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arXiv · 2506.16030

Learning in Random Utility Models Via Online Decision Problems

Emerson Melo
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Abstract

This paper examines the Random Utility Model (RUM) in repeated stochastic choice settings where decision-makers lack full information about payoffs. We propose a gradient-based learning algorithm that embeds RUM into an online decision-making framework. Our analysis establishes Hannan consistency for a broad class of RUMs, meaning the average regret relative to the best fixed action in hindsight vanishes over time. We also show that our algorithm is equivalent to the Follow-The-Regularized-Leader (FTRL) method, offering an economically grounded approach to online optimization. Applications include modeling recency bias and characterizing coarse correlated equilibria in normal-form games

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Emerson Melo. 2025-06-19. Learning in Random Utility Models Via Online Decision Problems. https://arxiv.org/abs/2506.16030

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