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Evolutionary instability of zero-determinant strategies demonstrates that winning is not everything

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  • Christoph Adami

    (Michigan State University
    Michigan State University
    BEACON Center for the Study of Evolution in Action, Michigan State University)

  • Arend Hintze

    (Michigan State University
    BEACON Center for the Study of Evolution in Action, Michigan State University)

Abstract

Zero-determinant strategies are a new class of probabilistic and conditional strategies that are able to unilaterally set the expected payoff of an opponent in iterated plays of the Prisoner’s Dilemma irrespective of the opponent’s strategy (coercive strategies), or else to set the ratio between the player’s and their opponent’s expected payoff (extortionate strategies). Here we show that zero-determinant strategies are at most weakly dominant, are not evolutionarily stable, and will instead evolve into less coercive strategies. We show that zero-determinant strategies with an informational advantage over other players that allows them to recognize each other can be evolutionarily stable (and able to exploit other players). However, such an advantage is bound to be short-lived as opposing strategies evolve to counteract the recognition.

Suggested Citation

  • Christoph Adami & Arend Hintze, 2013. "Evolutionary instability of zero-determinant strategies demonstrates that winning is not everything," Nature Communications, Nature, vol. 4(1), pages 1-8, October.
  • Handle: RePEc:nat:natcom:v:4:y:2013:i:1:d:10.1038_ncomms3193
    DOI: 10.1038/ncomms3193
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    Cited by:

    1. Wang, JunFang & Guo, JinLi, 2019. "A synergy of punishment and extortion in cooperation dilemmas driven by the leader," Chaos, Solitons & Fractals, Elsevier, vol. 119(C), pages 263-268.
    2. Masahiko Ueda & Toshiyuki Tanaka, 2020. "Linear algebraic structure of zero-determinant strategies in repeated games," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-13, April.
    3. Yali Dong & Cong Li & Yi Tao & Boyu Zhang, 2015. "Evolution of Conformity in Social Dilemmas," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-12, September.
    4. Christopher Graser & Takako Fujiwara-Greve & Julian García & Matthijs van Veelen, 2024. "Repeated games with partner choice," Tinbergen Institute Discussion Papers 24-038/I, Tinbergen Institute.
    5. McAvoy, Alex & Hauert, Christoph, 2017. "Autocratic strategies for alternating games," Theoretical Population Biology, Elsevier, vol. 113(C), pages 13-22.
    6. El-Salam, Salsabeel M. Abd & El-Seidy, Essam & Abdel-Malek, Amira R., 2023. "Evaluating zero-determinant strategies’ effects on cooperation and conflict resolution in repeated games," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    7. Kang, Kai & Tian, Jinyan & Zhang, Boyu, 2024. "Cooperation and control in asymmetric repeated games," Applied Mathematics and Computation, Elsevier, vol. 470(C).
    8. Taha, Mohammad A. & Ghoneim, Ayman, 2020. "Zero-determinant strategies in repeated asymmetric games," Applied Mathematics and Computation, Elsevier, vol. 369(C).
    9. He, Zhixue & Geng, Yini & Shen, Chen & Shi, Lei, 2020. "Evolution of cooperation in the spatial prisoner’s dilemma game with extortion strategy under win-stay-lose-move rule," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    10. Jie, Yingmo & Liu, Charles Zhechao & Choo, Kim-Kwang Raymond & Guo, Cheng, 2024. "An incentive compatible ZD strategy-based data sharing model for federated learning: A perspective of iterated prisoner's dilemma," European Journal of Operational Research, Elsevier, vol. 315(2), pages 764-776.
    11. Axel Gautier & Ashwin Ittoo & Pieter Cleynenbreugel, 2020. "AI algorithms, price discrimination and collusion: a technological, economic and legal perspective," European Journal of Law and Economics, Springer, vol. 50(3), pages 405-435, December.
    12. Yohsuke Murase & Seung Ki Baek, 2021. "Friendly-rivalry solution to the iterated n-person public-goods game," PLOS Computational Biology, Public Library of Science, vol. 17(1), pages 1-17, January.
    13. Marc Harper & Vincent Knight & Martin Jones & Georgios Koutsovoulos & Nikoleta E Glynatsi & Owen Campbell, 2017. "Reinforcement learning produces dominant strategies for the Iterated Prisoner’s Dilemma," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-33, December.
    14. Christopher Lee & Marc Harper & Dashiell Fryer, 2015. "The Art of War: Beyond Memory-one Strategies in Population Games," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-16, March.
    15. Nikoleta E. Glynatsi & Vincent A. Knight, 2021. "A bibliometric study of research topics, collaboration, and centrality in the iterated prisoner’s dilemma," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-12, December.
    16. Jin-Li Guo, 2014. "Zero-determinant strategies in iterated multi-strategy games," Papers 1409.1786, arXiv.org, revised Sep 2014.
    17. 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.
    18. Vincent Knight & Marc Harper & Nikoleta E Glynatsi & Owen Campbell, 2018. "Evolution reinforces cooperation with the emergence of self-recognition mechanisms: An empirical study of strategies in the Moran process for the iterated prisoner’s dilemma," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-33, October.
    19. Amnon Rapoport & Darryl A Seale & Andrew M Colman, 2015. "Is Tit-for-Tat the Answer? On the Conclusions Drawn from Axelrod's Tournaments," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-11, July.

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