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Neural networks as a learning paradigm for general normal form games

Author

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  • Spiliopoulos, Leonidas
Abstract
This paper addresses how neural networks learn to play one-shot normal form games through experience in an environment of randomly generated game payoffs and randomly selected opponents. This agent based computational approach allows the modeling of learning all strategic types of normal form games, irregardless of the number of pure and mixed strategy Nash equilibria that they exhibit. This is a more realistic model of learning than the oft used models in the game theory learning literature which are usually restricted either to repeated games against the same opponent (or games with different payoffs but belonging to the same strategic class). The neural network agents were found to approximate human behavior in experimental one-shot games very well as the Spearman correlation coefficients between their behavior and that of human subjects ranged from 0.49 to 0.8857 across numerous experimental studies. Also, they exhibited the endogenous emergence of heuristics that have been found effective in describing human behavior in one-shot games. The notion of bounded rationality is explored by varying the topologies of the neural networks, which indirectly affects their ability to act as universal approximators of any function. The neural networks' behavior was assessed across various dimensions such as convergence to Nash equilibria, equilibrium selection and adherence to principles of iterated dominance.

Suggested Citation

  • Spiliopoulos, Leonidas, 2009. "Neural networks as a learning paradigm for general normal form games," MPRA Paper 16765, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:16765
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    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. Yang, Z. R. & Platt, Marjorie B. & Platt, Harlan D., 1999. "Probabilistic Neural Networks in Bankruptcy Prediction," Journal of Business Research, Elsevier, vol. 44(2), pages 67-74, February.
    3. Cho, In-Koo & Sargent, Thomas J., 1996. "Neural networks for encoding and adapting in dynamic economies," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 9, pages 441-470, Elsevier.
    4. Mookherjee, Dilip & Sopher, Barry, 1997. "Learning and Decision Costs in Experimental Constant Sum Games," Games and Economic Behavior, Elsevier, vol. 19(1), pages 97-132, April.
    5. Cabrales, Antonio & Garcia-Fontes, Walter & Motta, Massimo, 2000. "Risk dominance selects the leader: An experimental analysis," International Journal of Industrial Organization, Elsevier, vol. 18(1), pages 137-162, January.
    6. Straub, Paul G., 1995. "Risk dominance and coordination failures in static games," The Quarterly Review of Economics and Finance, Elsevier, vol. 35(4), pages 339-363.
    7. Itzhak Gilboa & David Schmeidler, 1995. "Case-Based Decision Theory," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(3), pages 605-639.
    8. Reinhard Selten & Klaus Abbink & Ricarda Cox, 2005. "Learning Direction Theory and the Winner’s Curse," Experimental Economics, Springer;Economic Science Association, vol. 8(1), pages 5-20, April.
    9. Tesfatsion, Leigh S., 2002. "Agent-Based Computational Economics: Growing Economies from the Bottom Up," Staff General Research Papers Archive 5075, Iowa State University, Department of Economics.
    10. Nagel, Rosemarie, 1995. "Unraveling in Guessing Games: An Experimental Study," American Economic Review, American Economic Association, vol. 85(5), pages 1313-1326, December.
    11. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, University Library of Munich, Germany, revised 15 Aug 2002.
    12. Selten, Reinhard, 1998. "Features of experimentally observed bounded rationality," European Economic Review, Elsevier, vol. 42(3-5), pages 413-436, May.
    13. Pedro Rey Biel, 2005. "Equilibrium PLay and Best Response to (Stated) Beliefs in Constant Sum Games," Experimental 0506003, University Library of Munich, Germany.
    14. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    15. Ockenfels, Axel & Selten, Reinhard, 2005. "Impulse balance equilibrium and feedback in first price auctions," Games and Economic Behavior, Elsevier, vol. 51(1), pages 155-170, April.
    16. Tang, Fang-Fang, 2001. "Anticipatory learning in two-person games: some experimental results," Journal of Economic Behavior & Organization, Elsevier, vol. 44(2), pages 221-232, February.
    17. Reinhard Selten & Klaus Abbink & Ricarda Cox, 2005. "Learning Direction Theory and the Winner’s Curse," Experimental Economics, Springer;Economic Science Association, vol. 8(1), pages 5-20, April.
    18. 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.
    19. Haruvy, Ernan & Stahl, Dale O., 2004. "Deductive versus inductive equilibrium selection: experimental results," Journal of Economic Behavior & Organization, Elsevier, vol. 53(3), pages 319-331, March.
    20. Schotter Andrew & Weigelt Keith & Wilson Charles, 1994. "A Laboratory Investigation of Multiperson Rationality and Presentation Effects," Games and Economic Behavior, Elsevier, vol. 6(3), pages 445-468, May.
    21. Fabrizio Germano, 2007. "Stochastic Evolution of Rules for Playing Finite Normal Form Games," Theory and Decision, Springer, vol. 62(4), pages 311-333, May.
    22. Louviere,Jordan J. & Hensher,David A. & Swait,Joffre D., 2000. "Stated Choice Methods," Cambridge Books, Cambridge University Press, number 9780521788304, September.
    23. Binmore, Ken & Swierzbinski, Joe & Proulx, Chris, 2001. "Does Minimax Work? An Experimental Study," Economic Journal, Royal Economic Society, vol. 111(473), pages 445-464, July.
    24. LiCalzi Marco, 1995. "Fictitious Play by Cases," Games and Economic Behavior, Elsevier, vol. 11(1), pages 64-89, October.
    25. Leung, Mark T. & Daouk, Hazem & Chen, An-Sing, 2000. "Forecasting stock indices: a comparison of classification and level estimation models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 173-190.
    26. Sgroi, Daniel & Zizzo, Daniel J., 2007. "Neural networks and bounded rationality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 375(2), pages 717-725.
    27. Kuan, Chung-Ming & Liu, Tung, 1995. "Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(4), pages 347-364, Oct.-Dec..
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    More about this item

    Keywords

    Behavioral game theory; Learning; Global games; Neural networks; Agent-based computational economics; Simulations; Complex adaptive systems; Artificial intelligence;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

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