Auction Design for Value Maximizers with Budget and Return-on-spend Constraints
Pinyan Lu, Chenyang Xu, Ruilong Zhang

TL;DR
This paper develops auction mechanisms for value-maximizing agents with budget and return-on-spend constraints, providing constant approximation guarantees in worst-case scenarios and improving previous results under different privacy settings.
Contribution
It introduces the first worst-case analysis of revenue-maximizing auctions for value maximizers with budget and ROS constraints, extending beyond Bayesian models.
Findings
Constant approximation mechanisms for unit-demand agents.
Constant approximation for additive agents under large market assumptions.
Improved approximation ratios in partially private settings.
Abstract
The paper designs revenue-maximizing auction mechanisms for agents who aim to maximize their total obtained values rather than the classical quasi-linear utilities. Several models have been proposed to capture the behaviors of such agents in the literature. In the paper, we consider the model where agents are subject to budget and return-on-spend constraints. The budget constraint of an agent limits the maximum payment she can afford, while the return-on-spend constraint means that the ratio of the total obtained value (return) to the total payment (spend) cannot be lower than the targeted bar set by the agent. The problem was first coined by [Balseiro et al., EC 2022]. In their work, only Bayesian mechanisms were considered. We initiate the study of the problem in the worst-case model and compare the revenue of our mechanisms to an offline optimal solution, the most ambitious…
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Taxonomy
TopicsAuction Theory and Applications · Consumer Market Behavior and Pricing · Housing Market and Economics
