IDEAS home Printed from https://ideas.repec.org/a/gam/jgames/v12y2021i3p52-d579711.html
   My bibliography  Save this article

Rock-Paper-Scissors Play: Beyond the Win-Stay/Lose-Change Strategy

Author

Listed:
  • Hanshu Zhang

    (School of Psychology, Central China Normal University, Wuhan 430079, China
    Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA)

  • Frederic Moisan

    (EM Lyon Business School, GATE UMR 5824, F-69130 Ecully, France)

  • Cleotilde Gonzalez

    (Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA)

Abstract

This research studied the strategies that players use in sequential adversarial games. We took the Rock-Paper-Scissors (RPS) game as an example and ran players in two experiments. The first experiment involved two humans, who played the RPS together for 100 times. Importantly, our payoff design in the RPS allowed us to differentiate between participants who used a random strategy from those who used a Nash strategy. We found that participants did not play in agreement with the Nash strategy, but rather, their behavior was closer to random. Moreover, the analyses of the participants’ sequential actions indicated heterogeneous cycle-based behaviors: some participants’ actions were independent of their past outcomes, some followed a well-known win-stay/lose-change strategy, and others exhibited the win-change/lose-stay behavior. To understand the sequential patterns of outcome-dependent actions, we designed probabilistic computer algorithms involving specific change actions (i.e., to downgrade or upgrade according to the immediate past outcome): the Win-Downgrade/Lose-Stay (WDLS) or Win-Stay/Lose-Upgrade (WSLU) strategies. Experiment 2 used these strategies against a human player. Our findings show that participants followed a win-stay strategy against the WDLS algorithm and a lose-change strategy against the WSLU algorithm, while they had difficulty in using an upgrade/downgrade direction, suggesting humans’ limited ability to detect and counter the actions of the algorithm. Taken together, our two experiments showed a large diversity of sequential strategies, where the win-stay/lose-change strategy did not describe the majority of human players’ dynamic behaviors in this adversarial situation.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jgames:v:12:y:2021:i:3:p:52-:d:579711
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-4336/12/3/52/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-4336/12/3/52/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Seth Frey & Robert L Goldstone, 2013. "Cyclic Game Dynamics Driven by Iterated Reasoning," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-11, February.
    2. 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.
    3. de Weerd Harmen & Diepgrond Denny & Verbrugge Rineke, 2018. "Estimating the Use of Higher-Order Theory of Mind Using Computational Agents," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 18(2), pages 1-12, July.
    4. Colin F. Camerer & Teck-Hua Ho & Juin-Kuan Chong, 2004. "A Cognitive Hierarchy Model of Games," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(3), pages 861-898.
    5. Dimitris Batzilis & Sonia Jaffe & Steven Levitt & John A. List & Jeffrey Picel, 2019. "Behavior in Strategic Settings: Evidence from a Million Rock-Paper-Scissors Games," Games, MDPI, vol. 10(2), pages 1-34, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Erik Brockbank & Edward Vul, 2021. "Formalizing Opponent Modeling with the Rock, Paper, Scissors Game," Games, MDPI, vol. 12(3), pages 1-20, September.
    2. Seth Frey & Robert L. Goldstone, 2018. "Cognitive mechanisms for human flocking dynamics," Journal of Computational Social Science, Springer, vol. 1(2), pages 349-375, September.
    3. Hanshu Zhang & Frederic Moisan & Cleotilde Gonzalez, 2021. "Rock-paper-scissors play: Beyond the win-stay/lose-change strategy," Post-Print hal-04325630, HAL.
    4. Drew Fudenberg & Jon Kleinberg & Annie Liang & Sendhil Mullainathan, 2019. "Measuring the Completeness of Theories," Papers 1910.07022, arXiv.org.
    5. Bosch-Domènech, Antoni & Vriend, Nicolaas J., 2013. "On the role of non-equilibrium focal points as coordination devices," Journal of Economic Behavior & Organization, Elsevier, vol. 94(C), pages 52-67.
    6. Benito Arruñada & Marcos Casarin & Francesca Pancotto, 2012. "Are Self-regarding Subjects More Rational?," Working Papers 611, Barcelona School of Economics.
    7. Ispano, Alessandro & Schwardmann, Peter, 2017. "Cooperating over losses and competing over gains: A social dilemma experiment," Games and Economic Behavior, Elsevier, vol. 105(C), pages 329-348.
    8. Sergeyev, Dmitriy & Iovino, Luigi, 2018. "Central Bank Balance Sheet Policies Without Rational Expectations," CEPR Discussion Papers 13100, C.E.P.R. Discussion Papers.
    9. Yuval Heller & Eyal Winter, 2020. "Biased-Belief Equilibrium," American Economic Journal: Microeconomics, American Economic Association, vol. 12(2), pages 1-40, May.
    10. Belzil, Christian & Sidibé, Modibo, 2016. "Internal and External Validity of Experimental Risk and Time Preferences," IZA Discussion Papers 10348, Institute of Labor Economics (IZA).
    11. Philippe Jehiel, 2022. "Analogy-Based Expectation Equilibrium and Related Concepts:Theory, Applications, and Beyond," Working Papers halshs-03735680, HAL.
    12. Vincent P. Crawford & Nagore Iriberri, 2004. "Fatal Attraction: Focality, Naivete, and Sophistication in Experimental Hide-and-Seek Games," Levine's Bibliography 122247000000000345, UCLA Department of Economics.
    13. Carrillo, Juan D. & Palfrey, Thomas R., 2011. "No trade," Games and Economic Behavior, Elsevier, vol. 71(1), pages 66-87, January.
    14. Strzalecki, Tomasz, 2014. "Depth of reasoning and higher order beliefs," Journal of Economic Behavior & Organization, Elsevier, vol. 108(C), pages 108-122.
    15. Marie-Claire Villeval, 2007. "Experimental Economics: Contributions, Recent Developments, and New Challenges," Working Papers 0706, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    16. Chorus, Caspar & van Cranenburgh, Sander & Daniel, Aemiro Melkamu & Sandorf, Erlend Dancke & Sobhani, Anae & Szép, Teodóra, 2021. "Obfuscation maximization-based decision-making: Theory, methodology and first empirical evidence," Mathematical Social Sciences, Elsevier, vol. 109(C), pages 28-44.
    17. Jeroen Struben & Brandon H. Lee & Christopher B. Bingham, 2020. "Collective Action Problems and Resource Allocation During Market Formation," Post-Print hal-02927584, HAL.
    18. Haruvy, Ernan & Stahl, Dale O., 2007. "Equilibrium selection and bounded rationality in symmetric normal-form games," Journal of Economic Behavior & Organization, Elsevier, vol. 62(1), pages 98-119, January.
    19. Samahita, Margaret & Holm, Håkan J., 2020. "Mining for Mood Effect in the Field," Working Papers 2020:2, Lund University, Department of Economics.
    20. Frederic Koessler & Ch. Noussair & A. Ziegelmeyer, 2005. "Individual Behavior and Beliefs in Experimental Parimutuel Betting Markets," THEMA Working Papers 2005-08, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jgames:v:12:y:2021:i:3:p:52-:d:579711. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.