Strategic Attribute Learning

TL;DR

This paper develops a strategic model of attribute learning where a researcher allocates testing resources to influence decision-makers, considering disagreement and organizational dynamics, revealing optimal strategies and societal implications.

Contribution

It introduces a novel equilibrium framework for strategic attribute learning, accounting for disagreement and hierarchical effects, with applications to organizational and political contexts.

Findings

Researchers avoid learning when expecting excessive responses.

Managers prefer diverse analysts as hierarchical distance increases.

Opposed advisors can effectively constrain discriminatory politicians.

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.