Auctions Between Regret-Minimizing Agents

Abstract

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 misreport 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.

Published in Proceedings of the ACM Web Conference 2022 (WWW '22)

References in corpus (5)

• Auctions Between Regret-Minimizing Agents
• Maximizing Welfare with Incentive-Aware Evaluation Mechanisms
• Nash Convergence of Mean-Based Learning Algorithms in First-Price Auctions
• Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions
• Stochastic Multiplicative Weights Updates in Zero-Sum Games

Cited by in corpus (4)

• Auctions Between Regret-Minimizing Agents
• Nash Convergence of Mean-Based Learning Algorithms in First-Price Auctions
• The Role of Transparency in Repeated First-Price Auctions with Unknown Valuations
• Liquid Welfare Guarantees for No-Regret Learning in Sequential Budgeted Auctions