# GSP - The Cinderella of Mechanism Design

arXiv: https://arxiv.org/abs/1701.05946 · HTML: https://mlsanity.com/paper/1701.05946/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2017.

## Built on by (most cited first, 5 of 5)

- [Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising](https://mlsanity.com/paper/2106.03593/) (2021)
- [LLM-Auction: Generative Auction towards LLM-Native Advertising](https://mlsanity.com/paper/2512.10551/) (2025)
- [Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search](https://mlsanity.com/paper/2106.04075/) (2021)
- [Hierarchically Constrained Adaptive Ad Exposure in Feeds](https://mlsanity.com/paper/2205.15759/) (2022)
- [Mechanism Design for Ad Auctions with Display Prices](https://mlsanity.com/paper/2303.13192/) (2023)

## Abstract (arXiv)

Nearly fifteen years ago, Google unveiled the generalized second price (GSP) auction. By all theoretical accounts including their own [Varian 14], this was the wrong auction --- the Vickrey-Clarke-Groves (VCG) auction would have been the proper choice --- yet GSP has succeeded spectacularly. We give a deep justification for GSP's success: advertisers' preferences map to a model we call value maximization, they do not maximize profit as the standard theory would believe. For value maximizers, GSP is the truthful auction [Aggarwal 09]. Moreover, this implies an axiomatization of GSP --- it is an auction whose prices are truthful for value maximizers --- that can be applied much more broadly than the simple model for which GSP was originally designed. In particular, applying it to arbitrary single-parameter domains recovers the folklore definition of GSP. Through the lens of value maximization, GSP metamorphosizes into a powerful auction, sound in its principles and elegant in its simplicity.