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.

翻译：规模化、商品化的数据采集与处理工具快速发展，使得企业及政策制定者能够采用日益复杂的指标作为决策依据。这一进展凸显了一个持续存在的挑战——确定"计算哪些指标"。特别是，企业计算更广泛现有指标的能力并未解决"未知的未知"问题，这反映了企业在信息方面的局限性。为应对这一信息困境以指导指标选择，我们转而依靠被评估的代理人——他们可能比委托人更了解如何有效衡量结果。我们将这种互动建模为简单的委托代理博弈，提出核心问题："代理人何时有动机向委托人揭示成本相关变量的可观测性？"这存在两种效应：更充分的信息既会减少代理人的信息租金，也会使某些原本会失败的项目得以推进。研究表明，当揭示的信息能充分区分高成本与低成本时，代理人倾向于揭示信息。通过将代理人的行动空间扩展至包含"混淆"信息的能力，我们发现代理人通常更倾向于完全揭示信息。尽管如此，赋予代理人混淆信息的能力反而可能提升总体福利。该模型与价格歧视具有类比性，我们利用这些协同效应分析总体福利。