Goodhart’s Law And Why It Matters In Business

Goodhart’s Law is named after British monetary policy theorist and economist Charles Goodhart. Speaking at a conference in Sydney in 1975, Goodhart said that “any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.” Goodhart’s Law states that when a measure becomes a target, it ceases to be a good measure.

Concept OverviewGoodhart’s Law is a fundamental concept in economics and social sciences that highlights a potential pitfall when using metrics or indicators as a basis for decision-making. The law states: “When a measure becomes a target, it ceases to be a good measure.” In other words, when people or organizations set specific metrics or indicators as targets to achieve, they may change their behavior in ways that undermine the original purpose of the measure. Goodhart’s Law was formulated by economist Charles Goodhart and has implications in various fields, including finance, economics, business, and public policy.
Key Principle– The key principle of Goodhart’s Law is that using a metric as a target can lead to unintended consequences. When individuals or organizations are incentivized to achieve a particular metric, they may focus solely on optimizing that metric, even if it comes at the expense of other important factors or the original goal. This can result in distorted behavior, misaligned incentives, and outcomes that are counterproductive or contrary to the intended purpose.
Examples– Examples of Goodhart’s Law in action include: 1. Financial Markets: When stock prices or earnings per share (EPS) become the primary focus for a company, management may engage in financial engineering or short-term strategies to boost these metrics, potentially at the expense of long-term growth and sustainability. 2. Education: In education systems where standardized test scores are used as a primary metric for evaluating schools or teachers, educators may “teach to the test” rather than providing a well-rounded education. 3. Healthcare: In healthcare, if hospital performance is measured based on patient satisfaction scores, healthcare providers may prioritize patient comfort over necessary medical procedures or treatments. 4. Corporate Performance Metrics: When organizations set specific quarterly revenue targets as key performance indicators (KPIs), sales teams might push for aggressive sales tactics, discounting, or revenue recognition manipulation to meet the targets.
Applications– Understanding Goodhart’s Law has applications in various fields: 1. Business Metrics: Business leaders should be cautious about relying solely on a single metric as a performance indicator and consider potential unintended consequences. 2. Public Policy: Policymakers need to design policies and regulations that account for the possibility of metrics being manipulated or distorted. 3. Financial Markets: Investors and analysts should consider how companies may be influenced by financial metrics in their investment decisions. 4. Education and Healthcare: Administrators and policymakers should use a balanced set of metrics to evaluate performance rather than relying solely on one measure.
Mitigation Strategies– To mitigate the negative effects of Goodhart’s Law, individuals and organizations can consider the following strategies: 1. Diversify Metrics: Use a combination of metrics rather than relying on a single measure to assess performance. 2. Scrutinize Incentives: Carefully examine the incentives created by performance metrics to ensure they align with the intended goals. 3. Regularly Review Metrics: Continuously assess whether the chosen metrics are still relevant and meaningful for evaluating performance. 4. Encourage Transparency: Foster a culture of transparency and open communication to reduce the likelihood of gaming the metrics. 5. Monitor for Unintended Consequences: Actively monitor for any unintended consequences or distortions resulting from the use of metrics as targets.
Impact and Relevance– Goodhart’s Law is significant because it underscores the complexity of human behavior and decision-making in various contexts. It serves as a reminder that setting targets based on metrics requires careful consideration and monitoring to avoid perverse incentives and unintended outcomes. The concept has broad implications in finance, economics, education, healthcare, and public policy, and it continues to be a critical element in decision-making and performance evaluation worldwide.

Understanding Goodhart’s Law

Goodhart would later admit that his quip was intended to be a humorous, throw-away comment. But it was nevertheless an accurate and perceptive observation about how the modern world functions.

It’s important to note that Goodhart himself had no role in naming the law for which he is named. That distinction goes to anthropologist Marilyn Strathern, who argued in a 1997 paper that the law had uses beyond statistics to evaluation in a broader sense.

An oft-told story of Goodhart’s Law at work can be described by the cobra effect. In India under British colonial rule, the government was troubled by the number of venomous cobras. To reduce their population, the government placed a bounty on every cobra the locals could catch. This strategy worked for a while, but some individuals began breeding the cobras only to kill them later and collect a higher bounty. 

Eventually, the colonial government caught on and scrapped the scheme, causing many of the bred cobras to be released into the wild. The key takeaway of the cobra effect story is that incentives designed to solve a problem end up rewarding people for making the problem worse.

The four forms of Goodhart’s Law

There are generally accepted to be four variations on Goodhart’s Law:

  1. Regressive Goodhart – here, the measures individuals use for their target (goal) are imperfectly correlated with that goal. For example, weight is imperfectly correlated with health because it encourages skipping meals or weighing oneself in the morning with an empty stomach.
  2. Extremal Goodhart – this occurs when a measurement is picked because it correlates with a goal in normal situations. In extreme circumstances however, the measure is erroneous. The human relationship with sugar is a classic example. While sugar was correlated with survival thousands of years ago, the same cannot be said of modern, sedentary lifestyles where sugar promotes obesity.
  3. Causal Goodhart – where the behavior of an individual does not directly affect the goal but has some causal effect on the measure. The number of times a gym membership is renewed does not directly impact how often an individual exercises, for example.
  4. Adversarial Goodhart – where other goals confound the goal a measure is trying to accomplish, such as the cobra effect mentioned above.

Avoiding the impact of Goodhart’s Law

Of the four variations of Goodhart’s Law, only the Regressive Goodhart is unavoidable.

For the remaining three, here are some simple avoidance tips:

  • Conduct regular checks to ensure the measure is still incentivizing in line with the desired outcome or goal.
  • Become aware of Goodhart’s Law and how it operates.
  • Maintain a focus on the end goal while using the measures as a guide only.
  • Reduce bureaucracy and formalism.
  • Use a combination of diversified metrics. A balanced scorecard can be useful here.

Examples and Case Studies

  • Academic Performance: In education, the use of standardized test scores as the primary measure of academic performance can lead to unintended consequences. Schools and teachers may be incentivized to focus solely on improving test scores, leading to teaching to the test and neglecting other important aspects of learning and development.
  • Business Metrics: In business, focusing solely on financial metrics like revenue or profit can lead to adverse effects. For example, if a sales team is rewarded solely based on revenue, they may engage in aggressive sales tactics or offer steep discounts to close deals, even if it negatively impacts long-term profitability.
  • Healthcare: In healthcare, the use of certain performance metrics to assess the quality of care can create unintended outcomes. For instance, a hospital that prioritizes reducing patient wait times may discharge patients prematurely or prioritize less critical cases to improve wait time metrics, potentially compromising patient outcomes.
  • Employee Performance: In the workplace, setting individual performance targets without considering the broader context can lead to unintended consequences. Employees may focus solely on meeting their targets, neglecting collaboration and teamwork, or engaging in unethical behavior to achieve their goals.
  • Government Policies: When governments set specific targets to measure the success of policies, such as reducing unemployment rates or increasing GDP growth, there is a risk of distorting efforts and focusing on short-term gains rather than addressing long-term challenges.

Key takeaways:

  • Goodhart’s Law states that when a measure becomes a target, it ceases to be a good measure.
  • Goodhart’s Law was informally coined during a speech by Charles Goodhart. Although the economist was speaking in the context of statistics, the law has broader evaluative applications.
  • Goodhart’s Law is generally categorized into four variations: Regressive Goodhart, Extremal Goodhart, Causal Goodhart, and Adversarial Goodhart.

Key Highlights

  • Goodhart’s Law: Named after economist Charles Goodhart, the law states that when a measure becomes a target, it loses its effectiveness as a measure.
  • Understanding the Law: Despite Goodhart’s original comment being meant humorously, it accurately describes how measures can be manipulated when used as targets.
  • Origin of the Term: Anthropologist Marilyn Strathern is credited with naming the law and extending its relevance beyond statistics to broader evaluation contexts.
  • The Cobra Effect: An example illustrating Goodhart’s Law is the cobra effect, where government incentives to reduce cobra populations backfired when people started breeding them for the rewards.
  • Four Variations of the Law:
    • Regressive Goodhart: Measures used for goals have imperfect correlation with those goals.
    • Extremal Goodhart: Measures that correlate with goals in normal situations fail under extreme circumstances.
    • Causal Goodhart: An individual’s behavior indirectly affects the measure but not the actual goal.
    • Adversarial Goodhart: Conflicting goals interfere with the desired goal of the measure.
  • Avoiding Goodhart’s Law:
    • Regularly reassess measures to ensure they align with the intended outcomes.
    • Understand and acknowledge the operation of Goodhart’s Law.
    • Keep the focus on the end goal while using measures as guides.
    • Minimize bureaucracy and formality.
    • Use a combination of diverse metrics, such as a balanced scorecard.
  • Examples and Case Studies:
    • In education, focusing solely on standardized test scores can lead to neglect of broader learning aspects.
    • In business, prioritizing financial metrics can lead to short-term gains at the expense of long-term profitability.
    • In healthcare, emphasizing certain metrics can compromise patient care quality.
    • In the workplace, setting individual performance targets can undermine collaboration and ethical behavior.
    • In government policies, setting specific targets may lead to neglect of long-term challenges.
  • Key Takeaways:
    • Goodhart’s Law highlights the problem of using a measure as a target.
    • Named after Charles Goodhart, the law has broader applications beyond statistics.
    • The four variations of the law explain different ways the law manifests in various contexts.

Connected Thinking Frameworks

Convergent vs. Divergent Thinking

Convergent thinking occurs when the solution to a problem can be found by applying established rules and logical reasoning. Whereas divergent thinking is an unstructured problem-solving method where participants are encouraged to develop many innovative ideas or solutions to a given problem. Where convergent thinking might work for larger, mature organizations where divergent thinking is more suited for startups and innovative companies.

Critical Thinking

Critical thinking involves analyzing observations, facts, evidence, and arguments to form a judgment about what someone reads, hears, says, or writes.


The concept of cognitive biases was introduced and popularized by the work of Amos Tversky and Daniel Kahneman in 1972. Biases are seen as systematic errors and flaws that make humans deviate from the standards of rationality, thus making us inept at making good decisions under uncertainty.

Second-Order Thinking

Second-order thinking is a means of assessing the implications of our decisions by considering future consequences. Second-order thinking is a mental model that considers all future possibilities. It encourages individuals to think outside of the box so that they can prepare for every and eventuality. It also discourages the tendency for individuals to default to the most obvious choice.

Lateral Thinking

Lateral thinking is a business strategy that involves approaching a problem from a different direction. The strategy attempts to remove traditionally formulaic and routine approaches to problem-solving by advocating creative thinking, therefore finding unconventional ways to solve a known problem. This sort of non-linear approach to problem-solving, can at times, create a big impact.

Bounded Rationality

Bounded rationality is a concept attributed to Herbert Simon, an economist and political scientist interested in decision-making and how we make decisions in the real world. In fact, he believed that rather than optimizing (which was the mainstream view in the past decades) humans follow what he called satisficing.

Dunning-Kruger Effect

The Dunning-Kruger effect describes a cognitive bias where people with low ability in a task overestimate their ability to perform that task well. Consumers or businesses that do not possess the requisite knowledge make bad decisions. What’s more, knowledge gaps prevent the person or business from seeing their mistakes.

Occam’s Razor

Occam’s Razor states that one should not increase (beyond reason) the number of entities required to explain anything. All things being equal, the simplest solution is often the best one. The principle is attributed to 14th-century English theologian William of Ockham.

Lindy Effect

The Lindy Effect is a theory about the ageing of non-perishable things, like technology or ideas. Popularized by author Nicholas Nassim Taleb, the Lindy Effect states that non-perishable things like technology age – linearly – in reverse. Therefore, the older an idea or a technology, the same will be its life expectancy.


Antifragility was first coined as a term by author, and options trader Nassim Nicholas Taleb. Antifragility is a characteristic of systems that thrive as a result of stressors, volatility, and randomness. Therefore, Antifragile is the opposite of fragile. Where a fragile thing breaks up to volatility; a robust thing resists volatility. An antifragile thing gets stronger from volatility (provided the level of stressors and randomness doesn’t pass a certain threshold).


Ergodicity is one of the most important concepts in statistics. Ergodicity is a mathematical concept suggesting that a point of a moving system will eventually visit all parts of the space the system moves in. On the opposite side, non-ergodic means that a system doesn’t visit all the possible parts, as there are absorbing barriers

Systems Thinking

Systems thinking is a holistic means of investigating the factors and interactions that could contribute to a potential outcome. It is about thinking non-linearly, and understanding the second-order consequences of actions and input into the system.

Vertical Thinking

Vertical thinking, on the other hand, is a problem-solving approach that favors a selective, analytical, structured, and sequential mindset. The focus of vertical thinking is to arrive at a reasoned, defined solution.

Metaphorical Thinking

Metaphorical thinking describes a mental process in which comparisons are made between qualities of objects usually considered to be separate classifications.  Metaphorical thinking is a mental process connecting two different universes of meaning and is the result of the mind looking for similarities.

Maslow’s Hammer

Maslow’s Hammer, otherwise known as the law of the instrument or the Einstellung effect, is a cognitive bias causing an over-reliance on a familiar tool. This can be expressed as the tendency to overuse a known tool (perhaps a hammer) to solve issues that might require a different tool. This problem is persistent in the business world where perhaps known tools or frameworks might be used in the wrong context (like business plans used as planning tools instead of only investors’ pitches).

Peter Principle

The Peter Principle was first described by Canadian sociologist Lawrence J. Peter in his 1969 book The Peter Principle. The Peter Principle states that people are continually promoted within an organization until they reach their level of incompetence.

Straw Man Fallacy

The straw man fallacy describes an argument that misrepresents an opponent’s stance to make rebuttal more convenient. The straw man fallacy is a type of informal logical fallacy, defined as a flaw in the structure of an argument that renders it invalid.

Google Effect

The Google effect is a tendency for individuals to forget information that is readily available through search engines. During the Google effect – sometimes called digital amnesia – individuals have an excessive reliance on digital information as a form of memory recall.

Streisand Effect

The Streisand Effect is a paradoxical phenomenon where the act of suppressing information to reduce visibility causes it to become more visible. In 2003, Streisand attempted to suppress aerial photographs of her Californian home by suing photographer Kenneth Adelman for an invasion of privacy. Adelman, who Streisand assumed was paparazzi, was instead taking photographs to document and study coastal erosion. In her quest for more privacy, Streisand’s efforts had the opposite effect.

Compromise Effect

Single-attribute choices – such as choosing the apartment with the lowest rent – are relatively simple. However, most of the decisions consumers make are based on multiple attributes which complicate the decision-making process. The compromise effect states that a consumer is more likely to choose the middle option of a set of products over more extreme options.

Butterfly Effect

In business, the butterfly effect describes the phenomenon where the simplest actions yield the largest rewards. The butterfly effect was coined by meteorologist Edward Lorenz in 1960 and as a result, it is most often associated with weather in pop culture. Lorenz noted that the small action of a butterfly fluttering its wings had the potential to cause progressively larger actions resulting in a typhoon.

IKEA Effect

The IKEA effect is a cognitive bias that describes consumers’ tendency to value something more if they have made it themselves. That is why brands often use the IKEA effect to have customizations for final products, as they help the consumer relate to it more and therefore appending to it more value.

Ringelmann Effect 

Ringelmann Effect
The Ringelmann effect describes the tendency for individuals within a group to become less productive as the group size increases.

The Overview Effect

The overview effect is a cognitive shift reported by some astronauts when they look back at the Earth from space. The shift occurs because of the impressive visual spectacle of the Earth and tends to be characterized by a state of awe and increased self-transcendence.

House Money Effect

The house money effect was first described by researchers Richard Thaler and Eric Johnson in a 1990 study entitled Gambling with the House Money and Trying to Break Even: The Effects of Prior Outcomes on Risky Choice. The house money effect is a cognitive bias where investors take higher risks on reinvested capital than they would on an initial investment.


As highlighted by German psychologist Gerd Gigerenzer in the paper “Heuristic Decision Making,” the term heuristic is of Greek origin, meaning “serving to find out or discover.” More precisely, a heuristic is a fast and accurate way to make decisions in the real world, which is driven by uncertainty.

Recognition Heuristic

The recognition heuristic is a psychological model of judgment and decision making. It is part of a suite of simple and economical heuristics proposed by psychologists Daniel Goldstein and Gerd Gigerenzer. The recognition heuristic argues that inferences are made about an object based on whether it is recognized or not.

Representativeness Heuristic

The representativeness heuristic was first described by psychologists Daniel Kahneman and Amos Tversky. The representativeness heuristic judges the probability of an event according to the degree to which that event resembles a broader class. When queried, most will choose the first option because the description of John matches the stereotype we may hold for an archaeologist.

Take-The-Best Heuristic

The take-the-best heuristic is a decision-making shortcut that helps an individual choose between several alternatives. The take-the-best (TTB) heuristic decides between two or more alternatives based on a single good attribute, otherwise known as a cue. In the process, less desirable attributes are ignored.

Bundling Bias

The bundling bias is a cognitive bias in e-commerce where a consumer tends not to use all of the products bought as a group, or bundle. Bundling occurs when individual products or services are sold together as a bundle. Common examples are tickets and experiences. The bundling bias dictates that consumers are less likely to use each item in the bundle. This means that the value of the bundle and indeed the value of each item in the bundle is decreased.

Barnum Effect

The Barnum Effect is a cognitive bias where individuals believe that generic information – which applies to most people – is specifically tailored for themselves.

Anchoring Effect

The anchoring effect describes the human tendency to rely on an initial piece of information (the “anchor”) to make subsequent judgments or decisions. Price anchoring, then, is the process of establishing a price point that customers can reference when making a buying decision.

Decoy Effect

The decoy effect is a psychological phenomenon where inferior – or decoy – options influence consumer preferences. Businesses use the decoy effect to nudge potential customers toward the desired target product. The decoy effect is staged by placing a competitor product and a decoy product, which is primarily used to nudge the customer toward the target product.

Commitment Bias

Commitment bias describes the tendency of an individual to remain committed to past behaviors – even if they result in undesirable outcomes. The bias is particularly pronounced when such behaviors are performed publicly. Commitment bias is also known as escalation of commitment.

First-Principles Thinking

First-principles thinking – sometimes called reasoning from first principles – is used to reverse-engineer complex problems and encourage creativity. It involves breaking down problems into basic elements and reassembling them from the ground up. Elon Musk is among the strongest proponents of this way of thinking.

Ladder Of Inference

The ladder of inference is a conscious or subconscious thinking process where an individual moves from a fact to a decision or action. The ladder of inference was created by academic Chris Argyris to illustrate how people form and then use mental models to make decisions.

Goodhart’s Law

Goodhart’s Law is named after British monetary policy theorist and economist Charles Goodhart. Speaking at a conference in Sydney in 1975, Goodhart said that “any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.” Goodhart’s Law states that when a measure becomes a target, it ceases to be a good measure.

Six Thinking Hats Model

The Six Thinking Hats model was created by psychologist Edward de Bono in 1986, who noted that personality type was a key driver of how people approached problem-solving. For example, optimists view situations differently from pessimists. Analytical individuals may generate ideas that a more emotional person would not, and vice versa.

Mandela Effect

The Mandela effect is a phenomenon where a large group of people remembers an event differently from how it occurred. The Mandela effect was first described in relation to Fiona Broome, who believed that former South African President Nelson Mandela died in prison during the 1980s. While Mandela was released from prison in 1990 and died 23 years later, Broome remembered news coverage of his death in prison and even a speech from his widow. Of course, neither event occurred in reality. But Broome was later to discover that she was not the only one with the same recollection of events.

Crowding-Out Effect

The crowding-out effect occurs when public sector spending reduces spending in the private sector.

Bandwagon Effect

The bandwagon effect tells us that the more a belief or idea has been adopted by more people within a group, the more the individual adoption of that idea might increase within the same group. This is the psychological effect that leads to herd mentality. What in marketing can be associated with social proof.

Moore’s Law

Moore’s law states that the number of transistors on a microchip doubles approximately every two years. This observation was made by Intel co-founder Gordon Moore in 1965 and it become a guiding principle for the semiconductor industry and has had far-reaching implications for technology as a whole.

Disruptive Innovation

Disruptive innovation as a term was first described by Clayton M. Christensen, an American academic and business consultant whom The Economist called “the most influential management thinker of his time.” Disruptive innovation describes the process by which a product or service takes hold at the bottom of a market and eventually displaces established competitors, products, firms, or alliances.

Value Migration

Value migration was first described by author Adrian Slywotzky in his 1996 book Value Migration – How to Think Several Moves Ahead of the Competition. Value migration is the transferal of value-creating forces from outdated business models to something better able to satisfy consumer demands.

Bye-Now Effect

The bye-now effect describes the tendency for consumers to think of the word “buy” when they read the word “bye”. In a study that tracked diners at a name-your-own-price restaurant, each diner was asked to read one of two phrases before ordering their meal. The first phrase, “so long”, resulted in diners paying an average of $32 per meal. But when diners recited the phrase “bye bye” before ordering, the average price per meal rose to $45.


Groupthink occurs when well-intentioned individuals make non-optimal or irrational decisions based on a belief that dissent is impossible or on a motivation to conform. Groupthink occurs when members of a group reach a consensus without critical reasoning or evaluation of the alternatives and their consequences.


A stereotype is a fixed and over-generalized belief about a particular group or class of people. These beliefs are based on the false assumption that certain characteristics are common to every individual residing in that group. Many stereotypes have a long and sometimes controversial history and are a direct consequence of various political, social, or economic events. Stereotyping is the process of making assumptions about a person or group of people based on various attributes, including gender, race, religion, or physical traits.

Murphy’s Law

Murphy’s Law states that if anything can go wrong, it will go wrong. Murphy’s Law was named after aerospace engineer Edward A. Murphy. During his time working at Edwards Air Force Base in 1949, Murphy cursed a technician who had improperly wired an electrical component and said, “If there is any way to do it wrong, he’ll find it.”

Law of Unintended Consequences

The law of unintended consequences was first mentioned by British philosopher John Locke when writing to parliament about the unintended effects of interest rate rises. However, it was popularized in 1936 by American sociologist Robert K. Merton who looked at unexpected, unanticipated, and unintended consequences and their impact on society.

Fundamental Attribution Error

Fundamental attribution error is a bias people display when judging the behavior of others. The tendency is to over-emphasize personal characteristics and under-emphasize environmental and situational factors.

Outcome Bias

Outcome bias describes a tendency to evaluate a decision based on its outcome and not on the process by which the decision was reached. In other words, the quality of a decision is only determined once the outcome is known. Outcome bias occurs when a decision is based on the outcome of previous events without regard for how those events developed.

Hindsight Bias

Hindsight bias is the tendency for people to perceive past events as more predictable than they actually were. The result of a presidential election, for example, seems more obvious when the winner is announced. The same can also be said for the avid sports fan who predicted the correct outcome of a match regardless of whether their team won or lost. Hindsight bias, therefore, is the tendency for an individual to convince themselves that they accurately predicted an event before it happened.

Read Next: BiasesBounded RationalityMandela EffectDunning-Kruger EffectLindy EffectCrowding Out EffectBandwagon Effect.

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