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Emergence and suppression of cooperation by action visibility in transparent games

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  • Anton M Unakafov
  • Thomas Schultze
  • Alexander Gail
  • Sebastian Moeller
  • Igor Kagan
  • Stephan Eule
  • Fred Wolf

Abstract

Real-world agents, humans as well as animals, observe each other during interactions and choose their own actions taking the partners’ ongoing behaviour into account. Yet, classical game theory assumes that players act either strictly sequentially or strictly simultaneously without knowing each other’s current choices. To account for action visibility and provide a more realistic model of interactions under time constraints, we introduce a new game-theoretic setting called transparent games, where each player has a certain probability of observing the partner’s choice before deciding on its own action. By means of evolutionary simulations, we demonstrate that even a small probability of seeing the partner’s choice before one’s own decision substantially changes the evolutionary successful strategies. Action visibility enhances cooperation in an iterated coordination game, but reduces cooperation in a more competitive iterated Prisoner’s Dilemma. In both games, “Win–stay, lose–shift” and “Tit-for-tat” strategies are predominant for moderate transparency, while a “Leader-Follower” strategy emerges for high transparency. Our results have implications for studies of human and animal social behaviour, especially for the analysis of dyadic and group interactions.Author summary: Humans and animals constantly make social decisions. Should an animal during group foraging or a human at the buffet try to obtain an attractive food item but risk a confrontation with a dominant conspecific, or is it better to opt for a less attractive but non-confrontational choice, especially when considering that the situation will repeat in the future? To model decision-making in such situations game theory is widely used. However, classic game theory assumes that agents act either at the same time, without knowing each other’s choices, or one after another. In contrast, humans and animals usually try to take the behaviour of their opponents and partners into account, to instantaneously adjust their own actions if possible. To provide a more realistic model of decision making in a social setting, we here introduce the concept of transparent games. It integrates the probability of observing the partner’s instantaneous actions into the game-theoretic framework of knowing previous choice outcomes. We find that such “transparency” has a direct influence on the emergence of cooperative behaviours in classic iterated games. The transparent games can contribute to a deeper understanding of social behaviour and decision-making of humans and animals.

Suggested Citation

  • Anton M Unakafov & Thomas Schultze & Alexander Gail & Sebastian Moeller & Igor Kagan & Stephan Eule & Fred Wolf, 2020. "Emergence and suppression of cooperation by action visibility in transparent games," PLOS Computational Biology, Public Library of Science, vol. 16(1), pages 1-32, January.
  • Handle: RePEc:plo:pcbi00:1007588
    DOI: 10.1371/journal.pcbi.1007588
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    as
    1. Hoffmann, Sönke & Mihm, Benedikt & Weimann, Joachim, 2015. "To commit or not to commit? An experimental investigation of pre-commitments in bargaining situations with asymmetric information," Journal of Public Economics, Elsevier, vol. 121(C), pages 95-105.
    2. Bar-Eli, Michael & Azar, Ofer H. & Ritov, Ilana & Keidar-Levin, Yael & Schein, Galit, 2007. "Action bias among elite soccer goalkeepers: The case of penalty kicks," Journal of Economic Psychology, Elsevier, vol. 28(5), pages 606-621, October.
    3. Drew Fudenberg & David K. Levine, 2009. "Learning and Equilibrium," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 385-420, May.
    4. Kandori, Michihiro, 2002. "Introduction to Repeated Games with Private Monitoring," Journal of Economic Theory, Elsevier, vol. 102(1), pages 1-15, January.
    5. P.-A. Chiappori, 2002. "Testing Mixed-Strategy Equilibria When Players Are Heterogeneous: The Case of Penalty Kicks in Soccer," American Economic Review, American Economic Association, vol. 92(4), pages 1138-1151, September.
    6. Dekel, Eddie & Fudenberg, Drew & Levine, David K., 2004. "Learning to play Bayesian games," Games and Economic Behavior, Elsevier, vol. 46(2), pages 282-303, February.
    7. Philip J. Reny, 2011. "On the Existence of Monotone Pure‐Strategy Equilibria in Bayesian Games," Econometrica, Econometric Society, vol. 79(2), pages 499-553, March.
    8. Ely, Jeffrey C. & Sandholm, William H., 2005. "Evolution in Bayesian games I: Theory," Games and Economic Behavior, Elsevier, vol. 53(1), pages 83-109, October.
    9. Hitoshi Matsushima, 2004. "Repeated Games with Private Monitoring: Two Players," Econometrica, Econometric Society, vol. 72(3), pages 823-852, May.
    10. Friedman, Daniel, 1991. "Evolutionary Games in Economics," Econometrica, Econometric Society, vol. 59(3), pages 637-666, May.
    11. Dirk Helbing & Martin Schönhof & Hans-Ulrich Stark & Janusz A. Hołyst, 2005. "How Individuals Learn To Take Turns: Emergence Of Alternating Cooperation In A Congestion Game And The Prisoner'S Dilemma," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 8(01), pages 87-116.
    12. John C. Harsanyi, 1967. "Games with Incomplete Information Played by "Bayesian" Players, I-III Part I. The Basic Model," Management Science, INFORMS, vol. 14(3), pages 159-182, November.
    13. Martin J. Osborne & Ariel Rubinstein, 1994. "A Course in Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262650401, April.
    14. repec:hhs:iuiwop:487 is not listed on IDEAS
    15. Heller, Yuval & Mohlin, Erik, 2015. "Stable Observable Behavior," MPRA Paper 63013, University Library of Munich, Germany.
    16. Nick Feltovich, 2000. "Reinforcement-Based vs. Belief-Based Learning Models in Experimental Asymmetric-Information," Econometrica, Econometric Society, vol. 68(3), pages 605-642, May.
    17. Jorgen W. Weibull, 1997. "Evolutionary Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262731215, April.
    18. Marie Devaine & Guillaume Hollard & Jean Daunizeau, 2014. "Theory of Mind: Did Evolution Fool Us?," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-12, February.
    19. Robert X D Hawkins & Robert L Goldstone, 2016. "The Formation of Social Conventions in Real-Time Environments," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-14, March.
    20. Arieli, Itai & Babichenko, Yakov, 2016. "Random extensive form games," Journal of Economic Theory, Elsevier, vol. 166(C), pages 517-535.
    21. Qingmin Liu & George J. Mailath & Andrew Postlewaite & Larry Samuelson, 2014. "Stable Matching With Incomplete Information," Econometrica, Econometric Society, vol. 82(2), pages 541-587, March.
    22. Daniel Friedman & Ryan Oprea, 2012. "A Continuous Dilemma," American Economic Review, American Economic Association, vol. 102(1), pages 337-363, February.
    23. Christian Hilbe & Martin A Nowak & Arne Traulsen, 2013. "Adaptive Dynamics of Extortion and Compliance," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-9, November.
    24. M. Posch & A. Pichler & K. Sigmund, 1998. "The Efficiency of Adapting Aspiration Levels," Working Papers ir98103, International Institute for Applied Systems Analysis.
    25. Heller, Yuval, 2015. "Instability of Equilibria with Imperfect Private Monitoring," MPRA Paper 64468, University Library of Munich, Germany.
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