Who is a Bayesian?
No 5861, Working Paper from Department of Economics, University of Pittsburgh
We take a decision theoretic approach to predictive inference. We construct a simple dynamic setup in the presence of inherent uncertainty. At any giventime period the decision maker updates her posterior regarding the uncertainty related to the subsequent period. The posteriors reflect the decision maker's preferences, period by period. We study the evolution of the agent's posteriors and provideaxioms under which the decision maker exhibits learning in a Bayesian fashion. Weshow how behavioral implications of diff erent Bayesian models diff er from one another, and specifi cally from those dictated by exchangeability.
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