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Learning Optimal Strategies in a Duel Game

Author

Listed:
  • Angelos Gkekas

    (Department of Electronics and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Athina Apostolidou

    (Department of Electronics and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Artemis Vernadou

    (Department of Electronics and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Athanasios Kehagias

    (Department of Electronics and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

Abstract

We study a duel game in which each player has incomplete knowledge of the game parameters. We present a simple, heuristically motivated and easily implemented algorithm by which, in the course of repeated plays, each player estimates the missing parameters and consequently learns his optimal strategy.

Suggested Citation

  • Angelos Gkekas & Athina Apostolidou & Artemis Vernadou & Athanasios Kehagias, 2025. "Learning Optimal Strategies in a Duel Game," Games, MDPI, vol. 16(1), pages 1-16, February.
    • Handle: RePEc:gam:jgames:v:16:y:2025:i:1:p:8-:d:1583800
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    References listed on IDEAS

    as
    1. Jorgen W. Weibull, 1997. "Evolutionary Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262731215, December.
    2. Ross Cressman, 2003. "Evolutionary Dynamics and Extensive Form Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262033054, December.
    3. D. Fudenberg & D. K. Levine, 2017. "Whither game theory? Towards a theory oflearning in games," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 5.
    4. Drew Fudenberg & David K Levine, 2016. "Whither Game Theory?," Levine's Working Paper Archive 786969000000001307, David K. Levine.
    Full references (including those not matched with items on IDEAS)

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