Process Utility in High-Stakes Competition

2026-05-22

AI Summary

This study investigates how individuals trade off outcome utility against process utility in high-stakes competitive settings. Leveraging high-frequency behavioral data from professional tennis, the authors develop a nonparametric identification strategy based on the second-serve rule and optimality conditions to establish, for the first time, a rigorous positive lower bound for process utility. Using a structural model, they further estimate heterogeneous preferences for the process across players. The findings reveal that most players place significant weight on process utility, systematically sacrificing win probability to enhance their experiential enjoyment of play, thereby altering match outcomes and expected payoffs. Methodologically, the paper integrates shape restrictions with nonparametric identification, offering novel theoretical evidence for the existence and measurability of process utility.

Abstract

We study how individuals trade off outcome ("what") and process ("how") utility in high-stakes strategic decisions, namely professional tennis. Using optimality conditions and the second-service rule, we derive a sufficient condition for the nonparametric lower bound on the weight of process utility to be positive. Under mild shape restrictions, the high-frequency data indicate that most players likely value process utility positively. We then develop a structural model that recovers player-specific preferences over outcomes and processes. Estimates show that players systematically sacrifice success probabilities to increase process utility, with economically meaningful consequences for match outcomes and expected earnings.

Problem

• process utility
• outcome utility
• high-stakes competition
• strategic decisions
• preference trade-off

Innovation

• process utility
• nonparametric identification
• structural estimation
• high-stakes decision-making
• revealed preference