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Strategic Attribute Learning

Author

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  • Jean-Michel Benkert
  • Ludmila Matyskova
  • Egor Starkov

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.

Suggested Citation

  • Jean-Michel Benkert & Ludmila Matyskova & Egor Starkov, 2024. "Strategic Attribute Learning," Papers 2412.10024, arXiv.org.
    • Handle: RePEc:arx:papers:2412.10024
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    References listed on IDEAS

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