There are a set of strategies for mitigating the problems, and I have a paper on this that is written but still needs to be submitted somewhere, tentatively titled “Building Less Flawed Metrics: Dodging Goodhart and Campbell’s Laws,” if anyone wants to see it they can message/email/tweet at me.
Abstract: 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 Campbell and 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. Finally, it will discuss important desiderata and the trade-offs involved in each approach.
(I should edit this comment to be a link once I have submitted and have a pre-print or publication.)
I didn’t think it likely that business has solved any of these problems, but I just wonder if it might include particular failures not found in other fields that help point towards a solution; or even if it has found one or two pieces of a solution.
Business may at least provide some instructive examples of how things go wrong. Because business involves lots of people & motives & cooperation & conflict, things which I suspect humans are particularly good at thinking & reasoning about (as opposed to more abstract things).
There is a literature on this, and it’s not great for the purposes here—principle-agent setups assume we can formalize the goal as a good metric, and the complexity of management is a fundamentally hard problem that we don’t have good answers for (see my essay on scaling companies here: https://www.ribbonfarm.com/2016/03/17/go-corporate-or-go-home/ ), and goodhart failures due to under-specified goals are fundamentally impossible (see my essay on this here: https://www.ribbonfarm.com/2016/09/29/soft-bias-of-underspecified-goals/ ).
There are a set of strategies for mitigating the problems, and I have a paper on this that is written but still needs to be submitted somewhere, tentatively titled “Building Less Flawed Metrics: Dodging Goodhart and Campbell’s Laws,” if anyone wants to see it they can message/email/tweet at me.
Abstract: 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 Campbell and 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. Finally, it will discuss important desiderata and the trade-offs involved in each approach.
(I should edit this comment to be a link once I have submitted and have a pre-print or publication.)
Thanks, I’ll read those with interest.
I didn’t think it likely that business has solved any of these problems, but I just wonder if it might include particular failures not found in other fields that help point towards a solution; or even if it has found one or two pieces of a solution.
Business may at least provide some instructive examples of how things go wrong. Because business involves lots of people & motives & cooperation & conflict, things which I suspect humans are particularly good at thinking & reasoning about (as opposed to more abstract things).