Social Science Job Candidate

We propose a model of backward induction with a decision maker who has limited understanding of continuation payoffs and her own future behavior. To an outside observer, her behavior appears stochastic and her choices become imperfect signals of her payoffs. Our axioms yield a two-parameter representation of the decision maker's behavior; one parameter characterizes her attitude towards complexity; i.e., her willingness to choose more complicated subtrees over simpler, shorter paths, the other her error-proneness. Our model nests fully rational backward induction as a limit of these parameters. We introduce and analyze a measure of complexity aversion and a measure of error-proneness. We apply our model to product assortment and advertising problems. Among others, we analyze two foundational features of advertising. One repeats an advertised option in the choice problem, and the other singles out an advertised option.