Learning is Revelation in Disguise: Improved Regret and Equivalence Results for Dynamic Pricing
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866908995125510144 |
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| author | Zuo, Shiliang |
| author_facet | Zuo, Shiliang |
| contents | We study dynamic pricing where a seller repeatedly interacts with a strategic, non-myopic buyer who has a fixed private valuation and discounts future utility. Prior work focused exclusively on posted-price mechanisms, which only extract binary accept/reject signals. For our first result, we show that menu mechanisms-offering allocation-payment contracts are able to achieve $O(T_γ\log T_γ)$ regret, where $T_γ$ is the buyer's effective discounted time horizon, improving all prior bounds. Our second contribution is more conceptual in nature. The problem of dynamic pricing sits at the intersection of two paradigms: adaptive learning in computer science / machine learning and revelation-principle-based mechanism design in economics-yet their relationship has remained unclear. We establish a fundamental equivalence: indirect learning mechanisms and direct revelation mechanisms achieve identical optimal regret. The adaptive, data-driven algorithms of online learning and explicit type elicitation are two languages towards solving the same problem; hence, learning is revelation in disguise. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_24093 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning is Revelation in Disguise: Improved Regret and Equivalence Results for Dynamic Pricing Zuo, Shiliang Computer Science and Game Theory We study dynamic pricing where a seller repeatedly interacts with a strategic, non-myopic buyer who has a fixed private valuation and discounts future utility. Prior work focused exclusively on posted-price mechanisms, which only extract binary accept/reject signals. For our first result, we show that menu mechanisms-offering allocation-payment contracts are able to achieve $O(T_γ\log T_γ)$ regret, where $T_γ$ is the buyer's effective discounted time horizon, improving all prior bounds. Our second contribution is more conceptual in nature. The problem of dynamic pricing sits at the intersection of two paradigms: adaptive learning in computer science / machine learning and revelation-principle-based mechanism design in economics-yet their relationship has remained unclear. We establish a fundamental equivalence: indirect learning mechanisms and direct revelation mechanisms achieve identical optimal regret. The adaptive, data-driven algorithms of online learning and explicit type elicitation are two languages towards solving the same problem; hence, learning is revelation in disguise. |
| title | Learning is Revelation in Disguise: Improved Regret and Equivalence Results for Dynamic Pricing |
| topic | Computer Science and Game Theory |
| url | https://arxiv.org/abs/2604.24093 |