Stochastic Choice and Revealed Perturbed Utility

Perturbed utility functions—the sum of expected utility and a non-linear perturba-tion function—provide a simple and tractable way to model various sorts of stochastic choice. We provide easily understood conditions that characterize this representation by generalizing the acyclicity condition used in revealed preference theory. We show how to relax Luce’s IIA condition to model cases where the agent finds it harder to discriminate between items in larger menus, and how to extend the perturbation-function approach to model choice overload and nested decisions. We also show that these representations correspond to a form of ambiguity-averse preferences for an agent who is uncertain about her true utility