The Evolution of Value and Inequality

Bradly Alicea

Orthogonal Research

KW: Inequality, Evolution, Modeling, Neuroimaging

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Summary

“The Evolution of Value and Inequality” involves applying a hybrid modeling approach

to the study of economic behavior. This approach, which is consistent with heterodox and

contemporary complex systems approaches, merges evolutionary dynamics with neurobehavioral

insights. In the context of the project, income inequality and economic value will both be

redefined in a manner more consistent with cognitive and cultural biases. Another focus of this

project will involve reconceptualizing the role that cumulative social interactions play in

determining patterns of persistent inequality. The evolutionary models used in this work are

generative and do not involve stochastic optimization. Such models also allow for a systematic

accounting of history and context, which is not necessarily possible using a single approach (e.g.

historiography or data mining).

The focus of this project will involve understanding both the plausible historical origins

of income inequality and re-evaluating the standard behavioral assumptions behind economic

modeling. Since this project is a non-standard approach to economic inquiry, micro- and macro-

economic phenomena can be evaluated within the same framework. While the hybrid model

component of this project is an abstraction of social organization, human decision- making, and

historical processes, the modeling outputs and big-picture lessons of this project are expected to

be highly relevant to real-world economic markets and public policy.

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Introduction

While most economists have traditionally taken causal [1] and comparative [2] views of

income inequality, it will be argued here that the study of social evolutionary dynamics would be

helpful in teasing out questions of inequality's origins. As a heterodox paradigm, this might

ultimately involve data as diverse as historical data, ethnographic data, evolutionary modeling,

and behavioral/neurophysiological data. In the end, we will be able to provide a conceptual

alternative to the intuition, moralizing, and argument to moderation that typically dominates

discussion at the intersection of sociobiology and economic change.

"The Evolution of Value and Inequality" is an attempt to recast the problem of income

inequality as an evolutionary and neurocognitive phenomenon. Using neuroimaging and

evolutionary modeling approaches, questions regarding inequality’s origins can more effectively

be addressed. A primary focus of this proposal will be that as a population of heterogeneous

agents, people will interact and construct hierarchies based on their differing traits, varying

abilities, and biases in their perceptual judgments. The hybrid algorithmic-biobehavioral

approach proposed here is more flexible than more traditional game-theoretic and rational

decision-making paradigms in that the former consider a broader range of possible behaviors,

including historical contingencies and cultural biases. These types of perspectives are lacking in

the current conversation, but are required in order to address the issue of how broad trends and

distributions of resources come to be over time. The broadness of the approach is necessary to

make the connection between the pure inferential approaches of evolutionary science and

potential public policy outcomes.

A secondary rationale for this project is to recast the general conversation of how and

under what conditions inequality arises without the usual rhetoric. Arguments that attempt to

explain income inequality as an outcome of human nature are often premised upon a series of

arguments from genetic determinism [3]. This project will try to move beyond such fallacious

arguments, both in terms of biologically-deterministic arguments and reducing everything to

singular causes. Assigning inequality to genes for poverty, wealth, or intelligence are

unsatisfying precisely because they do not address the interface between social interactions,

judgments (individual or collective), and individual abilities. This inclusive, multidisciplinary

approach is a core component of this project.

The focus of this project will be two-fold: to establish models for uncovering the

evolutionary dynamics of income inequality, and to establish a neuroimaging dataset that provide

potential neurological correlates of income inequality at the societal level. There are also three

big-question related correlates of inequality origins that we will focus on: initial distribution of

resources, lock-in with respect to the economic system of a given society, and self-reinforcing

nature of inequality. These are referred to as correlates because it is unclear as to the exact causal

relations and contributions. Some of these even act as feedbacks, which obscures the formal

causal structure even more.

Main Correlates

The first correlate that this project will uncover involves the relationship between income

inequality and resource patchiness. This is either been implicit or confused with the

accumulation of surpluses in our current understanding of how inequality emerges. It has also

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not been synthesized with decision-making in any meaningful way. From a historical standpoint,

geographical patchiness is an important component of settlement patterns and geographical

exchange networks [4, 5]. Yet decision-making theory often assumes only a simple asymmetry

of information, which is decontextualized from evolutionary dynamics. By approximating the

dynamics of cultural evolution, the individual and population levels we can also model possible

long-term effects of segregation and discrimination.

The second correlate involves valuation and argumentative lock-in [6]. Some sets of legal

arrangements and beliefs, the outcome of previous cultural evolution, essentially "locks-in" a set

of asymmetrical social relations [7]. This evolutionary contingency acts as constraints within

which the major features of a society's economic structure tend to evolve. While these outcomes

(such as unequal concentrations of wealth) tend to be the outcome of social complexity, they are

by no means immutable. The most interesting aspects of this are symbolic and cognitive, while

the rest of this involves the outcomes of bio-behavioral components of establishing trust [8],

dominance hierarchies [9] and the effects of conditioned defeat [10]. The evolution of valuation

is highly symbolic, and determines which things and objects are of value (or of great value

relative to everything else in society). This seems to be in direct conflict with a purely energetic

view of "economy", but can be mediated by the physiological correlates of status. Therefore,

another aim of this project is in establishing the extent of this interrelationship.

The third correlate involves self-reinforcing homogeneity on dynamic hierarchical

networks. As an adaptive process, social hierarchies can be viewed as networks [11] yield

increasingly sparse connectivity [12], which in turn contributes to the concentration of resources

and increasing income inequality. This process may or may not be a linear phenomenon. To

approximate both the initial and evolving conditions, trees and other network structures will be

used to model patterns of exchange across a population. These structures become directed and

acyclic (e.g. hierarchical) when one of two conditions is met: either exchanges between agents

are consistently asymmetrical, or connections between agents become consolidated by some

coordinating entity (e.g. corporation, government). There are at least three forms of adaptation

here: a social learning mechanism, a cultural evolutionary form of selection, and the

neurophysiological imperative of satiety. This gives us a fitness and selection criterion that is

related to the reproductive success of particular economic strategies within a population.

Subsequent evolutionary algorithms and simulations may bear out the evolutionary dynamics of

allied factors such as the assignment of value [13] and social stratification.

Aside from explaining the origins of inequality, this project might also address the role of

inequality as a causal factor. There seems to be a great deal of debate as to exactly how

inequality relates to negative (or positive) social outcomes. Given these vagaries, this project can

resolve the social architecture and causal structure of inequality relative to a constellation of

outcomes.

Approach to Problem

This project will define income and associated social inequality as the selective

perpetuation of “heritable conditions”. Heritable conditions involve cultural, cognitive, and

social network biases that favor some individuals to gain an advantage over others in acquiring

resources. Rather than stressing the ascendance or superiority of specific cultural or genetic

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traits, our work will focus on biological and cultural mechanisms that contribute to heuristics

used during perceptual selection, which in turn produces income and social inequality via

positive-feedback and collective behavioral mechanisms.

Two approaches will be used to provide insights into how these processes occur in human

societies. Hybrid computational models and neuroimaging (fMRI) will be used in terms of

modeling and validation, respectively. Hybrid model of individuals and populations will involve

using a cognitive model will be used for individuals and a hierarchical network model for

populations. They will jointly provide insight into how each node in a network (representing

individual social actors) sort access to resources. Future work will determine whether this sorting

is simply a matter of how connectivity is achieved, or if the assortative transmission of baseline

cultural or genetic traits also play a role.

Hierarchical network models also allow us to examine the roles of status, competition,

cooperation, and cultural differentiation in shaping the emergence of income inequality in a

given social structure. As in game-theoretic models, interactions can be modeled on the

hierarchical networks to observe how “winner-take-all” dynamics emerge. However, the

advantage of hierarchical networks involves uncovering both the local and global components of

these outcomes. This spatial and structural variation will provide insights into how inequality can

be alleviated. At the level of individual nodes, fMRI experiments will validate our modeling

assumptions. Particularly, we will re-examine the validity of traditional rational expectations

models in contrast to alternative models such as identity economics. These tools will bring us

closer to understanding how culturally-specific valuation, cognitive biases, and social learning

work to facilitate a specific social order manifest in the topology and behavior of a hierarchical

network model.

Premise #1. It is our first contention that income inequality is an outcome of emergent

processes. These emergent process include the cultural construction of value, the application of

cognitive/cultural biases to social interactions, and changes in the structure of social hierarchies.

Taken together, these phenomena contribute to patterns of income inequality which are directly

observable as social surveys and census statistics. Yet these emergent processes can also be

approximated through complex systems modeling and observed as a set of cognitive states. This

provides us with a large set of plausible alternative scenarios and thus a robust parameter space,

and the outcomes can be mapped onto real-world societies as appropriate.

Premise #2. Our second premise involves how, by bringing together neurocognitive

investigations and computational modeling, we can better understand alternative hypotheses with

regard to microeconomic decision-making. We can also form more focused hypotheses regarding

how and why societies achieve specific allocations of resources. While this does not allow us to

replicate the current state of income inequality, it does allow us to understand the parameters

within which concentrations and dispersions of income tend to occur. This also allows us to

predict the formation of "heritable conditions", which will tend to perpetuate patterns of

inequality over time.

Premise #3. The third premise is a historical statement, and involves the nature of social

evolutionary change. While changes in income inequality might emerge out of interactions

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specific to context, the tendency for specific patterns of inequality across societies will depend

on the mental models and initial structure typical of a given society. While history is expected to

lead to highly unique outcomes, it should also result in highly contingent outcomes. Thus, the

economic history of inequality can be demonstrated as self-similar (e.g. history repeating itself).

We hope to show that convergence is due in part to the nature of resource allocations and

individual cognition embedded within repeated social interactions. Rather than being shrouded in

the mysterious aura of inevitability, the emergence of income inequality will be shown to be both

identifiable and somewhat controllable.

Additional Project Detail

The scope of intellectual resources proposed to engage in this research is shown in

Appendix A. A detailed outline for the experimental and modeling/simulation work is shown in

Appendix B. A partial list of measures to be used in conjunction with the hierarchical model is

discussed in Appendix C. the budget and timeline are featured in Appendix D.

Applications

Much as in the case of existing agent-based models [14], the most obvious application for

this work is scenario-building. The integration of evolutionary models with functional

neuroimaging would transcend existing neuroeconomic approaches [15] and provide us with new

capabilities for modeling economic systems. Not only could various assumptions about income

inequality regarding social structure and motivated behaviors be critically re-evaluated, but

alternate scenarios of economic organization could also be created and evaluated. In this case,

data on such individual behaviors can act as an a priori set of conditions on a purely structural

economic model. These types of evolutionary models also provide the historical uniqueness due

to context missing in traditional economic models.

We will also be able to better understand possibilities and historical contingencies. This

application concerns the quality of scope of social change that results from certain historical

events and policy interventions. We can again turn to evolutionary simulation to provide us with

a set of historical trajectories. By replicating a cumulative series of events that lead to the

formation of a particular social hierarchy, we can then ask what kinds of changes are most

plausible [16]. This can be done in parallel to find patterns across many different types of

economic organization, each with their own unique history. For example, can we discover

historical contingencies that act as antecedents to sharp increases in income inequality, or is this

just a feature of specific policies? What might follow from strong confiscatory policies? Given

different histories, we might expect effects on and differences in that society’s social stability.

We will further be able to establish the connection between perceptual discrimination and

inequality. By focusing purely on individual contributions, we can combine fMRI with virtual

environment-based experiments to discover the neural correlates of emerging inequality. While

this approach rests on an assumption (that perceptual discrimination leads to broader

inequalities), experiments can be developed to demonstrate the connection between selective

biases and their ability to cascade throughout a population. These types of experiments can also

provide us with information about market dynamics, particularly those that interact with social

hierarchies. This has particular relevance for cases (e.g. housing and education markets) in which

the causes of inequality are difficult to understand, and remedies are hard to find.

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References

[1] Lamont, M., Beljean, S., and Clair, M. What is missing? Cultural processes and causal

pathways to inequality. Socioeconomic Review, 12(3), 573-608 (2014).

[2] Piketty, T. and Saez, E. Inequality in the long run. Science, 344, 838-843 (2014).

[3] Birch, K. Beneficence, determinism and justice: an engagement with the argument for the

genetic selection of intelligence. Bioethics, 19(1), 12-28 (2005).

[4] Pringle, H. The Ancient Roots of the 1%. Science, 344, 822-825 (2014).

[5] Porter, M.E. Clusters and the new economics of competition. Harvard Business Review,

76(6), 77-90 (1998).

[6] Mercier, H. and Sperber, D. Why do humans reason? Arguments for an argumentative

theory. Behavioral and Brain Sciences, 34, 57-111 (2011).

[7] Sidanius, J. and Pratto, F. Social Dominance: an intergroup theory of social hierarchy and

oppression. Cambridge University Press (1999).

[8] Koenig, M.A. and Harris, P.L. The role of social cognition in early trust. Trends in

Cognitive Science, 9(10), 457-459 (2005).

[9] Hammack, S.E., Cooper, M.A., and Lezak, K.R. Overlapping neurobiology of learned

helplessness and conditioned defeat: implications for PTSD and mood disorders.

Neuropharmacology, 62(2), 565-575 (2012).

[10] Shizuka, D. and McDonald, D.B. A social network perspective on measurements of

dominance hierarchies. Animal Behaviour, 83, 925-934 (2012).

[11] Kleinberg, J. (1999). Authoritative sources in a hyperlinked environment. Journal of the

ACM, 46(5), 604-632.

[12] Alicea, B. Contextual and Structural Representations of Market-mediated Economic

Value. arXiv [q-fin], 1403.7021 (2014).

[13] Chen, S-H., Chang, C-L., and Du, Y-R. Agent-based economic models and econometrics.

Knowledge Engineering Review, 27(2), 187-219 (2012).

[14] Camerer, C. Goals, Methods, and Progress in Neuroeconomics. Annual Review of

Economics, 5, 425-455 (2013).

[15] Chase, I.D. Dynamics of Hierarchy Formation: the sequential development of dominance

relationships. Behaviour, 80, 218-240 (1982).

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Appendix A: Intellectual Resources

Institutional Profile:

Orthogonal Research is an independent research organization located in Champaign-

Urbana, IL. Orthogonal Research engages in projects that span the natural and social sciences,

with an emphasis on innovation in data analysis and our understanding of complex systems. This

is located near the University of Illinois Urbana-Champaign. Both potential collaborators and

computational resources/expertise can be drawn from this community. The main resources of

this organization lie in collaborations with other labs and groups, both private and University-

based. This project will be realized through collaboration with the Media Neuroscience

Laboratory at UCSB. This collaboration will provide the fMRI resources necessary for the non-

modeling portion of the project.

PI profile:

Bradly Alicea has a Masters degree from the University of Florida and a PhD from

Michigan State University. He has published in multiple academic fields, and in venues

including Nature Reviews Neuroscience, Stem Cells and Development, and Proceedings of

Artificial Life. Bradly is particularly interested in biological approaches to cognition and social

dynamics, theoretical synthesis, and computational modeling. With disciplinary interests that

span the biological, computational, and social sciences, he currently operates an academic start-

up called Orthogonal Research. Bradly is also the administrator of Synthetic Daisies blog, and

has an active interest in open and virtual science. For more information, please see Bradly’s

profile on Academia.edu (https://independent.academia.edu/ BradlyAlicea).

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Appendix B: Outline of Experimental and Simulation Design

I. Experiments on Value

1) Context vs. Interaction (fMRI):

* expose people to geometric primitives or novel environments (VR). Get them to assign value to

objects, models of well-known objects vs. novel objects. Then use two conditions:

a) give people information (context) about novel objects without exchanging them.

b) allow people to trade novel objects as proxies for well-known objects (this should indirectly

assign a value to novel objects).

Repeat the value assignation task. What is the change in value, and what is the difference

between conditions?

2) Dynamic Equilibrium (fMRI):

* Initial condition: provide set values for objects, and then force people to accept much less or

much more than the object is "worth". If you then remove this constraint, do people return to the

set value? In a dynamic free-viewing condition, use two conditions:

a) a control condition where much less or much more are offered, but subject is not forced to

transact. Observe natural drift in value.

b) an experimental condition where subject is forced to transact under constraints. The remove

constraints and observe hysteresis in valuation.

What is the change in value with respect to the control condition?

II. Models of Value and Emergent Inequality

MMEV model/simulations:

* market-mediated economic value (MMEV) involves using a parameter space as a stand-in for a

market. Economic value depends on how agents with beliefs and natural classifications (cultural

construction) value objects. This can occur in either an unsupervised or semi-supervised

manner). Valuations will be determined by cultural construction, but also constrained by both

social learning mechanisms and the market structure.

* in a "rough" parameter space (where the range of permissible values is small), the market is

expected to have a larger influence.

* in a smoother parameter space (where the range of permissible values is larger), the market is

expected to have less of an influence.

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In the latter case, value is determined in polysemic manner. This means that valuation is less

stable because the object means many different things to the market's constituents. In the former

case, by contrast, traditional ideas about market efficiency play a more deterministic role.

III. Models of Social Dynamics and Inequality

Node/Arc Pruning simulations on hierarchical networks:

* start with various types of hierarchical structures (trees, graphs, rings) representative of social

complexity.

* remove nodes and arcs according to different algorithms: each type of removal is expected to

have different effects.

* if hierarchical structure is repaired in a conserved manner (removed nodes/arcs grow back after

a certain period of time), or decayed in a conserved manner (nodes/arcs die off without

supervised removal), what is the consequence?

* we can use the measures described in Appendix C to evaluate the effects of inequality in our

hierarchical network models. More measures can be developed and interesting motif identified

throughout the course of the project.

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Appendix C: Equations for Hierarchical Network Simulations

Correspondence. The correspondence index can be defined as

[1]

A diagonal matrix (perfect correspondence amongst A, B, and C) would yield a value of 1.0. The

greater the number of alternate pathways amongst A, B, and C, the closer the value will be to

0.0.

Intra-level Status. Intra-level Status can be defined as

=

[2]

where I are the inbound links and O are the outbound links for each . Various regimes are

expected for bivariate comparisons of intra-level status and correspondence.

Homogeneity. The homogeneity measure can be defined as

H = ln

[3]

where Mo is the mode of a vector and is the mean value of a vector.

Vulnerability. The vulnerability measure can be defined as

V = 1 -

[4]

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where is the number of unique connections (excluding redundant pathways between

individual nodes), and is the total number of connections in the network.

Competition/Cooperation Kernel. The competition/cooperation kernel is a function that

determines whether or not a specific i,j interaction is cooperative or competitive. The following

two matrices demonstrate the outcomes (payoff matrix) for interactions between nodes i and j.

i++ i+ i- i--

j++ Unstable Stable Gain

Outcome

Unstable Unstable

j+ Stable Gain

Outcome

Mutual

Cooperation

j biased Unstable

j- Unstable i biased Mutual

Loss

Stable Loss

Outcome

j-- Unstable Unstable Stable Loss

Outcome

Unstable

i++ i+ i- i--

j++

j+

j-

j--

Mutual Loss

Region

Zero-sum

Region

Zero-sum

Region

Mutual Benefit

Region

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Appendix D: Budget and Timeline (3 year duration)

Component Amount Purpose References

Salary 120,000

To pay staff scientists

Benefits 30,000

To pay for benefits for

staff scientists

Technology 12,000

To pay for needed

computing and

equipment resources

(includes budget for

cluster computing)

SOURCE: Amazon EC2 Price

List

(http://aws.amazon.com/ec2/prici

ng/)

Travel 10,000

Travel expenses to

conferences and for

visiting collaborators

Investigation 30,000

Empirical Budget

(estimated number of

fMRI participants: 24)

SOURCE: UCSB Brain Imaging

Center

(http://www.bic.ucsb.edu/mri-

users.htm)

Consulting Fees 5,000

To pay for outside

expertise Consulting Fees

Overhead 5%

5% Administrative

Overhead

TOTAL 217,350

NOTES: The first year will involve setting up the hierarchical network models. This involves

coding and analysis. The second year will feature fMRI experimentation and data analysis. The

third year will involve integrating the simulation and neuroimaging results. The salary

component will be divided up amongst the PI and research assistants as required. The consulting

fees will pay for specific expertise related to executing research (e.g. research support, technical

assistance).