| Autor: |
Dütting, Paul, Feng, Zhe, Narasimham, Harikrishna, Parkes, David C., Ravindranath, Sal S |
| Přispěvatelé: |
Chaudhuri, Kamalika, Salakhutdinov, Ruslan |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
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| Popis: |
Designing an incentive compatible auction that maximizes expected revenue is an intricate task. The single-item case was resolved in a seminal piece of work by Myerson in 1981. Even after 30-40 years of intense research the problem remains unsolved for seemingly simple multibidder, multi-item settings. In this work, we initiate the exploration of the use of tools from deep learning for the automated design of optimal auctions. We model an auction as a multi-layer neural network, frame optimal auction design as a constrained learning problem, and show how it can be solved using standard pipelines. We prove generalization bounds and present extensive experiments, recovering essentially all known analytical solutions for multi-item settings, and obtaining novel mechanisms for settings in which the optimal mechanism is unknown. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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