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arXiv · 2412.10024

Strategic Attribute Learning

Jean-Michel Benkert·Ludmila Matyskova·Egor Starkov
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Abstract

A researcher allocates a budget of informative tests across multiple unknown attributes to influence a decision-maker. We derive the researcher's equilibrium learning strategy by solving an auxiliary single-player problem. The attribute weights in this problem depend on how much the researcher and the decision-maker disagree. If the researcher expects an excessive response to new information, she forgoes learning altogether. In an organizational context, we show that a manager favors more diverse analysts as the hierarchical distance grows. In another application, we show how an appropriately opposed advisor can constrain a discriminatory politician, and identify the welfare-inequality Pareto frontier of researchers.

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Jean-Michel Benkert, Ludmila Matyskova, Egor Starkov. 2024-12-13. Strategic Attribute Learning. https://arxiv.org/abs/2412.10024

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