Identifying Behavioral Types

2026-02-11

AI Summary

This study addresses the problem of identifying the distribution of latent behavioral types and their choice patterns from aggregate data when only group-level choices are observed and individual types are unobserved. Assuming minimal qualitative prior knowledge, the authors establish the first necessary and sufficient conditions for the identifiability of behavioral types. They characterize cross-type behavioral heterogeneity through an equivalence condition that combines a matching criterion with the algebraic structure of the mapping matrix linking types to aggregate outcomes. Leveraging tools from combinatorics and matrix algebra, the paper elucidates the mechanism by which type-specific behaviors map to aggregate data and demonstrates that, provided the data exhibit sufficient cross-type heterogeneity, both the latent types and their distribution are uniquely identifiable—thereby establishing a theoretical foundation for nonparametric identification in this setting.

Technology Category

Game Theory and Economic Paradigms: Behavioral Game TheoryMultiagent Systems: Mechanism DesignReasoning under Uncertainty: Relational Probabilistic Models

Application Category

Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphsEconomics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactionsUser Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

We study identification in models of aggregate choice generated by unobserved behavioral types. An analyst observes only aggregate choice behavior, while the population distribution of types and their type-level choice patterns are latent. Assuming only minimal and purely qualitative prior knowledge of the process generating type-level choice probabilities, we characterize necessary and sufficient conditions for identifiability. Identification obtains if and only if the data exhibit sufficient cross-type behavioral heterogeneity, which we characterize equivalently through combinatorial matching conditions between types and alternatives, and through algebraic properties of the matrices mapping type-level to aggregate choice behavior.

Problem

• identification
• behavioral types
• aggregate choice
• latent heterogeneity
• choice behavior

Innovation

• identification
• behavioral types
• aggregate choice
• combinatorial matching
• algebraic characterization