# Auctions Between Regret-Minimizing Agents

arXiv: https://arxiv.org/abs/2110.11855 · HTML: https://mlsanity.com/paper/2110.11855/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 78 references that are in the map.
- First posted on arXiv 22 Oct 2021.

## Builds on (most cited first, 26 of 26)

- [Strategic Classification](https://mlsanity.com/paper/1506.06980/) (2015)
- [Strategic Classification from Revealed Preferences](https://mlsanity.com/paper/1710.07887/) (2017)
- [Strategic Classification in the Dark](https://mlsanity.com/paper/2102.11592/) (2021)
- [Strategic Classification Made Practical](https://mlsanity.com/paper/2103.01826/) (2021)
- [Strategizing against No-regret Learners](https://mlsanity.com/paper/1909.13861/) (2019)
- [Maximizing Welfare with Incentive-Aware Evaluation Mechanisms](https://mlsanity.com/paper/2011.01956/) (2020)
- [Selling to a No-Regret Buyer](https://mlsanity.com/paper/1711.09176/) (2017)
- [A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers](https://mlsanity.com/paper/1806.00955/) (2018)
- [Optimum Statistical Estimation with Strategic Data Sources](https://mlsanity.com/paper/1408.2539/) (2014)
- [Econometrics for Learning Agents](https://mlsanity.com/paper/1505.00720/) (2015)
- [Learning in Auctions: Regret is Hard, Envy is Easy](https://mlsanity.com/paper/1511.01411/) (2015)
- [Towards Efficient Auctions in an Auto-bidding World](https://mlsanity.com/paper/2103.13356/) (2021)
- [Bid Prediction in Repeated Auctions with Learning](https://mlsanity.com/paper/2007.13193/) (2020)
- [Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions](https://mlsanity.com/paper/2009.06136/) (2020)
- [The Price of Anarchy in Auctions](https://mlsanity.com/paper/1607.07684/) (2016)
- [Optimally Deceiving a Learning Leader in Stackelberg Games](https://mlsanity.com/paper/2006.06566/) (2020)
- [Imitative Follower Deception in Stackelberg Games](https://mlsanity.com/paper/1903.02917/) (2019)
- [Correlated and Coarse equilibria of Single-item auctions](https://mlsanity.com/paper/1601.07702/) (2016)
- [Manipulating a Learning Defender and Ways to Counteract](https://mlsanity.com/paper/1905.11759/) (2019)
- [Nash Convergence of Mean-Based Learning Algorithms in First-Price Auctions](https://mlsanity.com/paper/2110.03906/) (2021)
- [An Experimental Evaluation of Regret-Based Econometrics](https://mlsanity.com/paper/1605.03838/) (2016)
- [Stochastic Multiplicative Weights Updates in Zero-Sum Games](https://mlsanity.com/paper/2110.02134/) (2021)
- [A "Quantal Regret" Method for Structural Econometrics in Repeated Games](https://mlsanity.com/paper/1702.04254/) (2017)
- [Response Prediction for Low-Regret Agents](https://mlsanity.com/paper/1911.02056/) (2019)
- [A Dynamic Axiomatic Approach to First-Price Auctions](https://mlsanity.com/paper/1304.7718/) (2013)
- [Expressiveness and Robustness of First-Price Position Auctions](https://mlsanity.com/paper/1307.5216/) (2013)

## Built on by (most cited first, 22 of 22)

- [Algorithmic Collusion by Large Language Models](https://mlsanity.com/paper/2404.00806/) (2024)
- [Contracting with a Learning Agent](https://mlsanity.com/paper/2401.16198/) (2024)
- [Artificial Intelligence and Auction Design](https://mlsanity.com/paper/2202.05947/) (2022)
- [Learning When to Advise Human Decision Makers](https://mlsanity.com/paper/2209.13578/) (2022)
- [How and Why to Manipulate Your Own Agent: On the Incentives of Users of Learning Agents](https://mlsanity.com/paper/2112.07640/) (2021)
- [The Role of Transparency in Repeated First-Price Auctions with Unknown Valuations](https://mlsanity.com/paper/2307.09478/) (2023)
- [Strategizing against No-Regret Learners in First-Price Auctions](https://mlsanity.com/paper/2402.08637/) (2024)
- [Maximizing utility in multi-agent environments by anticipating the behavior of other learners](https://mlsanity.com/paper/2407.04889/) (2024)
- [Cooperative AI via Decentralized Commitment Devices](https://mlsanity.com/paper/2311.07815/) (2023)
- [Algorithmic Collusion Without Threats](https://mlsanity.com/paper/2409.03956/) (2024)
- [Learning to Manipulate a Commitment Optimizer](https://mlsanity.com/paper/2302.11829/) (2023)
- [Learning in Repeated Multi-Unit Pay-As-Bid Auctions](https://mlsanity.com/paper/2307.15193/) (2023)
- [Games with Payments between Learning Agents](https://mlsanity.com/paper/2405.20880/) (2024)
- [Learning to Play Against Unknown Opponents](https://mlsanity.com/paper/2412.18297/) (2024)
- [Adaptive maximization of social welfare](https://mlsanity.com/paper/2310.09597/) (2023)
- [Best of Both Worlds: Regret Minimization versus Minimax Play](https://mlsanity.com/paper/2502.11673/) (2025)
- [From No-Regret to Strategically Robust Learning in Repeated Auctions](https://mlsanity.com/paper/2601.03853/) (2026)
- [Markets with Heterogeneous Agents: Dynamics and Survival of Bayesian vs. No-Regret Learners](https://mlsanity.com/paper/2502.08597/) (2025)
- [Learning in Strategic Queuing Systems with Small Buffers](https://mlsanity.com/paper/2502.08898/) (2025)
- [Breaking Algorithmic Collusion in Human-AI Ecosystems](https://mlsanity.com/paper/2511.21935/) (2025)
- [Barriers to Welfare Maximization with No-Regret Learning](https://mlsanity.com/paper/2411.01720/) (2024)
- [Mean-based algorithms: A lower bound and regret](https://mlsanity.com/paper/2606.04931/) (2026)

## Abstract (arXiv)

We analyze a scenario in which software agents implemented as regret-minimizing algorithms engage in a repeated auction on behalf of their users. We study first-price and second-price auctions, as well as their generalized versions (e.g., as those used for ad auctions). Using both theoretical analysis and simulations, we show that, surprisingly, in second-price auctions the players have incentives to misreport their true valuations to their own learning agents, while in the first-price auction it is a dominant strategy for all players to truthfully report their valuations to their agents.