) performed the worst, whereas the open information (2) and guided price scenarios (3) performed the best. These results were used by the project team to identify and list the most promising and feasible solutions so that these could be tried out and validated in Phase 3. The results were as follows:

• information more important than price (all scenarios rely on open information) • cooperation between cargo transporters in the annual planning allocation • keeping capacity reserved for cargo transport with late allocation requests • flexible pricing for predefined expensive hours • variations in the prices of cancellations and change requests

Session 3: DesignThe above solutions were used as input for the three scenarios that were explored and validated in Phase 3. Qualitative and quantitative data were gathered through the in-game computer model, questionnaires and group discussions among the players after each round of the game.

Table 3. Capacity Requests Effectuated (Percentage of Total Number of Requests; Phase 2)

Scenario/Role 1. Baseline 2. Open Information 3. Guided price 4. Auction

a. MegaRail 60 90 82 60b. MonoRail 75 75 80 40c. MiniRail 50 100 50 100d. MaxiRail 40 40 67 100Total rail network 57 76 75 68

Note: Bold indicates wished behavior.

Table 4. Qualitative Assessment of the Scenarios by the ProRail Players (Phase 2)

Scenario/Quality Aspect 1. Baseline 2. Open Info 3. Guided Price 4. Auction

Information about availability − − ++ Planning with margins − − ++ ++ Steering using incentives ++ ++ ++Prioritizing − − Directive role of RIMC − − − −Relation management − − ++ ++ Dysfunctional collaboration − − − −

Note: ++ means strongly positive; -- means strongly negative; all less clear outcomes have been left out.

96 Simulation & Gaming 43(1)

Figure 3 shows the development of the market share of the transporters in each of the three scenarios. Figure 4 shows the development of the price per train received by the transporters. The price drop for most transporters is explained by the fact that the clients realized quite suddenly that they should negotiate harder, and this put pressure on the market price. Figure 3 indicates that MegaRail (scripted as the largest transporter) increased its dominance in all three scenarios. In Scenario 1, MegaRail had the most trains (volume market share). In Scenario 3, MegaRail had to lower prices to gain an even larger volume (visible in the growing volume market share and steady turnover market share). MaxiRail and MiniRail were able to maintain their prices through good relations with their clients. They did not aim to expand their market share and left extra trains to MegaRail.

Table 5 presents the assessment of the three scenarios by the ProRail players in the discussions. The plus and minus signs indicate that Scenario 3 (‘to reserve is to go’) was evaluated the most highly overall. The role of ProRail in this scenario was to facilitate the rail cargo market between clients and transporters, unlike its central role in Scenario 1 and its purely administrative role in Scenario 2. During Scenario 3, the clients suddenly started to lower the prices they paid for transport. This did not put pressure on ProRail.

Figure 3. Market share of transporters in three scenarios (Phase 3)

Meijer et al. 97

It should be noted that the expensive peak hours in Scenario 1 did not deter cargo transporters from using these hours, even though this would have been technically possible. The network usage in all three scenarios in this session was optimal, since all requests could be processed, producing close to 100% use of the network. The small number of disruptions observed was due to user errors, such as forgetting to submit a request form. Scenario 3 relied on three assumptions, which ensured its functionality:

• the perfect availability of information for the transporters • the reservation of cargo capacity without specific claims in yearly planning • ProRail as the ultimate arbiter in conflicts

Data on the external validity of the game (Level 4 in Table 1) were gathered through an extensive expert debriefing after the third game session. The debriefing focused on

0

2

4

6

8

10

12

Scenario 1 Scenario 2 Scenario 3

Pro

fit

per

tra

in

Mono

Mega

Maxi

Mini

Figure 4. Profit per train in three scenarios (Phase 3)

Table 5. Qualitative Assessment of the Scenarios by ProRail Players (Phase 3)

1. Expensive Peak Hours

2. Allocate It Yourself

3. To Reserve Is to Go

Time required for planning (minutes)

69 (– –) 61 51 (++)

Pressure on ProRail High (but also start-up effect; – –)

Low, with peaks in workload

Low (++)

Role of ProRail Central Administrative (– –) Support (++)Dependability Low (– –) High (++) High (++)Overall satisfaction Mediocre (– –) High (++) High (++)Number of mutations High (– –) Low (++) Low (by design; ++)

98 Simulation & Gaming 43(1)

the question of whether and how the mechanisms that made Scenarios 2 and 3 successful could be translated into real-world solutions. The list of points that this yielded was ranked by the group. The top three rated items were the following:

1. The involvement of real stakeholders: As information sharing and coordi-nated action is a process that requires cooperation, real-world implementation should enjoy robust support from the real cargo transporters.

2. Steps were to be made in IT infrastructure to create an easy-to-use interface to search for available capacity and reserve the capacity yourself. (Note: this is already technically possible, but remains difficult to use.)

3. A study of the flexibility of cargo transport demand. ProRail currently focuses on cargo transporters, taking their requests for granted. Greater insight into the flexibility of the capacity requested should increase the scope for planning solutions.

Real-World Impact (Level 5)The real-world impact (Level 5 in Table 1) of the three game sessions was assessed by monitoring the policy-making process after the game sessions. The real-world impact was significant: many of the initial plans and ideas for introducing price and market mechanisms were halted. One concrete result of the gaming sessions was a ProRail initiative to proceed with the project of introducing market mechanisms, using other methods to arrive at solutions ready for immediate implementation. Whether this will get off the ground is the topic of much internal discussion at ProRail. Various parties within the organization have differing ideas about what is required. Some believe that involvement of real stakeholders would damage real-world relationships, while others believe that involving them would generate support. ProRail project members have undertaken various initiatives to play the successful scenario in demo sessions with senior levels of management. Each of these demo sessions was subsequently cancelled due to the political sensitivity of the topic. The real-world impact of the gaming project is therefore not to be found in its direct implementation but in the understanding gained by all the experts who participated and discussed it afterwards. Their conclusions affected the future policy making of ProRail, but this impact did not take the form of a series of projects based on the results of the gaming simulation but came, rather, as an implicit decision to halt further initiatives regarding the introduction of market mechanisms for freight train capacity management.

Conclusions and DiscussionResearch Questions

The three gaming sessions yielded a stream of insights and conclusions that were nar-rowed down into one scenario that functioned well. This scenario will need to be checked with the external stakeholders (i.e., cargo transporters) and developed in detail before

Meijer et al. 99

it can be implemented. The most significant conclusion from the gaming sessions was that ProRail did not actually need “market mechanisms” with flexible prices; ProRail and its clients need open information and communication about the available capacity. Another conclusion is that open communication and even cooperation between cargo transporters does not necessarily reduce ProRail’s coordinating power, provided that ProRail remains the ultimate decision maker.

The game sessions focused particularly on the computer support tool that visualized the essence of the DONNA planning system that was about to be implemented. A positive and unintended outcome of the gaming approach was that the participants became con-vinced by the new DONNA system. This also improved the chance that gaming may be used to support future IT development projects.

What the games have not yet explored in any depth are the power differences and internal cost drivers of the cargo companies. Neither have they addressed the geographi-cal differences between sections in the network. Some sections are used more intensively than others, and one corridor is even reserved exclusively for freight trains. International connections should be introduced. Future simulations could focus on these areas in order to make the step from the abstract to real-world implementation.

Research MethodWe have argued that gaming is an effective research method with which to analyze complex sociotechnical infrastructures in general and for answers to specific questions such as capacity management and strategic behavior. In order to use gaming for real-world policy support, a sufficient level of organizational support is required. We have learned that the “rules of the system”—such as the incentives, the lack of transparency, and the inflexibility of capacity management—trigger all kinds of strategic behavior that lead to suboptimal performance of the system. For instance, companies request more slots and train paths than they need. It is suspected, however, that the introduction of price incentives brings about new and other forms of strategic behavior that do not significantly increase performance. Transparency and open information have a greater effect on the performance of the system. In addition, we have indicated and demonstrated how gaming can be used, at what level, and what sort of policy-relevant data can be gathered. Further evaluation studies into the real-world impact and consequences of gaming for policy making are desirable.

Acknowledgment

The authors would like to thank Gert Jan Stolk and his colleagues of the TU Delft/CPS Gaming Street for their contribution to the production of the gaming software and support during the gaming sessions. We are grateful for the valuable comments received from two anonymous reviewers.

Authors’ Note

This article is a substantially augmented expanded rewrite of a paper: Meijer, S. A., Mayer, I. S., van Luipen, J., & Weitenberg, N. (2009, June). Gaming rail cargo capacity management: Exploring and validating alternative modes of organization. Paper presented at the 40th conference of the

100 Simulation & Gaming 43(1)

International Simulation and Gaming Association (ISAGA), organized by the Society of Simula-tion and Gaming of Singapore (SSAGSg), Yeo Gee Kin (National University of Singapore), and Cai Yiyu (Nanyang Technological University, Singapore), Singapore.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the authorship and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research and/or author-ship of this article:

The research presented in this article was conducted within the framework of the Next Gen-eration Infrastructures (NGI) program (http://www.nextgenerationinfrastructures.eu/). The research was funded by ProRail and NGI.

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Bios

Sebastiaan A. Meijer is an assistant professor at Delft University of Technology. He graduated at Wageningen University in gaming simulation as a research method in supply chains and net-works. He won the Deutschen Planspielpreis Sonderpreis 2010 for best European dissertation on gaming. His research focuses on collaboration between organizations without hierarchical relations in the domains of railways and supply chains. Contact: sebastiaan.meijer@tudelft.nl.

Igor S. Mayer is an associate professor in the faculty of Technology, Policy and Management at Delft University of Technology. He is also a director of CPS–TU-Delft/TPM Centre for Serious Gaming. He is a cofounder and board member of SAGANET—the Netherlands’ Simulation and Gaming Association—and a member of the Netherlands Institute of Government. Contact: i.s.mayer@tudelft.nl.

Jelle van Luipen is an innovator at ProRail, working on situation awareness in operations. He instigated many projects that include gamelike environments and developed his own multiactor and multilevel gaming tool called MATRICS. Contact: jelle.vanluipen@prorail.nl.

Natasha Weitenberg is a project manager at ProRail and is responsible for successful completion of projects on the edge of technology, business processes, and external stakeholders. Contact: natasha.weitenberg@prorail.nl.