Artificial Intelligence and Auction Design

• URL: http://arxiv.org/abs/2202.05947v1
• Date: Sat, 12 Feb 2022 00:54:40 GMT
• Title: Artificial Intelligence and Auction Design
• Authors: Martino Banchio, Andrzej Skrzypacz
• Abstract summary: We find that first-price auctions with no additional feedback lead to tacit-collusive outcomes. We show that the difference is driven by the incentive in first-price auctions to outbid opponents by just one bid increment. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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