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Behavioural Isomorphism, Cognitive Economy and Recursive Thought in Non-Transitive Game Strategy

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  • Benjamin J. Dyson

    (Department of Psychology, P217 Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
    Department of Psychology, Ryerson University, Toronto, ON M5B 2K3, Canada
    Department of Psychology, University of Sussex, Sussex BN1 9RH, UK)

Abstract

Game spaces in which an organism must repeatedly compete with an opponent for mutually exclusive outcomes are critical methodologies for understanding decision-making under pressure. In the non-transitive game rock, paper, scissors (RPS), the only technique that guarantees the lack of exploitation is to perform randomly in accordance with mixed-strategy. However, such behavior is thought to be outside bounded rationality and so decision-making can become deterministic, predictable, and ultimately exploitable. This review identifies similarities across economics, neuroscience, nonlinear dynamics, human, and animal cognition literatures, and provides a taxonomy of RPS strategy. RPS strategies are discussed in terms of (a) whether the relevant computations require sensitivity to item frequency, the cyclic relationships between responses, or the outcome of the previous trial, and (b) whether the strategy is framed around the self or other. The negative implication of this taxonomy is that despite the differences in cognitive economy and recursive thought, many of the identified strategies are behaviorally isomorphic. This makes it difficult to infer strategy from behavior. The positive implication is that this isomorphism can be used as a novel design feature in furthering our understanding of the attribution, agency, and acquisition of strategy in RPS and other game spaces.

Suggested Citation

  • Benjamin J. Dyson, 2019. "Behavioural Isomorphism, Cognitive Economy and Recursive Thought in Non-Transitive Game Strategy," Games, MDPI, vol. 10(3), pages 1-14, August.
  • Handle: RePEc:gam:jgames:v:10:y:2019:i:3:p:32-:d:255514
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    References listed on IDEAS

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    Cited by:

    1. Vincent Srihaput & Kaylee Craplewe & Benjamin James Dyson, 2020. "Switching Competitors Reduces Win-Stay but Not Lose-Shift Behaviour: The Role of Outcome-Action Association Strength on Reinforcement Learning," Games, MDPI, vol. 11(3), pages 1-10, July.
    2. Hanshu Zhang & Frederic Moisan & Cleotilde Gonzalez, 2021. "Rock-Paper-Scissors Play: Beyond the Win-Stay/Lose-Change Strategy," Games, MDPI, vol. 12(3), pages 1-15, June.
    3. Erik Brockbank & Edward Vul, 2021. "Formalizing Opponent Modeling with the Rock, Paper, Scissors Game," Games, MDPI, vol. 12(3), pages 1-20, September.

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