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arXiv · 2006.14185

Optimizing Affine Maximizer Auctions via Linear Programming: an Application to Revenue Maximizing Mechanism Design for Zero-Day Exploits Markets

Mingyu Guo·Hideaki Hata·Ali Babar
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Abstract

Optimizing within the affine maximizer auctions (AMA) is an effective approach for revenue maximizing mechanism design. The AMA mechanisms are strategy-proof and individually rational (if the agents' valuations for the outcomes are nonnegative). Every AMA mechanism is characterized by a list of parameters. By focusing on the AMA mechanisms, we turn mechanism design into a value optimization problem, where we only need to adjust the parameters. We propose a linear programming based heuristic for optimizing within the AMA family. We apply our technique to revenue maximizing mechanism design for zero-day exploit markets. We show that due to the nature of the zero-day exploit markets, if there are only two agents (one offender and one defender), then our technique generally produces a near optimal mechanism: the mechanism's expected revenue is close to the optimal revenue achieved by the optimal strategy-proof and individually rational mechanism (not necessarily an AMA mechanism).

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Mingyu Guo, Hideaki Hata, Ali Babar. 2020-06-25. Optimizing Affine Maximizer Auctions via Linear Programming: an Application to Revenue Maximizing Mechanism Design for Zero-Day Exploits Markets. https://arxiv.org/abs/2006.14185

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