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2022

Artificial Intelligence and Auction Design

Banchio, Martino, Skrzypacz, Andrzej

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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.

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