Principal-Agent Hypothesis Testing
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Abstract (arXiv)
Consider the relationship between a regulator (the principal) and an experimenter (the agent) such as a pharmaceutical company. The pharmaceutical company wishes to sell a drug for profit, whereas the regulator wishes to allow only efficacious drugs to be marketed. The efficacy of the drug is not known to the regulator, so the pharmaceutical company must run a costly trial to prove efficacy to the regulator. Critically, the statistical protocol used to establish efficacy affects the behavior of a strategic, self-interested agent; a lower standard of statistical evidence incentivizes the agent to run more trials that are less likely to be effective. The interaction between the statistical protocol and the incentives of the pharmaceutical company is crucial for understanding this system and designing protocols with high social utility. In this work, we discuss how the regulator can set up a protocol with payoffs based on statistical evidence. We show how to design protocols that are robust to an agent's strategic actions, and derive the optimal protocol in the presence of strategic entrants.
Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 7 of its 49 references that are in the map. First posted on arXiv 13 May 2022.
Builds on
7 of 49 references.
References · 7
- 2019Safe Testingcited by 101
- 2014Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problemscited by 46
- 2018Simple versus Optimal Contractscited by 33
- 2020Contracts under Moral Hazard and Adverse Selectioncited by 21
- 2020The Complexity of Contractscited by 16
- 2021Bayesian Agency: Linear versus Tractable Contractscited by 12
- 2021Contracts with Private Cost per Unit-of-Effortcited by 12
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Citing papers per year: 2022: 2; 2023: 5; 2024: 1; 2025: 3; 2026: 3.
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- 2022The Sample Complexity of Online Contract Designcited by 35
- 2023Delegated Classificationcited by 10
- 2024Incentivizing Quality Text Generation via Statistical Contractscited by 10
- 2023Online Learning in a Creator Economycited by 8
- 2023Delegating Data Collection in Decentralized Machine Learningcited by 8
- 2023Incentive-Theoretic Bayesian Inference for Collaborative Sciencecited by 5
- 2022Solving Constrained Variational Inequalities via a First-order Interior Point-based Methodcited by 3
- 2025Poisoning Bayesian Inference via Data Deletion and Replicationcited by 3
- 2025Instance-Adaptive Hypothesis Tests with Heterogeneous Agentscited by 3
- 2023Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetrycited by 2
- 2025Incentivizing High-Quality Human Annotations with Golden Questionscited by 0
- 2026Incentive Aware AI Regulations: A Credal Characterisationcited by 0
2 more
- 2026Optimizing Social Utility in Sequential Experimentscited by 0
- 2026Bentkus-type asymptotic e-valuescited by 0
- 2026Bentkus-type asymptotic e-values4 Jun 2026
- 2026Optimizing Social Utility in Sequential Experiments7 May 2026
- 2026Incentive Aware AI Regulations: A Credal Characterisation5 Mar 2026
- 2025Instance-Adaptive Hypothesis Tests with Heterogeneous Agents24 Oct 2025
- 2025Incentivizing High-Quality Human Annotations with Golden Questions25 May 2025
- 2025Poisoning Bayesian Inference via Data Deletion and Replication6 Mar 2025
- 2024Incentivizing Quality Text Generation via Statistical Contracts17 Jun 2024
- 2023Delegating Data Collection in Decentralized Machine Learning4 Sep 2023
- 2023Incentive-Theoretic Bayesian Inference for Collaborative Science7 Jul 2023
- 2023Delegated Classification20 Jun 2023
- 2023Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry24 May 2023
- 2023Online Learning in a Creator Economy19 May 2023
2 more
- 2022The Sample Complexity of Online Contract Designcited by 35
- 2023Delegated Classificationcited by 10
- 2024Incentivizing Quality Text Generation via Statistical Contractscited by 10
- 2023Online Learning in a Creator Economycited by 8
- 2023Delegating Data Collection in Decentralized Machine Learningcited by 8
- 2023Incentive-Theoretic Bayesian Inference for Collaborative Sciencecited by 5
- 2022Solving Constrained Variational Inequalities via a First-order Interior Point-based Methodcited by 3
- 2025Poisoning Bayesian Inference via Data Deletion and Replicationcited by 3
- 2025Instance-Adaptive Hypothesis Tests with Heterogeneous Agentscited by 3
- 2023Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetrycited by 2
- 2026Incentive Aware AI Regulations: A Credal Characterisationcited by 0
- 2026Optimizing Social Utility in Sequential Experimentscited by 0
12 most cited of 13 All 13 (JSON)
- 2026Bentkus-type asymptotic e-values4 Jun 2026
- 2026Optimizing Social Utility in Sequential Experiments7 May 2026
- 2026Incentive Aware AI Regulations: A Credal Characterisation5 Mar 2026
- 2025Instance-Adaptive Hypothesis Tests with Heterogeneous Agents24 Oct 2025
- 2025Poisoning Bayesian Inference via Data Deletion and Replication6 Mar 2025
- 2024Incentivizing Quality Text Generation via Statistical Contracts17 Jun 2024
- 2023Delegating Data Collection in Decentralized Machine Learning4 Sep 2023
- 2023Incentive-Theoretic Bayesian Inference for Collaborative Science7 Jul 2023
- 2023Delegated Classification20 Jun 2023
- 2023Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry24 May 2023
- 2023Online Learning in a Creator Economy19 May 2023
- 2022The Sample Complexity of Online Contract Design10 Nov 2022
12 newest of 13 All 13 (JSON)