Advanced Search
MyIDEAS: Login

Neural networks as a learning paradigm for general normal form games

Contents:

Author Info

  • 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.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://mpra.ub.uni-muenchen.de/16765/
File Function: original version
Download Restriction: no

Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 16765.

as in new window
Length:
Date of creation: 12 Aug 2009
Date of revision:
Handle: RePEc:pra:mprapa:16765

Contact details of provider:
Postal: Schackstr. 4, D-80539 Munich, Germany
Phone: +49-(0)89-2180-2219
Fax: +49-(0)89-2180-3900
Web page: http://mpra.ub.uni-muenchen.de
More information through EDIRC

Related research

Keywords: Behavioral game theory; Learning; Global games; Neural networks; Agent-based computational economics; Simulations; Complex adaptive systems; Artificial intelligence;

Find related papers by JEL classification:

This paper has been announced in the following NEP Reports:

References

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
as in new window
  1. 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).
  2. 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.
  3. 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.
  4. Selten, Reinhard, . "Features of Experimentally Observed Bounded Rationality," Discussion Paper Serie B 421, University of Bonn, Germany, revised Nov 1997.
  5. Barry Sopher & Dilip Mookherjee, 2000. "Learning and Decision Costs in Experimental Constant Sum Games," Departmental Working Papers 199625, Rutgers University, Department of Economics.
  6. 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.
  7. 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.
  8. M. Li Calzi, 2010. "Fictitious Play By Cases," Levine's Working Paper Archive 407, David K. Levine.
  9. Fabrizio Germano, 2007. "Stochastic Evolution of Rules for Playing Finite Normal Form Games," Theory and Decision, Springer, vol. 62(4), pages 311-333, May.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. Reinhard Selten & Klaus Abbink & Ricarda Cox, 2001. "Learning Direction Theory and the Winner’s Curse," Bonn Econ Discussion Papers bgse10_2001, University of Bonn, Germany.
  15. Nagel, Rosemarie, 1995. "Unraveling in Guessing Games: An Experimental Study," American Economic Review, American Economic Association, vol. 85(5), pages 1313-26, December.
  16. 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.
  17. 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.
  18. 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.
  19. 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..
  20. Louviere,Jordan J. & Hensher,David A. & Swait,Joffre D. With contributions by-Name:Adamowicz,Wiktor, 2000. "Stated Choice Methods," Cambridge Books, Cambridge University Press, number 9780521788304, October.
Full references (including those not matched with items on IDEAS)

Citations

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:16765. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ekkehart Schlicht).

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

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