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StateSim: lessons learned from 20 years of a country modeling and simulation toolset

Author

Listed:
  • Barry G. Silverman

    (University of Pennsylvania
    Edutainiacs.Com, LLC)

  • Daniel M. Silverman

    (Edutainiacs.Com, LLC
    Carnegie Mellon University)

  • Gnana Bharathy

    (Edutainiacs.Com, LLC
    University of Technology Sydney)

  • Nathan Weyer

    (Edutainiacs.Com, LLC)

  • William R. Tam

    (Edutainiacs.Com, LLC)

Abstract

A holy grail for military, diplomatic, and intelligence analysis is a valid set of software agent models that act as the desired ethno-political factions so that one can test the effects of alternative courses of action in different countries. This article explains StateSim, a country modeling approach that synthesizes best-of-breed theories from across the social sciences and that has helped numerous organizations over 20 years to study insurgents, gray zone actors, and other societal instabilities. The country modeling literature is summarized (Sect. 1.1) and synthetic inquiry is contrasted with scientific inquiry (Sects. 1.2 and 2). Section 2 also explains many fielded StateSim applications and 100s of past acceptability tests and validity assessments. Section 3 then describes how users now construct and run ‘first pass’ country models within hours due to the StateSim Generator, while Sect. 4 offers two country analyses that illustrate this approach. The conclusions explain lessons learned.

Suggested Citation

  • Barry G. Silverman & Daniel M. Silverman & Gnana Bharathy & Nathan Weyer & William R. Tam, 2021. "StateSim: lessons learned from 20 years of a country modeling and simulation toolset," Computational and Mathematical Organization Theory, Springer, vol. 27(3), pages 231-263, September.
    • Handle: RePEc:spr:comaot:v:27:y:2021:i:3:d:10.1007_s10588-021-09324-1
    DOI: 10.1007/s10588-021-09324-1
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    References listed on IDEAS

    as
    1. Barry G. Silverman & Gnana Bharathy & Benjamin Nye & Roy J. Eidelson, 2007. "Modeling factions for “effects based operations”: part I—leaders and followers," Computational and Mathematical Organization Theory, Springer, vol. 13(4), pages 379-406, December.
    2. Robert M. Solow, 1956. "A Contribution to the Theory of Economic Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 70(1), pages 65-94.
    3. Armstrong, J. Scott, 2002. "Assessing game theory, role playing, and unaided judgment," International Journal of Forecasting, Elsevier, vol. 18(3), pages 345-352.
    4. Jack A. Goldstone & Robert H. Bates & David L. Epstein & Ted Robert Gurr & Michael B. Lustik & Monty G. Marshall & Jay Ulfelder & Mark Woodward, 2010. "A Global Model for Forecasting Political Instability," American Journal of Political Science, John Wiley & Sons, vol. 54(1), pages 190-208, January.
    5. Douglas Gollin, 2014. "The Lewis Model: A 60-Year Retrospective," Journal of Economic Perspectives, American Economic Association, vol. 28(3), pages 71-88, Summer.
    6. Gholamreza Askari & Madjid Eshaghi Gordji & Choonkil Park, 2019. "The behavioral model and game theory," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-8, December.
    7. Barry G. Silverman & Gnana Bharathy & Benjamin Nye & Tony Smith, 2008. "Modeling factions for ‘effects based operations’, part II: behavioral game theory," Computational and Mathematical Organization Theory, Springer, vol. 14(2), pages 120-155, June.
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    Cited by:

    1. Sai Yayavaram & Sasanka Sekhar Chanda, 2023. "Decision making under high complexity: a computational model for the science of muddling through," Computational and Mathematical Organization Theory, Springer, vol. 29(2), pages 300-335, June.

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