Strategic attribute learning - Jean-Michel Benkert; Ludmila Matyskova; Egor Starkov (University of Bern, Department of Economics, 2024)
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.
Keywords: D72; D81; D83; 330; Attributes; Information acquisition; Gaussian distribution; Strategic learning