Interaction dynamics of two reinforcement learners
The paper investigates a stochastic model where two agents (persons, companies, institutions, states, software agents or other) learn interactive behavior in a series of alternating moves. Each agent is assumed to perform “stimulus-response-consequence” learning, as studied in psychology. In the presented model, the response of one agent to the other agent's move is both the stimulus for the other agent's next move and part of the consequence for the other agent's previous move. After deriving general properties of the model, especially concerning convergence to limit cycles, we concentrate on an asymptotic case where the learning rate tends to zero (“slow learning”). In this case, the dynamics can be described by a system of deterministic differential equations. For reward structures derived from [2×2] bimatrix games, fixed points are determined, and for the special case of the prisoner's dilemma, the dynamics is analyzed in more detail on the assumptions that both agents start with the same or with different reaction probabilities. Copyright Springer-Verlag 2006
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Volume (Year): 14 (2006)
Issue (Month): 1 (February)
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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.:
- Ed Hopkins, 2000.
"Two Competing Models of How People Learn in Games,"
ESE Discussion Papers
51, Edinburgh School of Economics, University of Edinburgh.
- Michel BenaÔm & J–rgen W. Weibull, 2003.
"Deterministic Approximation of Stochastic Evolution in Games,"
Econometric Society, vol. 71(3), pages 873-903, 05.
- Benaim, Michel & Weibull, Jörgen W., 2000. "Deterministic Approximation of Stochastic Evolution in Games," Working Paper Series 534, Research Institute of Industrial Economics, revised 30 Oct 2001.
- Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, vol. 8(1), pages 164-212.
- Richard J. Herrnstein & Drazen Prelec, 1991. "Melioration: A Theory of Distributed Choice," Journal of Economic Perspectives, American Economic Association, vol. 5(3), pages 137-156, Summer.
- T. Borgers & R. Sarin, 2010.
"Learning Through Reinforcement and Replicator Dynamics,"
Levine's Working Paper Archive
380, David K. Levine.
- Borgers, Tilman & Sarin, Rajiv, 1997. "Learning Through Reinforcement and Replicator Dynamics," Journal of Economic Theory, Elsevier, vol. 77(1), pages 1-14, November.
- Tilman Börgers & Rajiv Sarin, "undated". "Learning Through Reinforcement and Replicator Dynamics," ELSE working papers 051, ESRC Centre on Economics Learning and Social Evolution.
- Ron Smith & Martin Sola & Fabio Spagnolo, 2000. "The Prisoner's Dilemma and Regime-Switching in the Greek-Turkish Arms Race," Journal of Peace Research, Peace Research Institute Oslo, vol. 37(6), pages 737-750, November.
- Laslier, Jean-Francois & Topol, Richard & Walliser, Bernard, 2001.
"A Behavioral Learning Process in Games,"
Games and Economic Behavior,
Elsevier, vol. 37(2), pages 340-366, November.
- J.-F. Laslier & R. Topol & B. Walliser, 1999. "A behavioral learning process in games," THEMA Working Papers 99-03, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
- Laslier, J.-F. & Topol, R. & Walliser, B., 1999. "A Behavioral Learning Process in Games," Papers 99-03, Paris X - Nanterre, U.F.R. de Sc. Ec. Gest. Maths Infor..
- Brian Skyrms & Robin Pemantle, 2004. "Learning to Network," Levine's Bibliography 122247000000000436, UCLA Department of Economics.
- M. Posch & A. Pichler & K. Sigmund, 1998. "The Efficiency of Adapting Aspiration Levels," Working Papers ir98103, International Institute for Applied Systems Analysis.
- Greenwald, Amy & Friedman, Eric J. & Shenker, Scott, 2001. "Learning in Network Contexts: Experimental Results from Simulations," Games and Economic Behavior, Elsevier, vol. 35(1-2), pages 80-123, April.
- John G. Cross, 1973. "A Stochastic Learning Model of Economic Behavior," The Quarterly Journal of Economics, Oxford University Press, vol. 87(2), pages 239-266.
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