Learning in Bayesian Regulation

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

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.

• Item Type: MPRA Paper
• Institution: Bogazici University
• Language: English
• Keywords: Learning; Principle-Agent Model; Bayesian Regulation; Incomplete Information Learning
• Subjects: D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D83 - Search; Learning; Information and Knowledge; Communication; Belief; D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D82 - Asymmetric and Private Information; Mechanism Design