This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

A Behavioral Learning Process in Games

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Laslier, J.-F.
Topol, R.
Walliser, B.

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

Abstract

The paper studies a behavioral learning process where an agent plays, at each period, an action with a probability which is proportional to the cumulative utility he got in the past with that action. The so-called CPR learning rule and the dynamic process it induces are formally stated and compared to other reinforcement rules as well as to fictitious play or the replicator dynamics.

Download Info
To 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.

Publisher Info
Paper provided by Paris X - Nanterre, U.F.R. de Sc. Ec. Gest. Maths Infor. in its series Papers with number 99-03.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length: 34 pages
Date of creation: 1999
Date of revision:
Handle: RePEc:fth:pnegmi:99-03

Contact details of provider:
Postal: THEMA, Universite de Paris X-Nanterre, U.F.R. de science economiques, gestion, mathematiques et informatique, 200, avenue de la Republique 92001 Nanterre CEDEX.

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

Related research
Keywords: LEARNING ; GAME THEORY ; BEHAVIOUR;

Other versions of this item:

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

Cited by:
(explanations, 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.)

  1. Ed Hopkins, 2004. "Two Competing Models of How People Learn in Games," ESE Discussion Papers 51, Edinburgh School of Economics, University of Edinburgh. [Downloadable!]
    Other versions:
  2. Antonella Ianni, 2007. "Learning Strict Nash Equilibria through Reinforcement," Economics Working Papers ECO2007/21, European University Institute. [Downloadable!]
  3. Friederike Mengel, 2007. "Learning Across Games," Working Papers. Serie AD 2007-05, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie). [Downloadable!]
  4. Peter Duersch & Albert Kolb & Joerg Oechssler & Burkhard Schipper, 2005. "Rage Against the Machines: How Subjects Learn to Play Against Computers," Game Theory and Information 0510012, EconWPA. [Downloadable!]
    Other versions:
Statistics
Access and download statistics

Did you know? You may want to explore EconPapers, which displays the same data as IDEAS in a different way.

This page was last updated on 2009-11-20.

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.