# Artificial Intelligence and Auction Design

arXiv: https://arxiv.org/abs/2202.05947 · HTML: https://mlsanity.com/paper/2202.05947/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 12 Feb 2022.

## Builds on (most cited first, 3 of 3)

- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Auctions Between Regret-Minimizing Agents](https://mlsanity.com/paper/2110.11855/) (2021)
- [Learning to bid in revenue-maximizing auctions](https://mlsanity.com/paper/1902.10427/) (2019)

## Built on by (most cited first, 20 of 20)

- [Algorithmic Collusion by Large Language Models](https://mlsanity.com/paper/2404.00806/) (2024)
- [Strategic Collusion of LLM Agents: Market Division in Multi-Commodity Competitions](https://mlsanity.com/paper/2410.00031/) (2024)
- [An Economy of AI Agents](https://mlsanity.com/paper/2509.01063/) (2025)
- [EconEvals: Benchmarks and Litmus Tests for Economic Decision-Making by LLM Agents](https://mlsanity.com/paper/2503.18825/) (2025)
- [Cooperative AI via Decentralized Commitment Devices](https://mlsanity.com/paper/2311.07815/) (2023)
- [Learning to Bid in Non-Stationary Repeated First-Price Auctions](https://mlsanity.com/paper/2501.13358/) (2025)
- [Human Misperception of Generative-AI Alignment: A Laboratory Experiment](https://mlsanity.com/paper/2502.14708/) (2025)
- [Understanding Iterative Combinatorial Auction Designs via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2402.19420/) (2024)
- [Cheap Talking Algorithms](https://mlsanity.com/paper/2310.07867/) (2023)
- [Randomized Truthful Auctions with Learning Agents](https://mlsanity.com/paper/2411.09517/) (2024)
- [Solipsistic Superintelligence is Unlikely to be Cooperative](https://mlsanity.com/paper/2606.03237/) (2026)
- [Mitigating Retaliatory Algorithmic Collusion in Repeated Games](https://mlsanity.com/paper/2609.20548/) (2026)
- [Artificial Intelligence and Dual Contract](https://mlsanity.com/paper/2303.12350/) (2023)
- [Learned Collusion](https://mlsanity.com/paper/2304.12647/) (2023)
- [Advancing Ad Auction Realism: Practical Insights & Modeling Implications](https://mlsanity.com/paper/2307.11732/) (2023)
- [Algorithmic Collusion or Competition: the Role of Platforms' Recommender Systems](https://mlsanity.com/paper/2309.14548/) (2023)
- [Artificial Intelligence for Multi-Unit Auction design](https://mlsanity.com/paper/2404.15633/) (2024)
- [Learning to Coordinate Bidders in Non-Truthful Auctions](https://mlsanity.com/paper/2507.02801/) (2025)
- [Tacit Bidder-Side Collusion: Artificial Intelligence in Dynamic Auctions](https://mlsanity.com/paper/2511.21802/) (2025)
- [Persistent Partners Raise Prices Among Learning Agents](https://mlsanity.com/paper/2609.35402/) (2026)

## Abstract (arXiv)

Motivated by online advertising auctions, we study auction design in repeated auctions played by simple Artificial Intelligence algorithms (Q-learning). We find that first-price auctions with no additional feedback lead to tacit-collusive outcomes (bids lower than values), while second-price auctions do not. We show that the difference is driven by the incentive in first-price auctions to outbid opponents by just one bid increment. This facilitates re-coordination on low bids after a phase of experimentation. We also show that providing information about lowest bid to win, as introduced by Google at the time of switch to first-price auctions, increases competitiveness of auctions.