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

TL;DR

This paper introduces a linear programming heuristic to optimize affine maximizer auctions, specifically applied to revenue maximization in zero-day exploit markets, achieving near-optimal results with two agents.

Contribution

It presents a novel linear programming approach to optimize AMA mechanisms and demonstrates its effectiveness in zero-day exploit markets for two agents.

Findings

The heuristic produces near-optimal revenue in zero-day exploit markets with two agents.

AMA mechanisms are strategy-proof and individually rational, characterized by adjustable parameters.

The approach effectively turns mechanism design into a parameter optimization problem.

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).