Neural networks as a learning paradigm for general normal form games
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.
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- Fabrizio Germano, 2007. "Stochastic Evolution of Rules for Playing Finite Normal Form Games," Theory and Decision, Springer, vol. 62(4), pages 311-333, May.
- 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 347-64, Oct.-Dec..
- M. Li Calzi, 2010.
"Fictitious Play By Cases,"
Levine's Working Paper Archive
407, David K. Levine.
- Reinhard Selten & Klaus Abbink & Ricarda Cox, 2005.
"Learning Direction Theory and the Winnerâ€™s Curse,"
Springer, vol. 8(1), pages 5-20, April.
- 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.
- Axel Ockenfels & Reinhard Selten, 2004. "Impulse Balance Equilibrium and Feedback in First Price Auctions," Working Paper Series in Economics 7, University of Cologne, Department of Economics.
- Axel Ockenfels & Reinhard Selten, 2002. "Impulse Balance Equilibrium and Feedback in First Price Auctions," Papers on Strategic Interaction 2002-12, Max Planck Institute of Economics, Strategic Interaction Group.
- Selten, Reinhard, .
"Features of Experimentally Observed Bounded Rationality,"
Discussion Paper Serie B
421, University of Bonn, Germany, revised Nov 1997.
- Selten, Reinhard, 1998. "Features of experimentally observed bounded rationality," European Economic Review, Elsevier, vol. 42(3-5), pages 413-436, May.
- 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.
- repec:cup:cbooks:9780521788304 is not listed on IDEAS
- Barry Sopher & Dilip Mookherjee, 1997.
"Learning and Decision Costs in Experimental Constant Sum Games,"
Departmental Working Papers
199527, Rutgers University, Department of Economics.
- 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.
- Barry Sopher & Dilip Mookherjee, 2000. "Learning and Decision Costs in Experimental Constant Sum Games," Departmental Working Papers 199625, Rutgers University, Department of Economics.
- 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.
- Schotter, Andrew & Weigelt, Keith & Wilson, Charles, 1990. "A Laboratory Investigation Of Multi-Person Rationality And Presentation Effects," Working Papers 90-24, C.V. Starr Center for Applied Economics, New York University.
- Pedro Rey-Biel, 2007.
"Equilibrium Play and Best Response to (Stated) Beliefs in Constant Sum Games,"
UFAE and IAE Working Papers
676.07, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
- Pedro Rey Biel, 2005. "Equilibrium PLay and Best Response to (Stated) Beliefs in Constant Sum Games," Experimental 0506003, EconWPA.
- 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.
- 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.
- Antonio Cabrales & Walter Garcia Fontes & Massimo Motta, 1997. "Risk dominance selects the leader. An experimental analysis," Economics Working Papers 222, Department of Economics and Business, Universitat Pompeu Fabra.
- 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.
- 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.
- 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.
- Nagel, Rosemarie, 1995. "Unraveling in Guessing Games: An Experimental Study," American Economic Review, American Economic Association, vol. 85(5), pages 1313-26, December.
- Tesfatsion, Leigh S., 2002. "Agent-Based Computational Economics: Growing Economies from the Bottom Up," Staff General Research Papers 5075, Iowa State University, Department of Economics.
- 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.
- 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.
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