Identification in Stochastic Choice

2026-02-23

AI Summary

This study addresses the characterization of identification sets in stochastic choice models—including random utility, bounded rationality, and dynamic discrete choice frameworks—with the aim of determining whether distinct distributions over choice rules are observationally equivalent. The authors propose a unified analytical framework based on elementary permutation transformations, integrating tools from probability distribution transformations, convex analysis, and global inversion theory. For the first time, they fully characterize the inequality representations and extreme-point structures of identification sets across multiple model classes. Furthermore, they develop a globally valid identification test under smoothly varying parameters by leveraging global inversion techniques. This work not only establishes a rigorous theoretical foundation but also delivers a practical tool for conducting global identification tests in empirical applications.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Game Theory and Economic Paradigms: Social Choice / Voting
• Knowledge Representation and Reasoning: Preferences

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

We characterize the identified sets of a wide range of stochastic choice models, including random utility, various models of boundedly-rational behavior, and dynamic discrete choice. In each of these settings, we show two distributions over choice rules are observationally equivalent if and only if they can be obtained from one another via a finite sequence of simple swapping transforms. We leverage this to obtain complete descriptions of both the defining inequalities and extreme points of these identified sets. In cases where choice frequencies vary smoothly with some parameters, we provide a novel global-inverse result for practically testing identification.

Problem

• stochastic choice
• identification
• random utility
• bounded rationality
• dynamic discrete choice

Innovation

• stochastic choice
• identification
• swapping transforms
• random utility
• global inverse