Optimal Learning with Endogenous Data

David Easley and Nicholas Kiefer ()

International Economic Review, 1989, vol. 30, issue 4, 963-78

Abstract: This paper is concerned with the need for, and the implications of, $-optimality in learning problems. The authors consider a control problem in which a Bayesian decisionmaker faces a trade-off between expected current reward and accumulation of information. An example showing the need for the notion of $-optimality and the possibility of discontinuous transition functions is given. It is shown that there is always an $-optimal policy that allows the decisionmaker to learn any identified parameters, but that there are other $-optimal policies with very different limit behavior. Copyright 1989 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Date: 1989
References: Add references at CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://links.jstor.org/sici?sici=0020-6598%2819891 ... O%3B2-O&origin=repec full text (application/pdf)
Access to full text is restricted to JSTOR subscribers. See http://www.jstor.org for details.

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:ier:iecrev:v:30:y:1989:i:4:p:963-78

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0020-6598

Access Statistics for this article

International Economic Review is currently edited by Harold L. Cole

More articles in International Economic Review from Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association 160 McNeil Building, 3718 Locust Walk, Philadelphia, PA 19104-6297. Contact information at EDIRC.
Bibliographic data for series maintained by Wiley-Blackwell Digital Licensing () and ().