Learning in Random Utility Models Via Online Decision Problems
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913901323485184 |
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| author | Melo, Emerson |
| author_facet | Melo, Emerson |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16030 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning in Random Utility Models Via Online Decision Problems Melo, Emerson Theoretical Economics 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 |
| title | Learning in Random Utility Models Via Online Decision Problems |
| topic | Theoretical Economics |
| url | https://arxiv.org/abs/2506.16030 |