GOODHART'S LAW AND MACHINE LEARNING: A STRUCTURAL PERSPECTIVE
成果类型：
Article
署名作者：
Hennessy, Christopher A.; Goodhart, Charles A. E.
署名单位：
University of London; London Business School; University of London; London School Economics & Political Science; University of London; London Business School
刊物名称：
INTERNATIONAL ECONOMIC REVIEW
ISSN/ISSBN：
0020-6598
DOI：
10.1111/iere.12633
发表日期：
2023
页码：
1075-1086
关键词：
摘要：
We develop a simple structural model to illustrate how penalized regressions generate Goodhart bias when training data are clean but covariates are manipulated at known cost by future agents. With quadratic (extremely steep) manipulation costs, bias is proportional to Ridge (Lasso) penalization. If costs depend on absolute or percentage manipulation, the following algorithm yields manipulation-proof prediction: Within training data, evaluate candidate coefficients at their respective incentive-compatible manipulation configuration. We derive analytical coefficient adjustments: slopes (intercept) shift downward if costs depend on percentage (absolute) manipulation. Statisticians ignoring manipulation costs select socially suboptimal penalization. Model averaging reduces these manipulation costs.
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