Goodhart's Law

Overview

Goodhart's Law: when a metric controls behavior, people optimize the metric rather than the underlying goal. Formulated by economist Charles Goodhart (1975) on UK monetary policy; sharpened by Marilyn Strathern (1997): "When a measure becomes a target, it ceases to be a good measure." Four failure mechanisms (Manheim & Garrabrant 2018): Regressional, Extremal, Causal, Adversarial. Countermeasure is always multi-metric + audit + rotation.

Composes with feedback-loops, principal-agent, okr-goal-setting, survivorship-bias.

When to Use

• A KPI is being introduced or its weight is increasing in performance evaluation
• A metric is "improving" without corresponding improvement in the underlying goal
• People are visibly optimizing for a number rather than the work it was meant to track
• Algorithmic optimization is producing outcomes the designers didn't intend
• Resource allocation is driven by a single composite score or ranking
• An AI model, benchmark, or engagement metric is being optimized (or used to justify AI capex / adoption / AI-native competition) and the score is rising faster than real capability or user value

Not when: metric and goal are identical; stakes too low for gaming; metric is purely descriptive with no reward/punishment; question is which metric to use, not whether the measurement-reward system is sound.