Economics at your fingertips  

Scaling Up Learning Models in Public Good Games

Jasmina Arifovic () and John Ledyard

Journal of Public Economic Theory, 2004, vol. 6, issue 2, 203-238

Abstract: We study three learning rules (reinforcement learning (RL), experience weighted attraction learning (EWA), and individual evolutionary learning (IEL)) and how they perform in three different Groves-Ledyard mechanisms. We are interested in how well these learning rules duplicate human behavior in repeated games with a continuum of strategies. We find that RL does not do well, IEL does significantly better, as does EWA, but only if given a small discretized strategy space. We identify four main features a learning rule should have in order to stack up against humans in a minimal competency test: (1) the use of hypotheticals to create history, (2) the ability to focus only on what is important, (3) the ability to forget history when it is no longer important, and (4) the ability to try new things. Copyright 2004 Blackwell Publishing Inc..

Date: 2004
References: Add references at CitEc
Citations: View citations in EconPapers (31) Track citations by RSS feed

Downloads: (external link) ... &year=2004&part=null link to full text (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=1097-3923

Access Statistics for this article

Journal of Public Economic Theory is currently edited by Rabah Amir, Gareth Myles and Myrna Wooders

More articles in Journal of Public Economic Theory from Association for Public Economic Theory Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().

Page updated 2020-08-12
Handle: RePEc:bla:jpbect:v:6:y:2004:i:2:p:203-238