  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.
