SSE/EFI Working Paper Series in Economics and Finance
No 474:
A Numerical Analysis of the Evolutionary Stability of Learning Rules
Jens Josephson ()
Abstract: In this paper I define an evolutionary stability criterion
for learning rules. Using Monte Carlo simulations, I then apply this
criterion to a class of learning rules that can be represented by Camerer
and Ho's (1999) model of learning. This class contains perturbed versions
of reinforcement and belief learning as special cases. A large population
of individuals with learning rules in this class are repeatedly rematched
for a finite number of periods and play one out of four symmetric
two-player games. Belief learning is the only learning rule which is
evolutionarily stable in almost all cases, whereas reinforcement learning
is unstable in almost all cases. I also find that in certain games, the
stability of intermediate learning rules hinges critically on a parameter
of the model and the relative payoffs.
Keywords: Bounded rationality; Evolutionary game theory; Evolutionary Stability; Learning in games; Belief learning; Reinforcement learning.; (follow links to similar papers)
JEL-Codes: C72; C73; (follow links to similar papers)
29 pages, November 15, 2001
Before downloading any of the electronic versions below
you should read our statement on
copyright.
Download GhostScript
for viewing Postscript files and the
Acrobat Reader for viewing and printing pdf files.
Full text versions of the paper:
hastef0474.pdf 
(1.65MB)
Download Statistics
Questions (including download problems) about the papers in this series should be directed to Björn Thodenius ()
Report other problems with accessing this service to Sune Karlsson ()
or Björn Thodenius ().
Programing by
Design by Joachim Ekebom
