论文标题
人工智能和拍卖设计
Artificial Intelligence and Auction Design
论文作者
论文摘要
由在线广告拍卖会激励，我们研究了由简单人工智能算法（Q-Learning）播放的重复拍卖中的拍卖设计。我们发现，没有额外反馈的第一价格拍卖会导致默认吸气结果（竞标低于值），而第二价格拍卖不会。我们表明，差异是由仅以一个竞标递增的一笔价格拍卖来超越对手的动机所驱动的。这促进了实验阶段后在低竞标中的重新协调。我们还表明，提供有关赢得最低竞标的信息，如Google在转为首价格拍卖时介绍的那样，提高了拍卖的竞争力。
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.
