MICRO/THEORY: Updating under Imprecise Information; Dr Yi-Hsuan Lin (Institute of Economics, Academia Sinica (Taiwan))

Abstract

This paper models an agent that ranks actions with uncertain payoffs after observing a signal that could have been generated by multiple objective information structures. Under the assumption that the agent’s preferences conform to the multiple priors model (Gilboa and Schmeidler (1989)), we show that a simple behavioral axiom characterizes a generalization of Bayesian updating. Our axiom requires that whenever all possible sources of information agree that it is more “likely” for an action with uncertain payoffs to be better than one with certain payoffs, the agent prefers the former. We also provide axiomatizations for various special cases. Additionally, we explore a scenario where the informational content of a signal is purely subjective. We analyze the existence of a subjective set of information structures under full Bayesian updating for two extreme cases: (i) No ex-ante state ambiguity, and (ii) No signal imprecision.

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