Munich Personal RePEc Archive

Learning in Bayesian Regulation

Koray, Semih and Saglam, Ismail (2005): Learning in Bayesian Regulation.

WarningThere is a more recent version of this item available.
[img]
Preview
PDF
MPRA_paper_1899.pdf

Download (230Kb) | Preview

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.

Available Versions of this Item

UB_LMU-Logo
MPRA is a RePEc service hosted by
the Munich University Library in Germany.