Metrics are useful for measuring systems and motivating behaviors in academia as well as in public policy, medicine, business, and other systems. Unfortunately, naive application of metrics to a system can distort the system and even undermine the original goal. There are two interrelated problems to overcome in building better metrics in academia and elsewhere. The first, specifying evaluable metrics that correspond to the goals, is well recognized but still often ignored. The second, minimizing perverse effects that undermine the metric or that enable people to game the rewards, is less recognized but is critical. This perspective discusses designing metrics, beginning with design considerations and processes; the presentation of specific strategies for mitigating perverse impacts, including secrecy, randomization, diversification, and post hoc specification; and continuing with important desiderata and tradeoffs involved with examples of how they can complement each other or differ. Finally, this perspective presents a comprehensive process integrating these ideas.
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