Agent_Zero: Toward Neurocognitive Foundations for Generative Social Science

Agent_Zero: Toward Neurocognitive Foundations for Generative Social Science

Joshua M. Epstein
Copyright Date: 2013
Pages: 192
https://www.jstor.org/stable/j.ctt5hhp5x
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  • Book Info
    Agent_Zero: Toward Neurocognitive Foundations for Generative Social Science
    Book Description:

    The Final Volume of the Groundbreaking Trilogy on Agent-Based Modeling

    In this pioneering synthesis, Joshua Epstein introduces a new theoretical entity:Agent_Zero. This software individual, or "agent," is endowed with distinct emotional/affective, cognitive/deliberative, and social modules. Grounded in contemporary neuroscience, these internal components interact to generate observed, often far-from-rational, individual behavior. When multiple agents of this new type move and interact spatially, they collectively generate an astonishing range of dynamics spanning the fields of social conflict, psychology, public health, law, network science, and economics.

    Epstein weaves a computational tapestry with threads from Plato, Hume, Darwin, Pavlov, Smith, Tolstoy, Marx, James, and Dostoevsky, among others. This transformative synthesis of social philosophy, cognitive neuroscience, and agent-based modeling will fascinate scholars and students of every stripe. Epstein's computer programs are provided in the book or on its Princeton University Press website, along with movies of his "computational parables."

    Agent_Zerois a signal departure in what it includes (e.g., a new synthesis of neurally grounded internal modules), what it eschews (e.g., standard behavioral imitation), the phenomena it generates (from genocide to financial panic), and the modeling arsenal it offers the scientific community.

    For generative social science,Agent_Zeropresents a groundbreaking vision and the tools to realize it.

    eISBN: 978-1-4008-4825-6
    Subjects: Mathematics, Technology, Sociology, Biological Sciences

Table of Contents

  1. Front Matter
    (pp. I-VI)
  2. Table of Contents
    (pp. VII-X)
  3. Foreword
    (pp. XI-XII)

    I see this book as the third in a trilogy on generative social science.

    The first volume of the trilogy wasGrowing Artificial Societies: Social Science from the Bottom Up(MIT Press/Brookings Press), with coauthor Robert Axtell. Published in 1996, this introduced theSugarscapeagent-based model, and the notion of agenerative explanationof social phenomena. Sugarscape was a single sweeping exploratory artificial society, with glimmerings of a mature generative epistemology.

    For the subsequent decade, with diverse colleagues, I applied agent-based modeling to a broad spectrum of fields—economics, archaeology, conflict, epidemiology, spatial games, and the dynamics of norms—and...

  4. Preface
    (pp. XIII-XIV)
  5. Acknowledgments
    (pp. XV-XVIII)
  6. INTRODUCTION
    (pp. 1-18)

    In hisTreatise of Human Nature,David Hume (1739; 2000 ed.) famously wrote, “Reason is . . . the slave of the passions.”² In using the termslave, however, Hume’s point is not that the passions always prevail over reason in a tug of war,³ but that the two are, in some sense, incommensurable. Distinguishing the passions from factual (true/false) claims, he writes, “Tis impossible [that] they [the passions] can be pronounced either true or false, and be either contrary or conformable to reason” (p. 458). I take this “nonconformability” to mean that, as a modeling proposition, passion and reason...

  7. PART I Mathematical Model
    (pp. 19-80)

    In this part, we specify explicit mathematical models for the emotional, deliberative, and social components of theAgent_ Zeroframework. These choices are not cast in stone, and different components should certainly be explored, as discussed in the Future Research section. First, however, we review some underlying neuroscience of fear and its throne: the amygdala.³⁹

    This review is worthwhile because the Rescorla-Wagner equations (used for the affective model component) do not presuppose that fear acquisition is largely unconscious, while this is a crucially important fact from a social science standpoint, and the amygdala discussion demonstrates that it is a neuro-scientifically...

  8. PART II Agent-Based Computational Model
    (pp. 81-106)

    In agent modeling, we essentially build artificial societies of software individuals who can interact directly with one another and with their environment according to simple behavioral rules. On agent-based modeling in general, see, for example, J. M. Epstein and Axtell (1996), Axelrod (1997a), Resnick (1994), J. M. Epstein (2006), Tesfatsion and Judd (2006), Miller and Page (2007), and the large literature cited in these works.¹¹⁸

    I developed this model inNetLogo5.0. Source Code for the canonical ¹¹⁹ Parable 1 run is given in Appendix IV. A table of parameter values for every run is also provided. As earlier noted,...

  9. PART III Extensions
    (pp. 107-180)

    Just as agents can differ in their search radii, so they may differ in their destructive radii. Thus far, this has been treated as a single exogenous global constant. It is more realistic—and reduces the number of freely adjustable parameters—to endogenize this action radius. It might, for example, be a function of affect, or of total disposition.¹⁵² In Figure 48, the destructive radius is a simple linear function of disposition and thus differs among agents (and varies in time). This is a fertile extension, especially in the various alternative interpretations of the framework.

    For example, where the action...

  10. PART IV Future Research and Conclusions
    (pp. 181-194)

    In this exposition, I have been essentially interested in the feasibility of a fundamental synthesis, rather than in its robustness to numerical perturbations. Hence, I have left sensitivity analysis for future rounds of work. But it would certainly be interesting to more fully explore the parameter space of the model, charting out its regions of stability.

    This numerical cartography could naturally begin with the systematic covariation of the (currently) global variables²¹⁵ offered as sliders in theAgent_ Zerointerface. These are as follows:

    Attack rate (stochastic environmental stimulus rate)

    Spatial sampling radius (sometimes referred to advisedly as “vision”)

    Extinction rate...

  11. Appendix I. Threshold Imputation Bounds
    (pp. 195-196)
  12. Appendix II. Mathematica Code
    (pp. 197-212)
  13. Appendix III. Agent_Zero NetLogo Source Code
    (pp. 213-220)
  14. Appendix IV. Parameter Settings for Model Runs
    (pp. 221-226)
  15. References
    (pp. 227-242)
  16. Index
    (pp. 243-249)