Learning in a black box
Heinrich H. Nax,
Maxwell N. Burton-Chellew,
Stuart A. West and
H. Peyton Young
LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library
Abstract:
We study behavior in repeated interactions when agents have no information about the structure of the underlying game and they cannot observe other agents’ actions or payoffs. Theory shows that even when players have no such information, there are simple payoff-based learning rules that lead to Nash equilibrium in many types of games. A key feature of these rules is that subjects search differently depending on whether their payoffs increase, stay constant or decrease. This paper analyzes learning behavior in a laboratory setting and finds strong confirmation for these asymmetric search behaviors in the context of voluntary contribution games. By varying the amount of information we show that these behaviors are also present even when subjects have full information about the game.
Keywords: Learning; Information; Public goods game (search for similar items in EconPapers)
JEL-codes: J1 (search for similar items in EconPapers)
Date: 2016-07-01
New Economics Papers: this item is included in nep-gth and nep-hpe
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)
Published in Journal of Economic Behavior & Organization, 1, July, 2016, 127, pp. 1-15. ISSN: 0167-2681
Downloads: (external link)
http://eprints.lse.ac.uk/68714/ Open access version. (application/pdf)
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: https://EconPapers.repec.org/RePEc:ehl:lserod:68714
Access Statistics for this paper
More papers in LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library LSE Library Portugal Street London, WC2A 2HD, U.K.. Contact information at EDIRC.
Bibliographic data for series maintained by LSERO Manager ().