Truthful Reporting of Competence with Minimal Verification
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917275485863936 |
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| author | Meir, Reshef Wagner, Jonathan Ben-Porat, Omer |
| author_facet | Meir, Reshef Wagner, Jonathan Ben-Porat, Omer |
| contents | Suppose you run a home exam, where students should report their own scores but can cheat freely. You can, if needed, call a limited number of students to class and verify their actual performance against their reported score. We consider the class of mechanisms where truthful reporting is a dominant strategy, and truthful agents are never penalized -- even off-equilibrium.
How many students do we need to verify, in expectation, if we want to minimize the bias, i.e., the difference between agents' competence and their expected grade? When perfect verification is available, we characterize the best possible tradeoff between these requirements and provide a simple parametrized mechanism that is optimal in the class for any distribution of agents' types. When verification is noisy, the task becomes much more challenging. We show how proper scoring rules can be leveraged in different ways to construct truthful mechanisms with a good (though not necessarily optimal) tradeoff. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_14076 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Truthful Reporting of Competence with Minimal Verification Meir, Reshef Wagner, Jonathan Ben-Porat, Omer Computer Science and Game Theory Multiagent Systems Suppose you run a home exam, where students should report their own scores but can cheat freely. You can, if needed, call a limited number of students to class and verify their actual performance against their reported score. We consider the class of mechanisms where truthful reporting is a dominant strategy, and truthful agents are never penalized -- even off-equilibrium. How many students do we need to verify, in expectation, if we want to minimize the bias, i.e., the difference between agents' competence and their expected grade? When perfect verification is available, we characterize the best possible tradeoff between these requirements and provide a simple parametrized mechanism that is optimal in the class for any distribution of agents' types. When verification is noisy, the task becomes much more challenging. We show how proper scoring rules can be leveraged in different ways to construct truthful mechanisms with a good (though not necessarily optimal) tradeoff. |
| title | Truthful Reporting of Competence with Minimal Verification |
| topic | Computer Science and Game Theory Multiagent Systems |
| url | https://arxiv.org/abs/2602.14076 |