A numerical analysis of the evolutionary stability of learning rules
In this paper, we define an evolutionary stability criterion for learning rules. Using simulations, we then apply this criterion to three types of symmetric 2x2 games for a class of learning rules that can be represented by the parametric model of Camerer and Ho [1999. Experience-weighted attraction learning in normal form games. Econometrica 67, 827-874]. This class contains stochastic versions of reinforcement and fictitious play as extreme cases. We find that only learning rules with high or intermediate levels of hypothetical reinforcement are evolutionarily stable, but that the stable parameters depend on the game.
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.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
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.:
- Hanaki, Nobuyuki & Sethi, Rajiv & Erev, Ido & Peterhansl, Alexander, 2005.
Journal of Economic Behavior & Organization,
Elsevier, vol. 56(4), pages 523-542, April.
- Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
- Hopkins, Ed & Posch, Martin, 2005.
"Attainability of boundary points under reinforcement learning,"
Games and Economic Behavior,
Elsevier, vol. 53(1), pages 110-125, October.
- Ed Hopkins & Martin Posch, 2003. "Attainability of Boundary Points under Reinforcement Learning," ESE Discussion Papers 79, Edinburgh School of Economics, University of Edinburgh.
- Ed Hopkins & Martin Posch, 2003. "Attainability of Boundary Points under Reinforcement Learning," Levine's Working Paper Archive 506439000000000350, David K. Levine.
- Hopkins, E., 1995.
"Learning, Matching and Aggregation,"
95a20, Universite Aix-Marseille III.
- Ed Hopkins, 1995. "Learning, Matching and Aggregation," ESE Discussion Papers 2, Edinburgh School of Economics, University of Edinburgh.
- Ed Hopkins, "undated". "Learning, Matching and Aggregation," ELSE working papers 033, ESRC Centre on Economics Learning and Social Evolution.
- Ed Hopkins, "undated". "Learning, Matching and Aggregation," Department of Economics 1996 : II, Edinburgh School of Economics, University of Edinburgh.
- Ed Hopkins, 1995. "Learning, Matching and Aggregation," Game Theory and Information 9512001, EconWPA.
- Ed Hopkins, "undated". "Learning, Matching and Aggregation," Discussion Papers 1996-2, Edinburgh School of Economics, University of Edinburgh.
- Ed Hopkins, 2000.
"Two Competing Models of How People Learn in Games,"
ESE Discussion Papers
51, Edinburgh School of Economics, University of Edinburgh.
- Josef Hofbauer & William H. Sandholm, 2002. "On the Global Convergence of Stochastic Fictitious Play," Econometrica, Econometric Society, vol. 70(6), pages 2265-2294, November.
- Anderlini, L & Sabourian, H, 1996.
"The Evolution of Algorithmic Learning Rules : A Global Stability Result,"
Economics Working Papers
eco96/05, European University Institute.
- Luca Anderlini & Hamid Sabourian, 1995. "The Evolution of Algorithmic Learning Rules: A Global Stability Result," Game Theory and Information 9510001, EconWPA.
- Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
- Drew Fudenberg & David K. Levine, 1998.
"The Theory of Learning in Games,"
MIT Press Books,
The MIT Press,
edition 1, volume 1, number 0262061945.
- Cars H. Hommes, 2005.
"Heterogeneous Agent Models in Economics and Finance,"
Tinbergen Institute Discussion Papers
05-056/1, Tinbergen Institute.
- Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186 Elsevier.
- Arthur J. Robson, 2001. "The Biological Basis of Economic Behavior," Journal of Economic Literature, American Economic Association, vol. 39(1), pages 11-33, March.
- Timothy C. Salmon, 2001. "An Evaluation of Econometric Models of Adaptive Learning," Econometrica, Econometric Society, vol. 69(6), pages 1597-1628, November.
- Beggs, A.W., 2005.
"On the convergence of reinforcement learning,"
Journal of Economic Theory,
Elsevier, vol. 122(1), pages 1-36, May.
- Heller, Dana, 2004. "An evolutionary approach to learning in a changing environment," Journal of Economic Theory, Elsevier, vol. 114(1), pages 31-55, January.
- Nathaniel T Wilcox, 2006. "Theories of Learning in Games and Heterogeneity Bias," Econometrica, Econometric Society, vol. 74(5), pages 1271-1292, 09.
- Camerer, Colin F. & Ho, Teck-Hua & Chong, Juin-Kuan, 2002. "Sophisticated Experience-Weighted Attraction Learning and Strategic Teaching in Repeated Games," Journal of Economic Theory, Elsevier, vol. 104(1), pages 137-188, May.
- Teck H Ho & Colin Camerer & Juin-Kuan Chong, 2003. "Functional EWA: A one-parameter theory of learning in games," Levine's Working Paper Archive 506439000000000514, David K. Levine.
When requesting a correction, please mention this item's handle: RePEc:eee:dyncon:v:32:y:2008:i:5:p:1569-1599. 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: (Dana Niculescu)
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.