2022/02/12 by Martino Banchio, Andrzej Skrzypacz, Banchio, Martino +1 · 3 citations
Decision Sciences · Business, Management and Accounting · #Auction Theory and Applications #Consumer Market Behavior and Pricing #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.2202.05947
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.