Robustness and Uncertainty Aversion

Abstract

Max-min expected utility theory uses multiple prior distributions to represent a decision maker's uncertainty aversion. Robust control theory models a decision maker who fears that the data are generated by an unknown perturbation of his approximating model. We link the two approaches by interpreting the set of perturbations in robust control theory as the multiple priors of the max-min expected utility theory. Using a Brownian motion information structure, we construct recursive versions of max-min expected utility theory and robust control theory.

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