mlsanityData to 14 Oct 2026

Auctions Between Regret-Minimizing Agents

arXiv 2110.11855 ↗First posted 22 Oct 2021cs.GT cs.AI cs.LGcited by 22

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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.

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

26 of 78 references.

References · 26

  1. 2015Strategic Classificationcited by 257
  2. 2017Strategic Classification from Revealed Preferencescited by 128
  3. 2021Strategic Classification in the Darkcited by 66
  4. 2021Strategic Classification Made Practicalcited by 59
  5. 2019Strategizing against No-regret Learnerscited by 50
  6. 2020Maximizing Welfare with Incentive-Aware Evaluation Mechanismscited by 46
  7. 2017Selling to a No-Regret Buyercited by 43
  8. 2018A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providerscited by 33
  9. 2014Optimum Statistical Estimation with Strategic Data Sourcescited by 28
  10. 2015Econometrics for Learning Agentscited by 23
  11. 2015Learning in Auctions: Regret is Hard, Envy is Easycited by 23
  12. 2021Towards Efficient Auctions in an Auto-bidding Worldcited by 19
14 more
  1. 2020Bid Prediction in Repeated Auctions with Learningcited by 14
  2. 2020Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctionscited by 14
  3. 2016The Price of Anarchy in Auctionscited by 12
  4. 2020Optimally Deceiving a Learning Leader in Stackelberg Gamescited by 11
  5. 2019Imitative Follower Deception in Stackelberg Gamescited by 9
  6. 2016Correlated and Coarse equilibria of Single-item auctionscited by 8
  7. 2019Manipulating a Learning Defender and Ways to Counteractcited by 8
  8. 2021Nash Convergence of Mean-Based Learning Algorithms in First-Price Auctionscited by 8
  9. 2016An Experimental Evaluation of Regret-Based Econometricscited by 4
  10. 2021Stochastic Multiplicative Weights Updates in Zero-Sum Gamescited by 4
  11. 2017A "Quantal Regret" Method for Structural Econometrics in Repeated Gamescited by 3
  12. 2019Response Prediction for Low-Regret Agentscited by 2
  13. 2013A Dynamic Axiomatic Approach to First-Price Auctionscited by 1
  14. 2013Expressiveness and Robustness of First-Price Position Auctionscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 5; 2024: 8; 2025: 4; 2026: 2.

22 papers.

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  1. 2024Algorithmic Collusion by Large Language Modelscited by 62
  2. 2024Contracting with a Learning Agentcited by 23
  3. 2022Artificial Intelligence and Auction Designcited by 20
  4. 2022Learning When to Advise Human Decision Makerscited by 18
  5. 2021How and Why to Manipulate Your Own Agent: On the Incentives of Users of Learning Agentscited by 17
  6. 2023The Role of Transparency in Repeated First-Price Auctions with Unknown Valuationscited by 15
  7. 2024Strategizing against No-Regret Learners in First-Price Auctionscited by 8
  8. 2024Maximizing utility in multi-agent environments by anticipating the behavior of other learnerscited by 8
  9. 2023Cooperative AI via Decentralized Commitment Devicescited by 7
  10. 2024Algorithmic Collusion Without Threatscited by 7
  11. 2023Learning to Manipulate a Commitment Optimizercited by 3
  12. 2023Learning in Repeated Multi-Unit Pay-As-Bid Auctionscited by 3
10 more
  1. 2024Games with Payments between Learning Agentscited by 3
  2. 2024Learning to Play Against Unknown Opponentscited by 3
  3. 2023Adaptive maximization of social welfarecited by 2
  4. 2025Best of Both Worlds: Regret Minimization versus Minimax Playcited by 2
  5. 2026From No-Regret to Strategically Robust Learning in Repeated Auctionscited by 2
  6. 2025Markets with Heterogeneous Agents: Dynamics and Survival of Bayesian vs. No-Regret Learnerscited by 1
  7. 2025Learning in Strategic Queuing Systems with Small Bufferscited by 1
  8. 2025Breaking Algorithmic Collusion in Human-AI Ecosystemscited by 1
  9. 2024Barriers to Welfare Maximization with No-Regret Learningcited by 0
  10. 2026Mean-based algorithms: A lower bound and regretcited by 0