The Evolution of Algorithmic Learning Rules: A Global Stability Result

This file is part of IDEAS, which uses RePEc data

The Evolution of Algorithmic Learning Rules: A Global Stability Result

Author Info

Luca Anderlini (St. John's College, Cambridge)
Hamid Sabourian (King's College, Cambridge)

Additional information is available for the following registered author(s):

Abstract

This paper consider the dynamic evolution of algorithmic (recursive) learning rules in a normal form game. It is shown that the system - the population frequencies - is globally stable for any arbitrary N-player normal form game, if the evolutionary process is algorithmic and the `birth process' guarantees that an appropriate set of `smart' rules is present in the population. The result is independent of the nature of the evolutionary process; in particular it does not require the dynamics of the system to be `monotonic in payoffs' - those rules which do better in terms of payoffs grow faster than those who do less well. The paper also demonstrates that any limit point of the distribution of actions in such an evolutionary process corresponds to a Nash equilibrium (pure or mixed) of the underlying game if the dynamics of the system are continuous and monotonic in payoffs.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. 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.

Publisher Info
Paper provided by EconWPA in its series Game Theory and Information with number 9510001.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length: 53 pages
Date of creation: 12 Oct 1995
Date of revision:
Handle: RePEc:wpa:wuwpga:9510001

Note: Type of Document - LaTex/PostScript; prepared on EmTex; to print on PostScript; pages: 53 ; figures: included
Contact details of provider:
Web page: http://129.3.20.41

For technical questions regarding this item, or to correct its listing, contact: (EconWPA).

Other versions of this item:

Paper
- Anderlini, L & Sabourian, H, 1996. "The Evolution of Algorithmic Learning Rules : A Global Stability Result," Economics Working Papers eco96/05, European University Institute.

Find related papers by JEL classification:
C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
C79 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Other
D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information

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.:

Blume, Lawrence & Easley, David, 1992. "Evolution and market behavior," Journal of Economic Theory, Elsevier, vol. 58(1), pages 9-40, October. [Downloadable!] (restricted)

Full references

Statistics
Access and download statistics

Did you know? There are over 21000 authors registered on RePEc Author Service.

This page was last updated on 2009-12-2.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.