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Scoring Strategic Agents

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  • Ian Ball

Abstract

I introduce a model of predictive scoring. A receiver wants to predict a sender's quality. An intermediary observes multiple features of the sender and aggregates them into a score. Based on the score, the receiver makes a decision. The sender wants the most favorable decision, and she can distort each feature at a privately known cost. I characterize the most accurate scoring rule. This rule underweights some features to deter sender distortion, and overweights other features so that the score is correct on average. The receiver prefers this scoring rule to full disclosure because information aggregation mitigates his commitment problem.

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  • Ian Ball, 2019. "Scoring Strategic Agents," Papers 1909.01888, arXiv.org, revised Oct 2023.
    • Handle: RePEc:arx:papers:1909.01888
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    References listed on IDEAS

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    Cited by:

    1. Wouter Dessein & Alex Frankel & Navin Kartik, 2023. "Test-Optional Admissions," Papers 2304.07551, arXiv.org, revised Nov 2023.
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    3. Christopher A. Hennessy & Charles A. E. Goodhart, 2023. "Goodhart'S Law And Machine Learning: A Structural Perspective," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 1075-1086, August.
    4. Yasui, Yuta, 2021. "Controlling Fake Reviews," MPRA Paper 108177, University Library of Munich, Germany.

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