2y ago

TIL The Goodhart's Law: Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.

Here is the Wikipedia link.

33 comments

Alternative (and generally easier to understand) formulation: Once a measure becomes a target, it ceases to be a good measure.
See: grades, GDP, workplace metrics...
- Yeah. OP's overly complicated explanation doesn't convey the reason why this happens, which is really kinda just human psychology. People are probably thinking it's like observability in quantum mechanics or some shit.
  Goodhart's Law Example:
  App has poor testing and low quality. New bugs are introduced weekly. Customers complain.
  Management sets a test coverage KPI of 90% for the apps codebase.
  Dev team focuses on tests that hit as many lines of code as possible; NOT on requirements, business logic, or anything that would improve quality, or prevent bugs.
  Quality does not improve because KPI was an arbitrary statistic that holds no value in isolation. Productivity drops because dev team wasted hundreds of hours writing useless tests to achieve KPI. Codebase worse, abd less maintainable. Company wasted millions of dollars. Customers still not happy. Devs hate their lives.
  
  It sounds like this is an example you chose from first hand experience. If so, I'm very sorry. That sounds incredibly frustrating.
  
  I never heard of this before, but I now know the word to describe exercise problems!
  Body builders who are judged by how bulging their muscles are feel like garbage despite supposedly being "peak".
  People with high muscle mass or tall being screwed over by BMI targets.
  People who are told weight indicates health ignore everything else in exchange for lowering calories.
  Even in high school I remember how they would judge you based on like how many push ups you could do... no one who did a ton did proper push ups. Which led to them not helping at all as actual exercise, and even possibly leading to injury.
  Heck, we can even use this for stupid Dog Shows, where because they measure specific things for the "goodness" of the dog, they screw over the dog in every way imaginable that isn't being judged.
  This is a good law to know. I like knowing this law. It's sad how often it's used, but it's good to know.
  
  A big part of it seems to be manipulation of the results? So, like, devs writing tests for more parts of the code base, but ones that are written to always pass.
  
  People are probably thinking it's like observability in quantum mechanics or some shit.
  Lol I really don't think anyone was thinking that
  
  ya, code testing doesn't actually increase code complexity, nor worsen the code base, and tends to actually reduce and avoid bugs
- This is the first line of the linked article btw.
  Goodhart's law is an adage often stated as, "When a measure becomes a target, it ceases to be a good measure".
- So how does a company manage anything if they can't use measurement targets?
  Like software engineering. How do you improve productivity or code quality if setting a target value for a measurement doesn't work?
  
  You don’t make your measures your targets.
  Example:
  “Our customers hate us. We will make our employees get a 10 on their surveys for each customer or we’ll punish them” makes the measure a target.
  “Our customers hate us, so we’re going to change our shitty policies to be more consumer friendly and see how our customers respond” keeps the measure as a measure.
  
  Ok I'm going to answer my own question because I'm too curious to wait lol
  Goodhart’s Law states that “when a measure becomes a target, it ceases to be a good measure.” In other words, when we use a measure to reward performance, we provide an incentive to manipulate the measure in order to receive the reward. This can sometimes result in actions that actually reduce the effectiveness of the measured system while paradoxically improving the measurement of system performance. ... The manipulation of measures resulting from Goodhart’s Law is pervasive because direct measures of effectiveness (MOEs), which are more difficult to manipulate, are also more difficult to measure, and sometimes simply impossible to define and quantify. As a result, analysts must often settle for measures of performance (MOPs) that correlate to the desired effect of the MOE. ... These negative effects can sometimes be avoided. When they cannot, they can be identified, mitigated, and even reversed.
  Use MOEs instead of MOPs whenever practicable and possible
  Use the scientific method to generate new measurement data, rather than harvesting existing and possibly compromised data
  Help customers establish authoritative and difficult-to-manipulate definitions for measures
  Identify and avoid the use of manipulated data and data prone to manipulation
  Use measurement data not generated by the organization being measured
  Collect data secretly or after a measurable activity has already occurred
  Measure all relevant system characteristics rather than just a representative few
  Randomize the measures used over time
  Wargame or red team potential measures
  This report recommends that the organizations that employ analysts should do the following:
  Return to the roots of operational research to focus more on direct measurements in the field
  Answer the questions that should be answered, rather than the questions that can be answered simply because the required data are already available
  Train analysts on MOEs, MOPs, and Goodhart’s Law and how they are interrelated
  Make recognition of Goodhart’s Law part of the internal peer review process and part of all delivered analytical products
  Identify and share mitigation best practices
  [Source]
- Thanks, yes, I saw that one, too, but I liked the emphasis on relationships. The shorter version is easier to get but it does not explain why this happens. E.g., you can observe some relationship (e.g., test results and a student's intelligence) and then you target grades. But then you have an incentive to teach to the test, which breaks down the relationship between test results and intelligence. Other people here gave great examples of relationships that can fail.
  
  Imagine an antivirus program that looks at a piece of code and outputs either "Yes, this is malware" or "No, this is not malware." It is not perfect, but it is pretty good.
  If the malware authors have access to this program, they can test their malware with it. They can keep modifying their malware until it passes the antivirus program.
  Once the antivirus people publish a function AV(code)→boolean, the malware people can use that function to make malware that the function mistakes for non-malware.
  If you publish the exact metric that you promise to use to make a decision, then people who want to control your decision can use that metric to test their methods of manipulating you.
Apart from your mom's weight.
- God dammit almost made me spit out my coffee.
Now that I think of it, it seems to be at the core of some issues with training AI agents using reinforcement learning (e.g., if you choose a wrong metric, you'd get the behavior that makes sense for the agent but not what you want) and with any kind of planned economy (you need targets for planning, but people manipulate them, so you do not get what you want)
- The economic calculation problem is not only Goodhart, but Goodhart certainly doesn't help.

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