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：数据采集与处理工具的可扩展化与商品化快速推进，使得企业及政策制定者能够将日益复杂的指标作为决策指南。这些发展凸显出一项关键挑战——即决定*应该计算哪些指标*。具体而言，企业计算更多现有指标的能力并未解决"未知的未知"问题，这反映了企业自身的信息局限性。为应对这一信息问题以指导指标选择，我们转向被评估的代理人——他们可能比委托人更了解如何有效衡量结果。我们将这一互动建模为简单的委托博弈，并提出问题：*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？* 其中存在两种效应：更充分的信息既能减少代理人的信息租金，也可能使原本会失败的某些项目得以推进。研究发现，当代理人揭示的信息能充分区分高成本与低成本时，其更倾向于披露信息。进一步扩展代理人的行动空间（允许其*扭曲*信息）后，我们发现代理人往往更偏好扭曲信息而非完全披露。尽管如此，赋予代理人扭曲信息的能力反而可能提升总福利。本模型与价格歧视存在类比关系，我们借助这种协同效应分析总福利。