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


  • Hongtao Lv Shandong University Shanghai Jiao Tong University
  • Zhilin Zhang Alibaba Group
  • Zhenzhe Zheng Shanghai Jiao Tong University
  • Jinghan Liu Shanghai Jiao Tong University
  • Chuan Yu Alibaba Group
  • Lei Liu Shandong University
  • Lizhen Cui Shandong University
  • Fan Wu Shanghai Jiao Tong University




GTEP: Mechanism Design, APP: Business/Marketing/Advertising/E-Commerce, GTEP: Game Theory


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 5/4 that we also prove. The designed auction mechanism is a generalization of VCG for utility maximizers and GSP for value maximizers.




How to Cite

Lv, H., Zhang, Z., Zheng, Z., Liu, J., Yu, C., Liu, L., Cui, L., & Wu, F. (2023). Utility Maximizer or Value Maximizer: Mechanism Design for Mixed Bidders in Online Advertising. Proceedings of the AAAI Conference on Artificial Intelligence, 37(5), 5789-5796. https://doi.org/10.1609/aaai.v37i5.25718



AAAI Technical Track on Game Theory and Economic Paradigms