Equitable Auction Design: With and Without Distributions

TL;DR

This paper designs and analyzes auction mechanisms that ensure minority groups receive a fair share of allocations, comparing stochastic and regret-based approaches, and providing closed-form solutions and robustness analysis.

Contribution

It introduces a closed-form optimal stochastic mechanism and a regret-based mechanism for equitable auctions, with theoretical guarantees and practical insights.

Findings

The stochastic mechanism maximizes expected revenue with known distributions.

The regret-based mechanism minimizes worst-case regret without distribution assumptions.

The regret under the proposed mechanism is at most 1.31 times the optimal worst-case regret.

Abstract

We study a mechanism design problem where a seller aims to allocate a good to multiple bidders, each with a private value. The seller supports or favors a specific group, referred to as the minority group. Specifically, the seller requires that allocations to the minority group are at least a predetermined fraction (equity level) of those made to the rest of the bidders. Such constraints arise in various settings, including government procurement and corporate supply chain policies that prioritize small businesses, environmentally responsible suppliers, or enterprises owned by historically disadvantaged individuals. We analyze two variants of this problem: stochastic mechanism design, which assumes bidders' values follow a known distribution and seeks to maximize expected revenue, and regret-based mechanism design, which makes no distributional assumptions and aims to minimize the worst-case regret. We characterize a closed-form optimal stochastic mechanism and propose a closed-form regret-based mechanism, and establish that the ex-post regret under the latter is at most a constant multiple (dependent on the equity level) of the optimal worst-case regret. We further quantify that this approximation constant is at most 1.31 across different equity levels. Both mechanisms can be interpreted as set-asides, a common policy tool that reserves a fraction of goods for minority groups. Furthermore, numerical results demonstrate that the stochastic mechanism performs well when the bidders' value distribution is accurately estimated, while the regret-based mechanism exhibits greater robustness under estimation errors.