Learning in Bayesian Regulation
AbstractWe 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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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 1899.
Date of creation: Apr 2005
Date of revision:
Learning; Principle-Agent Model; Bayesian Regulation; Incomplete Information Learning;
Find related papers by JEL classification:
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
- D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
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