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arXiv · 2110.11855

Auctions Between Regret-Minimizing Agents

Yoav Kolumbus·Noam Nisan
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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.

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BibTeXRIS

Yoav Kolumbus, Noam Nisan. 2021-10-22. Auctions Between Regret-Minimizing Agents. https://doi.org/10.1145/3485447.3512055

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