# Utility Maximizer or Value Maximizer: Mechanism Design for Mixed Bidders in Online Advertising

arXiv: https://arxiv.org/abs/2211.16251 · HTML: https://mlsanity.com/paper/2211.16251/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2022.

## Built on by (most cited first, 1 of 1)

- [JD-BP: A Joint-Decision Generative Framework for Auto-Bidding and Pricing](https://mlsanity.com/paper/2604.05845/) (2026)

## Abstract (arXiv)

Digital advertising constitutes one of the main revenue sources for online platforms. In recent years, some advertisers tend to adopt auto-bidding tools to facilitate advertising performance optimization, making the classical utility maximizer model in auction theory not fit well. Some recent studies proposed a new model, called value maximizer, for auto-bidding advertisers with return-on-investment (ROI) constraints. However, the model of either utility maximizer or value maximizer could only characterize partial advertisers in real-world advertising platforms. In a mixed environment where utility maximizers and value maximizers coexist, the truthful ad auction design would be challenging since bidders could manipulate both their values and affiliated classes, leading to a multi-parameter mechanism design problem. In this work, we address this issue by proposing a payment rule which combines the corresponding ones in classical VCG and GSP mechanisms in a novel way. Based on this payment rule, we propose a truthful auction mechanism with an approximation ratio of 2 on social welfare, which is close to the lower bound of at least frac54 that we also prove. The designed auction mechanism is a generalization of VCG for utility maximizers and GSP for value maximizers.