Bernardo Modenesi
University of Michigan
Title
Unveiling Hidden Patterns in Agent Behavior with Discrete-Choice and Network Theory
Abstract
Many datasets in data science stem from agents making repeated choices over time, with each choice leading to an observable outcome. In this setup, we introduce a novel approach to uncover latent agent heterogeneity, enhancing our understanding of agent behavior and improving causal inference estimation. By combining discrete choice models with network theory, we develop a method to measure agent similarity based on their patterns of choice. This results in a network-based unsupervised clustering technique that groups agents with similar behaviors, offering an interpretable alternative to black-box machine learning clustering models, with explicit estimation assumptions. In this seminar, I will illustrate our approach using labor market data, where workers (agents) and jobs (choices) are represented as nodes in a bipartite network, with edges in this network denoting worker-job matches. By clustering workers based on their job choices, we can infer unobserved workers’ skills—an important factor in economic analysis. Through Bayesian estimation, we reveal latent groups of similar workers, which is used to make more accurate predictions of labor market outcomes and measurement of labor market discrimination, compared to models relying only on observable characteristics. This seminar will detail our methodological framework, estimation strategy, and practical applications for understanding and predicting agent-choice dynamics.
