# Robust Auction Design in the Auto-bidding World

arXiv: https://arxiv.org/abs/2111.02468 · HTML: https://mlsanity.com/paper/2111.02468/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2021.

## Built on by (most cited first, 21 of 21)

- [Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics](https://mlsanity.com/paper/2301.13306/) (2023)
- [How and Why to Manipulate Your Own Agent: On the Incentives of Users of Learning Agents](https://mlsanity.com/paper/2112.07640/) (2021)
- [Online Bidding Algorithms for Return-on-Spend Constrained Advertisers](https://mlsanity.com/paper/2208.13713/) (2022)
- [Multi-channel Autobidding with Budget and ROI Constraints](https://mlsanity.com/paper/2302.01523/) (2023)
- [GAS: Generative Auto-bidding with Post-training Search](https://mlsanity.com/paper/2412.17018/) (2024)
- [Online Learning in Contextual Second-Price Pay-Per-Click Auctions](https://mlsanity.com/paper/2310.05047/) (2023)
- [Optimal Return-to-Go Guided Decision Transformer for Auto-Bidding in Advertisement](https://mlsanity.com/paper/2506.21956/) (2025)
- [AIGB: Generative Auto-bidding via Conditional Diffusion Modeling](https://mlsanity.com/paper/2405.16141/) (2024)
- [Online Ad Procurement in Non-stationary Autobidding Worlds](https://mlsanity.com/paper/2307.05698/) (2023)
- [Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search](https://mlsanity.com/paper/2509.15927/) (2025)
- [Autobidding Arena: unified evaluation of the classical and RL-based autobidding algorithms](https://mlsanity.com/paper/2510.19357/) (2025)
- [PRO-Bid: Pareto-Prioritized Regret Optimization for Constraint-Aware Generative Auto-Bidding](https://mlsanity.com/paper/2602.08261/) (2026)
- [RTB Formulation Using Point Process](https://mlsanity.com/paper/2308.09122/) (2023)
- [Online Bidding Algorithms with Strict Return on Spend (ROS) Constraint](https://mlsanity.com/paper/2502.05599/) (2025)
- [Smart Fast Finish: Preventing Overdelivery via Daily Budget Pacing at DoorDash](https://mlsanity.com/paper/2509.07929/) (2025)
- [Auto-bidding under Return-on-Spend Constraints with Uncertainty Quantification](https://mlsanity.com/paper/2509.16324/) (2025)
- [Efficiency of Proportional Mechanisms in Online Auto-Bidding Advertising](https://mlsanity.com/paper/2604.12799/) (2026)
- [DRIVE: Distributional and Retrieval-Augmented Bidding with Value Evaluation](https://mlsanity.com/paper/2606.14192/) (2026)
- [AIGB-R1: Self-Evolving Generative Auto-Bidding via Hierarchical Planner-Executor Optimization](https://mlsanity.com/paper/2607.17281/) (2026)
- [Beyond Single-Episode Optimization: Sliding-Window Aware Generative Auto-Bidding for Long-Term Advertising Effectiveness](https://mlsanity.com/paper/2607.25233/) (2026)
- [Inference Auctions](https://mlsanity.com/paper/2609.40070/) (2026)

## Abstract (arXiv)

In classic auction theory, reserve prices are known to be effective for improving revenue for the auctioneer against quasi-linear utility maximizing bidders. The introduction of reserve prices, however, usually do not help improve total welfare of the auctioneer and the bidders. In this paper, we focus on value maximizing bidders with return on spend constraints -- a paradigm that has drawn considerable attention recently as more advertisers adopt auto-bidding algorithms in advertising platforms -- and show that the introduction of reserve prices has a novel impact on the market. Namely, by choosing reserve prices appropriately the auctioneer can improve not only the total revenue but also the total welfare. Our results also demonstrate that reserve prices are robust to bidder types, i.e., reserve prices work well for different bidder types, such as value maximizers and utility maximizers, without using bidder type information. We generalize these results for a variety of auction mechanisms such as VCG, GSP, and first-price auctions. Moreover, we show how to combine these results with additive boosts to improve the welfare of the outcomes of the auction further. Finally, we complement our theoretical observations with an empirical study confirming the effectiveness of these ideas using data from online advertising auctions.