@inproceedings{
deng2024efficiency,
title={Efficiency of the First-Price Auction in the Autobidding World},
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=4DHoSjET4R}
}
We study the price of anarchy of first-price auctions in the autobidding world, where bidders can be either utility maximizers (i.e., traditional bidders) or value maximizers (i.e., autobidders).  We show that with autobidders only, the price of anarchy of first-price auctions is $1/2$, and with both kinds of bidders, the price of anarchy degrades to about $0.457$ (the precise number is given by an optimization).  These results complement the recent result by [Jin and Lu, 2022] showing that the price of anarchy of first-price auctions with traditional bidders is $1 - 1/e^2$.  We further investigate a setting where the seller can utilize machine-learned advice to improve the efficiency of the auctions.  There, we show that as the accuracy of the advice increases, the price of anarchy improves smoothly from about $0.457$ to $1$.
Efficiency of the First-Price Auction in the Autobidding World
Yuan Deng Jieming Mao Vahab Mirrokni Hanrui Zhang Song Zuo