Bayesian learning and convergence to Nash equilibria without common priors
Economic Theory, 1998, vol. 11, issue 3, 643-655
Consider an infinitely repeated game where each player is characterized by a "type" which may be unknown to the other players in the game. Suppose further that each player's belief about others is independent of that player's type. Impose an absolute continuity condition on the ex ante beliefs of players (weaker than mutual absolute continuity). Then any limit point of beliefs of players about the future of the game conditional on the past lies in the set of Nash or Subjective equilibria. Our assumption does not require common priors so is weaker than Jordan (1991); however our conclusion is weaker, we obtain convergence to subjective and not necessarily Nash equilibria. Our model is a generalization of the Kalai and Lehrer (1993) model. Our assumption is weaker than theirs. However, our conclusion is also weaker, and shows that limit points of beliefs, and not actual play, are subjective equilibria.
JEL-codes: C70 C73 D81 D82 D83 D84 (search for similar items in EconPapers)
Note: Received: March 3, 1995; revised version: February 17, 1997
References: Add references at CitEc
Citations: View citations in EconPapers (9) Track citations by RSS feed
Downloads: (external link)
http://link.springer.de/link/service/journals/0019 ... 11003/80110643.ps.gz (application/postscript)
Access to the full text of the articles in this series is restricted
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:spr:joecth:v:11:y:1998:i:3:p:643-655
Ordering information: This journal article can be ordered from
http://www.springer. ... eory/journal/199/PS2
Access Statistics for this article
Economic Theory is currently edited by Nichoals Yanneils
More articles in Economic Theory from Springer, Society for the Advancement of Economic Theory (SAET) Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().