Simple vs Optimal Mechanisms in Auctions with Convex Payments

Amy Greenwald; Takehiro Oyakawa; Vasilis Syrgkanis

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arXiv · 1702.06062

Simple vs Optimal Mechanisms in Auctions with Convex Payments

Amy Greenwald·Takehiro Oyakawa·Vasilis Syrgkanis

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Abstract

We investigate approximately optimal mechanisms in settings where bidders' utility functions are non-linear; specifically, convex, with respect to payments (such settings arise, for instance, in procurement auctions for energy). We provide constant factor approximation guarantees for mechanisms that are independent of bidders' private information (i.e., prior-free), and for mechanisms that rely to an increasing extent on that information (i.e., detail free). We also describe experiments, which show that for randomly drawn monotone hazard rate distributions, our mechanisms achieve at least 80\% of the optimal revenue, on average. Both our theoretical and experimental results show that in the convex payment setting, it is desirable to allocate across multiple bidders, rather than only to bidders with the highest (virtual) value, as in the traditional quasi-linear utility setting.

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BibTeX RIS

Amy Greenwald, Takehiro Oyakawa, Vasilis Syrgkanis. 2017-02-20. Simple vs Optimal Mechanisms in Auctions with Convex Payments. https://arxiv.org/abs/1702.06062

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