We analyze a scenario in which software agents implemented as regret-minimizing algorithms engage in a repeated auction on behalf of their users. We study first price and second price auctions, as well as their generalized versions (e.g., as those used for ad auctions). Using both theoretical analysis and simulations, we show that, surprisingly, in second price auctions the players have incentives to mis-report their true valuations to their own learning agents, while in the first price auction it is a dominant strategy for all players to truthfully report their valuations to their agents.

翻译：我们分析一种情景,即软件代理商作为最遗憾最小化算法的实施代表其用户进行反复拍卖。 我们研究第一价和第二价拍卖及其通用版本(例如用于拍卖的版本 ) 。 我们利用理论分析和模拟,以出人意料地表明,在第二次价格拍卖中,玩家有动机向自己的学习代理商错误地报告其真实估值,而在第一次价格拍卖中,所有玩家都将真实的估值报告给自己的代理商。