Dynamic and Stochastic Rational Behavior

TL;DR

This paper introduces the Dynamic Random Utility Model (DRUM), a flexible framework for analyzing decision-making over time that accounts for heterogeneity and correlation, improving upon static models like Afriat's and RUM.

Contribution

The paper develops DRUM, a novel dynamic utility model that generalizes static frameworks, and provides characterizations and applications demonstrating its effectiveness.

Findings

DRUM can explain population choice behavior where static models fail.

DRUM accommodates unrestricted time correlation and heterogeneity.

Static utility maximization is insufficient for dynamic, real-world data.

Abstract

The (static) utility maximization model of Afriat (1967), which is the standard in analysing choice behavior, is under scrutiny. We propose the Dynamic Random Utility Model (DRUM) that is more flexible than the framework of Afriat (1967) and more informative than the static Random Utility Model (RUM) framework of McFadden and Richter (1990). Under DRUM, each decision-maker randomly draws a utility function in each period and maximizes it subject to a menu. DRUM allows for unrestricted time correlation and cross-section heterogeneity in preferences. We characterize DRUM for situations when panel data on choices and menus are available. DRUM is linked to a finite mixture of deterministic behaviors that can be represented as a product of static rationalizable behaviors. This link allows us to convert the characterizations of the static RUM to its dynamic form. In an application, we find that although the static utility maximization model fails to explain population behavior, DRUM can explain it.