Autobidder's Dilemma: Why More Sophisticated Autobidders Lead to Worse Auction Efficiency
Abstract
The recent increasing adoption of autobidding has inspired the growing interest in analyzing the performance of classic mechanism with value-maximizing autobidders both theoretically and empirically. It is known that optimal welfare can be obtained in first-price auctions if autobidders are restricted to uniform bid-scaling and the price of anarchy is $2$ when non-uniform bid-scaling strategies are allowed. In this paper, we provide a fine-grained price of anarchy analysis for non-uniform bid-scaling strategies in first-price auctions, demonstrating the reason why more powerful (individual) non-uniform bid-scaling strategies may lead to worse (aggregated) performance in social welfare. Our theoretical results match recent empirical findings that a higher level of non-uniform bid-scaling leads to lower welfare performance in first-price auctions.
Cite
Text
Deng et al. "Autobidder's Dilemma: Why More Sophisticated Autobidders Lead to Worse Auction Efficiency." Neural Information Processing Systems, 2024. doi:10.52202/079017-4111Markdown
[Deng et al. "Autobidder's Dilemma: Why More Sophisticated Autobidders Lead to Worse Auction Efficiency." Neural Information Processing Systems, 2024.](https://mlanthology.org/neurips/2024/deng2024neurips-autobidder/) doi:10.52202/079017-4111BibTeX
@inproceedings{deng2024neurips-autobidder,
title = {{Autobidder's Dilemma: Why More Sophisticated Autobidders Lead to Worse Auction Efficiency}},
author = {Deng, Yuan and Mao, Jieming and Mirrokni, Vahab and Zhang, Hanrui and Zuo, Song},
booktitle = {Neural Information Processing Systems},
year = {2024},
doi = {10.52202/079017-4111},
url = {https://mlanthology.org/neurips/2024/deng2024neurips-autobidder/}
}