IDEAS home Printed from https://ideas.repec.org/p/cam/camdae/0207.html
   My bibliography  Save this paper

Strategy Learning in 3x3 Games by Neural Networks

Author

Listed:
  • D. Sgroi
  • D. J. Zizzo

Abstract

This paper presents a neural network based methodology for examining the learning of game-playing rules in never-before seen games. A network is trained to pick Nash equilibria in a set of games and then released to play a larger set of new games. While faultlessly selecting Nash equilibria in never-before seen games is too complex a task for the network, Nash equilibria are chosen approximately 60% of the times. Furthermore, despite training the network to select Nash equilibria, what emerges are endogenously obtained bounded-rational rules which are closer to payoff dominance, and the best response to payoff dominance.

Suggested Citation

  • D. Sgroi & D. J. Zizzo, 2002. "Strategy Learning in 3x3 Games by Neural Networks," Cambridge Working Papers in Economics 0207, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:0207
    Note: EMT
    as

    Download full text from publisher

    File URL: http://www.econ.cam.ac.uk/research-files/repec/cam/pdf/wp0207.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stahl Dale O. & Wilson Paul W., 1995. "On Players' Models of Other Players: Theory and Experimental Evidence," Games and Economic Behavior, Elsevier, vol. 10(1), pages 218-254, July.
    2. Ben-porath, Elchanan, 1990. "The complexity of computing a best response automaton in repeated games with mixed strategies," Games and Economic Behavior, Elsevier, vol. 2(1), pages 1-12, March.
    3. Herbert A. Simon, 1955. "A Behavioral Model of Rational Choice," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 69(1), pages 99-118.
    4. Costa-Gomes, Miguel & Crawford, Vincent P & Broseta, Bruno, 2001. "Cognition and Behavior in Normal-Form Games: An Experimental Study," Econometrica, Econometric Society, vol. 69(5), pages 1193-1235, September.
    5. Gilboa, Itzhak, 1988. "The complexity of computing best-response automata in repeated games," Journal of Economic Theory, Elsevier, vol. 45(2), pages 342-352, August.
    6. Stahl, Dale II & Wilson, Paul W., 1994. "Experimental evidence on players' models of other players," Journal of Economic Behavior & Organization, Elsevier, vol. 25(3), pages 309-327, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Spiliopoulos, Leonidas, 2009. "Neural networks as a learning paradigm for general normal form games," MPRA Paper 16765, University Library of Munich, Germany.
    2. Fabrizio Germano, 2007. "Stochastic Evolution of Rules for Playing Finite Normal Form Games," Theory and Decision, Springer, vol. 62(4), pages 311-333, May.
    3. Sgroi, Daniel & Zizzo, Daniel John, 2009. "Learning to play 3×3 games: Neural networks as bounded-rational players," Journal of Economic Behavior & Organization, Elsevier, vol. 69(1), pages 27-38, January.
    4. Leonidas Spiliopoulos, 2005. "Can the human mind learn to backward induce? A neural network answer," Game Theory and Information 0505008, University Library of Munich, Germany.

    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. Sgroi, Daniel & Zizzo, Daniel John, 2009. "Learning to play 3×3 games: Neural networks as bounded-rational players," Journal of Economic Behavior & Organization, Elsevier, vol. 69(1), pages 27-38, January.
    2. Vincent P. Crawford & Nagore Iriberri, 2004. "Fatal Attraction: Focality, Naivete, and Sophistication in Experimental Hide-and-Seek Games," Levine's Bibliography 122247000000000316, UCLA Department of Economics.
    3. Strzalecki, Tomasz, 2014. "Depth of reasoning and higher order beliefs," Journal of Economic Behavior & Organization, Elsevier, vol. 108(C), pages 108-122.
    4. Dengler, Sebastian & Prüfer, Jens, 2021. "Consumers' privacy choices in the era of big data," Games and Economic Behavior, Elsevier, vol. 130(C), pages 499-520.
    5. Choo, Lawrence C.Y & Kaplan, Todd R., 2014. "Explaining Behavior in the "11-20" Game," MPRA Paper 52808, University Library of Munich, Germany.
    6. Kyle Hyndman & Antoine Terracol & Jonathan Vaksmann, 2022. "Beliefs and (in)stability in normal-form games," Experimental Economics, Springer;Economic Science Association, vol. 25(4), pages 1146-1172, September.
    7. Florian Gauer & Christoph Kuzmics, 2020. "Cognitive Empathy In Conflict Situations," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(4), pages 1659-1678, November.
    8. Dorothea Kübler & Georg Weizsäcker, 2004. "Limited Depth of Reasoning and Failure of Cascade Formation in the Laboratory," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 71(2), pages 425-441.
    9. Fabrizio Germano & Peio Zuazo-Garin, 2017. "Bounded rationality and correlated equilibria," International Journal of Game Theory, Springer;Game Theory Society, vol. 46(3), pages 595-629, August.
    10. Vincent P. Crawford & Nagore Iriberri, 2007. "Level-k Auctions: Can a Nonequilibrium Model of Strategic Thinking Explain the Winner's Curse and Overbidding in Private-Value Auctions?," Econometrica, Econometric Society, vol. 75(6), pages 1721-1770, November.
    11. Doğan, Gönül, 2018. "Collusion in a buyer–seller network formation game," Journal of Economic Behavior & Organization, Elsevier, vol. 155(C), pages 445-457.
    12. Nagel, Rosemarie & Bühren, Christoph & Frank, Björn, 2017. "Inspired and inspiring: Hervé Moulin and the discovery of the beauty contest game," Mathematical Social Sciences, Elsevier, vol. 90(C), pages 191-207.
    13. Devetag, Giovanna & Warglien, Massimo, 2003. "Games and phone numbers: Do short-term memory bounds affect strategic behavior?," Journal of Economic Psychology, Elsevier, vol. 24(2), pages 189-202, April.
    14. Healy, Paul J. & Park, Hyoeun, 2023. "Model selection accuracy in behavioral game theory: A simulation," European Economic Review, Elsevier, vol. 152(C).
    15. Vincent P. Crawford, 2006. "Look-ups as the Windows of the Strategic Soul: Studying Cognition via Information Search in Game Experiments," Levine's Bibliography 321307000000000462, UCLA Department of Economics.
    16. Breitmoser, Yves & Tan, Jonathan H.W. & Zizzo, Daniel John, 2014. "On the beliefs off the path: Equilibrium refinement due to quantal response and level-k," Games and Economic Behavior, Elsevier, vol. 86(C), pages 102-125.
    17. Despoina Alempaki & Andrew M. Colman & Felix Kölle & Graham Loomes & Briony D. Pulford, 2022. "Investigating the failure to best respond in experimental games," Experimental Economics, Springer;Economic Science Association, vol. 25(2), pages 656-679, April.
    18. Weizsacker, Georg, 2003. "Ignoring the rationality of others: evidence from experimental normal-form games," Games and Economic Behavior, Elsevier, vol. 44(1), pages 145-171, July.
    19. Berger, Ulrich & De Silva, Hannelore & Fellner-Röhling, Gerlinde, 2016. "Cognitive hierarchies in the minimizer game," Journal of Economic Behavior & Organization, Elsevier, vol. 130(C), pages 337-348.
    20. Rey-Biel, Pedro, 2009. "Equilibrium play and best response to (stated) beliefs in normal form games," Games and Economic Behavior, Elsevier, vol. 65(2), pages 572-585, March.

    More about this item

    Keywords

    rationality; learning; neural networks; normal form games; complexity;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D00 - Microeconomics - - General - - - General
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:cam:camdae:0207. 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: Jake Dyer (email available below). General contact details of provider: https://www.econ.cam.ac.uk/ .

    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.