We use a limited information environment to mimic the state of confusion in an experimental, repeated public goods game. The results show that reinforcement learning leads to dynamics similar to those observed in standard public goods games. However, closer inspection shows that individual decay of contributions in standard public goods games cannot be fully explained by reinforcement learning. According to our estimates, learning only accounts for 41 percent of the decay in contributions in standard public goods games. The contribution dynamics of subjects, who are identified as conditional cooperators, differ strongly from the learning dynamics, while a learning model estimated from the limited information treatment tracks behavior for subjects, who cannot be classified as conditional cooperators, reasonably well.
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 The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria in its series NRN working papers with number
2009-22.
Length: 33 pages Date of creation: Oct 2009 Date of revision: Handle: RePEc:jku:nrnwps:2009_22
Contact details of provider: Postal: NRN Labor Economics and the Welfare State, c/o Rudolf Winter-Ebmer, Altenbergerstr. 69, 4040 Linz Phone: +43-732-2468-8216 Fax: +43-732-2468-8217 Email: Web page: http://www.labornrn.at/ More information through EDIRC
For technical questions regarding this item, or to correct its listing, contact: (René Böheim).