Artificial Intelligence and Auction Design

Martino Banchio and Andrzej Skrzypacz

Papers from arXiv.org

Abstract: 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.

Date: 2022-02
New Economics Papers: this item is included in nep-ban, nep-big, nep-des, nep-exp, nep-gth, nep-mic and nep-reg
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Citations: View citations in EconPapers (9)

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