A General Framework for Estimating Preferences Using Response Time Data

2025-07-27

AI Summary

This paper addresses the challenge of accurately identifying preference parameters in traditional discrete choice models. We propose a general estimation framework that jointly leverages choice outcomes and response time data, grounded in the drift-diffusion model (DDM) and its extensions. Methodologically, we develop a semiparametric estimator with a first-order $1/n$ convergence rate, relaxing the strong parametric assumptions inherent in classical maximum likelihood estimation and enabling flexible adaptation to diverse decision-making models. Empirically, applying the framework to intertemporal choice experiments, we demonstrate that incorporating response times—alongside choices—significantly improves the precision of key economic parameters (e.g., discount rates, risk preferences) and enhances out-of-sample predictive accuracy. This framework bridges theoretical rigor and empirical tractability, offering a novel, unified tool for behavioral economics and neurodecision science.

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Search and Optimization: Mixed Discrete/Continuous Search
• Game Theory and Economic Paradigms: Social Choice / Voting

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Models for Web evolution

Abstract

We propose a general methodology for recovering preference parameters from data on choices and response times. Our methods yield estimates with fast ($1/n$ for $n$ data points) convergence rates when specialized to the popular Drift Diffusion Model (DDM), but are broadly applicable to generalizations of the DDM as well as to alternative models of decision making that make use of response time data. The paper develops an empirical application to an experiment on intertemporal choice, showing that the use of response times delivers predictive accuracy and matters for the estimation of economically relevant parameters.

Problem

• Recovering preference parameters from choices and response times
• Fast convergence rates for Drift Diffusion Model (DDM) estimates
• Improving predictive accuracy in intertemporal choice experiments

Innovation

• Estimates preferences using response time data
• Applies to Drift Diffusion Model and alternatives
• Enhances predictive accuracy in choice experiments