Building Less Flawed Metrics: Dodging Goodhart and Campbell's Laws

Metrics are useful for measuring systems and motivating behaviors. Unfortunately, naive application of metrics to a system can distort the system in ways that undermine the original goal. The problem was noted independently by first Campbell, then Goodhart, and in some forms it is not only common, but unavoidable due to the nature of metrics. There are two distinct but interrelated problems that must be overcome in building better metrics; first, specifying metrics more closely related to the true goals, and second, preventing the recipients from gaming the difference between the reward system and the true goal. This paper describes several approaches to designing metrics, beginning with design considerations and processes, then discussing specific strategies including secrecy, randomization, diversification, and post-hoc specification. The discussion will then address important desiderata and the trade-offs involved in each approach, and examples of how they differ, and how the issues can be addressed. Finally, the paper outlines a process for metric design for practitioners who need to design metrics, and as a basis for further elaboration in specific domains