Learning in Bayesian Regulation

Semih Koray and Ismail Saglam

MPRA Paper from University Library of Munich, Germany

Abstract: We examine the issue of learning in a generalized principal-agent model with incomplete information. We show that there are situations in which the agent prefers a Bayesian regulator to have more information about his private type. Moreover, the outcome of the Bayesian mechanism regulating the agent is path-dependent; i.e. the convergence of the regulator's belief to the truth does not always yield the complete information outcome.

Keywords: Learning; Principle-Agent Model; Bayesian Regulation; Incomplete Information Learning (search for similar items in EconPapers)
JEL-codes: D82 D83 (search for similar items in EconPapers)
Date: 2005-04
New Economics Papers: this item is included in nep-reg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/1899/1/MPRA_paper_1899.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/2704/1/MPRA_paper_2704.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/4525/1/MPRA_paper_4525.pdf revised 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:pra:mprapa:1899

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().