2022/02/12 by Martino Banchio, Andrzej Skrzypacz, Banchio, Martino +1 · 5 citations
Business, Management and Accounting · Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Auction Theory and Applications #Auction theory #Business #Combinatorial auction #Common value auction #Computer science #Consumer Market Behavior and Pricing #Economics #English auction #Forward auction #Incentive #Microeconomics #Stock Market Forecasting Methods #Unique bid auction #cs.AI #cs.GT #econ.TH
paper · pdf · doi:10.48550/arxiv.2202.05947
published in arXiv (Cornell University) (Cornell University) · 30 pages, 11 figures
arxiv created 2022/02/12 · openalex publication_date 2022/02/12 · arxiv updated 2022/02/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.