Identifying Heterogeneous Decision Rules From Choices When Menus Are Unobserved

TL;DR

This paper develops methods to infer the distribution of decision rules and preferences from aggregate choice data with unobserved menus, strengthening existing identification results and extending applicability to belief updating contexts.

Contribution

It provides a generalized analytical framework for robustly identifying heterogeneous decision rules from limited choice data, applicable across various contexts.

Findings

Robust identification of preference distributions from aggregate choices.

Generalization of existing identification results.

Application to belief updating rule identification.

Abstract

Given only aggregate choice data and limited information about how menus are distributed across the population, we describe what can be inferred robustly about the distribution of preferences (or more general decision rules). We strengthen and generalize existing results on such identification and provide an alternative analytical approach to study the problem. We show further that our model and results are applicable, after suitable reinterpretation, to other contexts. One application is to the robust identification of the distribution of updating rules given only the population distribution of beliefs and limited information about heterogeneous information sources.