@inproceedings{
deng2024autobidders,
title={Autobidder's Dilemma: Why More Sophisticated Autobidders Lead to Worse Auction Efficiency},
author={Yuan Deng and Jieming Mao and Vahab Mirrokni and Hanrui Zhang and Song Zuo},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hQJksiskaa}
}
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.
Autobidder's Dilemma: Why More Sophisticated Autobidders Lead to Worse Auction Efficiency
Yuan Deng Jieming Mao Vahab Mirrokni Hanrui Zhang Song Zuo