Goodhart’s law is an adage named after economist Charles Goodhart, which has been phrased by Marilyn Strathern as: “When a measure becomes a target, it ceases to be a good measure.” One way in which this can occur is individuals trying to anticipate the effect of a policy and then taking actions which alter its outcome.
Goodhart’s Law reflects part of human nature, there is no perfect rules, so there is always people using the defect of rules,. I think there are two advantages to understand Goodhart’s law for a data scientist. First, Goodhart’s law tells that data is not everything, a data scientist may be able to find and analysis data, but the difficult is the people behind data, if the data we collect and analysis is to help making rules, we must be aware of this, and be cautious so as to try to avoid unnecessary deviation; Second, as a data scientist, it is his/her duty to try to find the pattern behind data, that means a data scientist may find the abnormal pattern which may caused by Goodhart’s law and avoid further influence.
古德哈特定律（Goodhart’s law），是以 Charles Goodhart的名字命名的，这是一个非常有名的定理：当一个政策变成目标，它将不再是一个好的政策。作为前英格兰银行的建议者，提出：当政府试图管理这些金融财产的特别标识时，它们便不再是可信的经济风向标。