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Modeling Poker Challenges by Evolutionary Game Theory

Author

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  • Marco Alberto Javarone

    () (Department of Mathematics and Computer Science, University of Cagliari, Cagliari 09124, Italy)

Abstract

We introduce a model for studying the evolutionary dynamics of Poker. Notably, despite its wide diffusion and the raised scientific interest around it, Poker still represents an open challenge. Recent attempts for uncovering its real nature, based on statistical physics, showed that Poker in some conditions can be considered as a skill game. In addition, preliminary investigations reported a neat difference between tournaments and ‘cash game’ challenges, i.e., between the two main configurations for playing Poker. Notably, these previous models analyzed populations composed of rational and irrational agents, identifying in the former those that play Poker by using a mathematical strategy, while in the latter those playing randomly. Remarkably, tournaments require very few rational agents to make Poker a skill game, while ‘cash game’ may require several rational agents for not being classified as gambling. In addition, when the agent interactions are based on the ‘cash game’ configuration, the population shows an interesting bistable behavior that deserves further attention. In the proposed model, we aim to study the evolutionary dynamics of Poker by using the framework of Evolutionary Game Theory, in order to get further insights on its nature, and for better clarifying those points that remained open in the previous works (as the mentioned bistable behavior). In particular, we analyze the dynamics of an agent population composed of rational and irrational agents, that modify their behavior driven by two possible mechanisms: self-evaluation of the gained payoff, and social imitation. Results allow to identify a relation between the mechanisms for updating the agents’ behavior and the final equilibrium of the population. Moreover, the proposed model provides further details on the bistable behavior observed in the ‘cash game’ configuration.

Suggested Citation

  • Marco Alberto Javarone, 2016. "Modeling Poker Challenges by Evolutionary Game Theory," Games, MDPI, Open Access Journal, vol. 7(4), pages 1-10, December.
  • Handle: RePEc:gam:jgames:v:7:y:2016:i:4:p:39-:d:84580
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    References listed on IDEAS

    as
    1. Daniel Friedman, 1998. "On economic applications of evolutionary game theory," Journal of Evolutionary Economics, Springer, vol. 8(1), pages 15-43.
    2. Marco Alberto Javarone, 2016. "Statistical physics of the spatial Prisoner’s Dilemma with memory-aware agents," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 89(2), pages 1-6, February.
    3. Marco Alberto Javarone, 2016. "Statistical physics of the spatial Prisoner’s Dilemma with memory-aware agents," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 89(2), pages 1-6, February.
    4. Galam, Serge & Walliser, Bernard, 2010. "Ising model versus normal form game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(3), pages 481-489.
    5. Frey, Erwin, 2010. "Evolutionary game theory: Theoretical concepts and applications to microbial communities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(20), pages 4265-4298.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    poker; evolutionary game theory; population dynamics; PACS; 89.20.-a;

    JEL classification:

    • C - Mathematical and Quantitative Methods
    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
    • C71 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Cooperative Games
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

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