Non-uniform Bid-scaling and Equilibria for Different Auctions: An Empirical Study

Yuan Deng, Jieming Mao, Vahab Mirrokni, Yifeng Teng, Song Zuo

Keywords: Online Advertising, Auctions, Bidding

Abstract:

In recent years, the growing adoption of auto-bidding motivates the study of auction design with value-maximizing auto-bidders. It is known that under mild assumptions, uniform bid-scaling is an optimal bidding strategy in truthful auctions, e.g. Vickrey–Clarke–Groves auction (VCG) and the price of anarchy for VCG is 2. However, for other auction formats like First-price auction (FPA) and Generalized Second-price auction (GSP), uniform bid-scaling may not be an optimal bidding strategy, and bidders have incentives to deviate to adopt strategies with non-uniform bid-scaling. Moreover, FPA can achieve optimal welfare if restricted to uniform bid-scaling, while its price of anarchy becomes 2 when non-uniform bid-scaling strategies are allowed.

All these price of anarchy results have been focused on welfare approximation in the worst case scenarios. To complement theoretical understandings, we empirically study how different auction formats (FPA, GSP, VCG) with different levels of non-uniform bid-scaling perform in an auto-bidding world with a synthetic dataset for auctions. Our empirical findings include:

* For both uniform bid-scaling and non-uniform bid-scaling, FPA is better than GSP and GSP is better than VCG in terms of both welfare and profit;
* A higher level of non-uniform bid-scaling leads to lower welfare performance in both FPA and GSP, while different levels of non-uniform bid-scaling has no effect in VCG.

Our methodology of synthetic data generation may be of independent interests.

Track: Economics, Online Markets, and Human Computation

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Submission Guidelines Blind: Yes

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Student Author: No

Submission Number: 2068