Autobidder's Dilemma: Why More Sophisticated Autobidders Lead to Worse Auction Efficiency

Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo

Keywords: ad auctions, autobidding, non-uniform bidding, price of anarchy

TL;DR: We show that automated first-price auctions become more inefficient with more sophisticated autobidders.

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.

Primary Area: Algorithmic game theory

Submission Number: 11679