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Artificial intelligence for wargaming and modeling

Author

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  • Paul K Davis
  • Paul Bracken

Abstract

In this paper, we discuss how artificial intelligence (AI) could be used in political-military modeling, simulation, and wargaming of conflicts with nations having weapons of mass destruction and other high-end capabilities involving space, cyberspace, and long-range precision weapons. AI should help participants in wargames, and agents in simulations, to understand possible perspectives, perceptions, and calculations of adversaries who are operating with uncertainties and misimpressions. The content of AI should recognize the risks of escalation leading to catastrophe with no winner but also the possibility of outcomes with meaningful winners and losers. We discuss implications for the design and development of families of models, simulations, and wargames using several types of AI functionality. We also discuss decision aids for wargaming, with and without AI, informed by theory and exploratory work using simulation, history, and earlier wargaming.

Suggested Citation

  • Paul K Davis & Paul Bracken, 2025. "Artificial intelligence for wargaming and modeling," The Journal of Defense Modeling and Simulation, , vol. 22(1), pages 25-40, January.
    • Handle: RePEc:sae:joudef:v:22:y:2025:i:1:p:25-40
    DOI: 10.1177/15485129211073126
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    References listed on IDEAS

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    1. Adam T Biggs & Kyle A Pettijohn, 2021. "Prospect theory and its implications for adversarial decision-making," The Journal of Defense Modeling and Simulation, , vol. 18(2), pages 125-134, April.
    2. Steven W. Popper & Robert J. Lempert & Paul K. Davis & Tim McDonald, 2021. "Forecasts and decisions: A commentary on Lustick and Tetlock 2021," Futures & Foresight Science, John Wiley & Sons, vol. 3(2), June.
    3. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    4. Joshua M. Epstein, 2014. "Agent_Zero:Toward Neurocognitive Foundations for Generative Social Science," Economics Books, Princeton University Press, edition 1, number 10169, December.
    5. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, December.
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    Cited by:

    1. Solhdoost, Mohsen, 2025. "Forecasting Israel-Iran Escalation Bands with Structured Judgment Using Artificial Intelligence Algorithms," SocArXiv jkzby_v1, Center for Open Science.

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