The Evolution of Algorithmic Learning Rules : A Global Stability Result
AbstractThis paper considers 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.
Download InfoTo our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
Bibliographic InfoPaper provided by European University Institute in its series Economics Working Papers with number eco96/05.
Length: 52 pages
Date of creation: 1996
Date of revision:
Contact details of provider:
Postal: Badia Fiesolana, Via dei Roccettini, 9, 50016 San Domenico di Fiesole (FI) Italy
Web page: http://www.eui.eu/ECO/
More information through EDIRC
ECONOMETRICS ; LEARNING;
Other versions of this item:
- Luca Anderlini & Hamid Sabourian, 1995. "The Evolution of Algorithmic Learning Rules: A Global Stability Result," Game Theory and Information 9510001, EconWPA.
- 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
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.
- Megiddo, Nimrod, 1989. "On computable beliefs of rational machines," Games and Economic Behavior, Elsevier, vol. 1(2), pages 144-169, June.
- Josephson, Jens, 2001.
"A Numerical Analysis of the Evolutionary Stability of Learning Rules,"
Working Paper Series in Economics and Finance
474, Stockholm School of Economics.
- Josephson, Jens, 2008. "A numerical analysis of the evolutionary stability of learning rules," Journal of Economic Dynamics and Control, Elsevier, vol. 32(5), pages 1569-1599, May.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Marcia Gastaldo).
If references are entirely missing, you can add them using this form.