mlsanityData to 7 Oct 2026

Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model

arXiv 2303.08613 ↗First posted 15 Mar 2023cs.LG cs.AI cs.GTcited by 7

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Abstract (arXiv)

We study the incentivized information acquisition problem, where a principal hires an agent to gather information on her behalf. Such a problem is modeled as a Stackelberg game between the principal and the agent, where the principal announces a scoring rule that specifies the payment, and then the agent then chooses an effort level that maximizes her own profit and reports the information. We study the online setting of such a problem from the principal's perspective, i.e., designing the optimal scoring rule by repeatedly interacting with the strategic agent. We design a provably sample efficient algorithm that tailors the UCB algorithm (Auer et al., 2002) to our model, which achieves a sublinear T2/3-regret after T iterations. Our algorithm features a delicate estimation procedure for the optimal profit of the principal, and a conservative correction scheme that ensures the desired agent's actions are incentivized. Furthermore, a key feature of our regret bound is that it is independent of the number of states of the environment.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 22 of its 55 references that are in the map. First posted on arXiv 15 Mar 2023.

Builds on

22 of 55 references.

References · 22

  1. 2018Is Q-learning Provably Efficient?cited by 485
  2. 2013Learning Prices for Repeated Auctions with Strategic Buyerscited by 37
  3. 2022The Sample Complexity of Online Contract Designcited by 35
  4. 2017Learning to Bid Without Knowing your Valuecited by 34
  5. 2020Dynamic Incentive-aware Learning: Robust Pricing in Contextual Auctionscited by 34
  6. 2021Learning Equilibria in Matching Markets from Bandit Feedbackcited by 34
  7. 2018Simple versus Optimal Contractscited by 33
  8. 2022Learning in Stackelberg Games with Non-myopic Agentscited by 29
  9. 2020Optimization of Scoring Rulescited by 28
  10. 2020Contracts under Moral Hazard and Adverse Selectioncited by 21
  11. 2020Mechanisms for a No-Regret Agent: Beyond the Common Priorcited by 18
  12. 2022Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learningcited by 18
10 more
  1. 2020The Complexity of Contractscited by 16
  2. 2020Binary Scoring Rules that Incentivize Precisioncited by 12
  3. 2021Learning to Persuade on the Fly: Robustness Against Ignorancecited by 12
  4. 2021Bayesian Agency: Linear versus Tractable Contractscited by 12
  5. 2021Contracts with Private Cost per Unit-of-Effortcited by 12
  6. 2022Optimal Scoring Rules for Multi-dimensional Effortcited by 12
  7. 2017On Stackelberg Mixed Strategiescited by 10
  8. 2022Designing Menus of Contracts Efficiently: The Power of Randomizationcited by 10
  9. 2022Contracts with Information Acquisition, via Scoring Rulescited by 8
  10. 2022No-Regret Learning in Partially-Informed Auctionscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 3; 2026: 1.

7 papers.

  1. 2023Principal-Agent Reward Shaping in MDPscited by 12
  2. 2024Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationalitycited by 3
  3. 2025Aligned Textual Scoring Rulescited by 2
  4. 2025Learn then Decide: A Learning Approach for Designing Data Marketplacescited by 1
  5. 2023No Bidding, No Regret: Pairwise-Feedback Mechanisms for Digital Goods and Data Auctionscited by 0
  6. 2025Provably Efficient Algorithm for Best Scoring Rule Identification in Online Principal-Agent Information Acquisitioncited by 0
  7. 2026Scoring Rules! Statistical and Strategic Alignment for Text Evaluation Metricscited by 0