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

arXiv: https://arxiv.org/abs/2006.14185 · HTML: https://mlsanity.com/paper/2006.14185/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [A Scalable Neural Network for DSIC Affine Maximizer Auction Design](https://mlsanity.com/paper/2305.12162/) (2023)
- [Differentiable Economics for Randomized Affine Maximizer Auctions](https://mlsanity.com/paper/2202.02872/) (2022)
- [Mechanism Design for Ad Auctions with Display Prices](https://mlsanity.com/paper/2303.13192/) (2023)
- [Optimal Auction Design in the Joint Advertising](https://mlsanity.com/paper/2507.07418/) (2025)

## Abstract (arXiv)

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).