Information Elicitation in Agency Games
CoRR(2024)
摘要
Rapid progress in scalable, commoditized tools for data collection and data
processing has made it possible for firms and policymakers to employ ever more
complex metrics as guides for decision-making. These developments have
highlighted a prevailing challenge – deciding *which* metrics to compute. In
particular, a firm's ability to compute a wider range of existing metrics does
not address the problem of *unknown unknowns*, which reflects informational
limitations on the part of the firm. To guide the choice of metrics in the face
of this informational problem, we turn to the evaluated agents themselves, who
may have more information than a principal about how to measure outcomes
effectively. We model this interaction as a simple agency game, where we ask:
*When does an agent have an incentive to reveal the observability of a
cost-correlated variable to the principal?* There are two effects: better
information reduces the agent's information rents but also makes some projects
go forward that otherwise would fail. We show that the agent prefers to reveal
information that exposes a strong enough differentiation between high and low
costs. Expanding the agent's action space to include the ability to *garble*
their information, we show that the agent often prefers to garble over full
revelation. Still, giving the agent the ability to garble can lead to higher
total welfare. Our model has analogies with price discrimination, and we
leverage some of these synergies to analyze total welfare.
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