Artificial Intelligence and Auction Design [article]

Martino Banchio, Andrzej Skrzypacz
2022 arXiv   pre-print
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
more » ... perimentation. 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.
arXiv:2202.05947v1 fatcat:pdnxp76j6jby7gxbeughh6fc3u