• Yes

We propose and characterize a class of stochastic decision functions for a decision-maker who has a capacity for processing at most k-alternatives at a time. When faced with a menu containing more than k-alternatives, she randomly chooses a sub-menu of size k with uniform probability and selects the best alternative according to a strict ordering. For smaller menus, she chooses the best alternative according to the strict relation.

We introduce a model of inertial belief updating in a decision maker (DM), upon learning an event E ⊆ S, selects a posterior belief that minimizes the subjective distance between her prior and potential posteriors that assign probability one to event E. By varying the subjective distance between probability distributions, this model provides a unifying framework that nests three separate belief updating rules: (i) Bayesian updating, (ii) non-Bayesian updating rules such as the α − β rule (Grether, 1980), and (iii) updating rules for zero-probability events such as conditional probability systems of Myerson (1986a,b). We also show that our model is behaviorally equivalent to the Hypothesis Testing model (HT) of Ortoleva (2012).

Abstract We analyze choice behavior using Dynamic Random Utility Model (DRUM). Under DRUM, each consumer or decision-maker draws a utility function from a stochastic
utility process in each period and maximizes it subject to a menu. DRUM allows for unrestricted time correlation and cross-section heterogeneity in preferences. We fully
characterize DRUM when panel data on choices and menus are available. Our results cover consumer demand with a continuum of choices and finite discrete choice setups.
DRUM is linked to a finite mixture of deterministic behaviors that can be represented as the Kronecker product of static rationalizable behaviors. We exploit a generalization
of the Weyl-Minkowski theorem that uses this link and enables conversion of the characterizations of the static Random Utility Model (RUM) of McFadden-Richter
(1990) to its dynamic form. DRUM is more flexible than Afriat’s (1967) framework and more informative than RUM. In an application, we find that static utility maximization
fails to explain population behavior, but DRUM can explain it.