Goodhart's law is a social-science adage describing how a measure loses its reliability as soon as it is turned into a steering objective. It owes its name to British economist Charles Goodhart, who formulated its core in a 1975 article devoted to monetary policy in the United Kingdom: "any observed statistical regularity tends to collapse once pressure is placed upon it for control purposes". The intuition arose from analysis of British monetary-management difficulties: the stable correlations exploited by central banks as policy levers ceased to hold once instrumentalized.

The most widespread formulation, however, is not Goodhart's but that of anthropologist Marilyn Strathern, who in 1997, in a text on accountability in the university system, proposed the generalized and memorable version: "when a measure becomes a target, it ceases to be a good measure". This reformulation emphasizes the loss of diagnostic value a metric suffers when individuals optimize toward the measure itself rather than toward the underlying objective it is meant to represent.

The idea belongs to a constellation of related principles. Campbell's law (Donald T. Campbell, 1976) addresses the corruption of quantitative social indicators used for decision-making. The Lucas critique (1976) offers its macroeconomic equivalent: the effects of a policy cannot be predicted from historical relationships, because agents adapt to them. To these are added the cobra effect (an incentive that inadvertently rewards counterproductive behavior) and the McNamara fallacy (dismissing the qualitative because it escapes quantification). Several authors have enriched the corpus: Jerome Ravetz (1971), Keith Hoskin (1996), and Jon Danielsson for financial risk modeling.

Illustrations span numerous fields: in healthcare, making length of stay a target causes premature discharges and readmissions; in research, the h-index erodes as a measure of reputation as it becomes an evaluation criterion; in conservation, IUCN extinction classifications have been tightened after being used to lift protections; in education, No Child Left Behind encouraged grade advancement without mastery; during the pandemic, UK COVID testing targets conflated capacity with diagnostic usefulness. The principle ultimately reflects how rational actors optimize within measured systems — a legacy of accountability practices born in the 19th century. Today, it directly illuminates reward hacking and the fragility of optimization metrics in AI systems.