Artificial Intelligence and Auction Design
| Autor: | Martino Banchio, Andrzej Skrzypacz |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
TheoryofComputation_MISCELLANEOUS
FOS: Economics and business FOS: Computer and information sciences Artificial Intelligence (cs.AI) Computer Science - Artificial Intelligence Computer Science - Computer Science and Game Theory Economics - Theoretical Economics TheoryofComputation_GENERAL Theoretical Economics (econ.TH) Computer Science and Game Theory (cs.GT) |
| Zdroj: | Proceedings of the 23rd ACM Conference on Economics and Computation. |
| DOI: | 10.1145/3490486.3538244 |
| Popis: | Motivated by online advertising auctions, we study auction design in repeated auctions played by simple Artificial Intelligence algorithms (Q-learning). We find that first-price auctions with no additional feedback lead to tacit-collusive outcomes (bids lower than values), while second-price auctions do not. We show that the difference is driven by the incentive in first-price auctions to outbid opponents by just one bid increment. This facilitates re-coordination on low bids after a phase of experimentation. We also show that providing information about lowest bid to win, as introduced by Google at the time of switch to first-price auctions, increases competitiveness of auctions. Comment: 30 pages, 11 figures |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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