Learning in Bayesian Regulation

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.

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Year of publication:	2005-04
Authors:	Koray, Semih ; Saglam, Ismail
Institutions:	Volkswirtschaftliche Fakultät, Ludwig-Maximilians-Universität München
Subject:	Learning \| Principle-Agent Model \| Bayesian Regulation \| Incomplete Information Learning

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Extent:	application/pdf
Series:	MPRA Paper.
Type of publication:	Book / Working Paper
Classification:	D83 - Search, Learning, Information and Knowledge ; D82 - Asymmetric and Private Information
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005616573