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An excellent page of resources on [[Goodhart’s law]] to be found [https://modelthinkers.com/mental-model/goodharts-law here].
An excellent page of resources on [[Goodhart’s law]] to be found [https://modelthinkers.com/mental-model/goodharts-law here].


Regressive
*'''Regressive''': using a single metric as a proxy to measure a phenomenon that is actually [[multivariate]] — caused by several factors. Here [[Simpson ’s paradox]] is not your friend.


Choosing a measurement that is a single proxy to your goal, when the goal actually has several causal factors behind it.
*'''Extremal''': Where a the metric is a useful indicator under normal circumstances — [[Mediocristan]], but breaks down in extremes or unusual cases — [[Extremistan]]. Paging Messrs [[Black Scholes option pricing model|Black and Scholes]]. You know, using [[normal distribution]]s of independent events to model dependent events, like human behaviour of the market.


Knowing that successful basketballers tend to be tall, you might build a basketball team by recruiting tall people. However, they might not possess other factors such as coordination, fitness, quick responses etc..
*'''Causal''': the old [[correlation]] is not the same as [[causation]] chestnut.


Extremal
*'''Adversarial''': Substitute targeting the desired outcome — a senior tranche in a portfolio of mortgages that will not default in any circumstances — with one that is rated AAA. Hello, [[global financial crisis]].


Where a proxy might indicate the goal under normal circumstances, but not in variable contexts.


Sugar consumption helped define our ancient ancestors’ survival however today, with a different context, our excessive sugar consumption is often an indicator of an unhealthy diet. 
Causal
Confusing Causation vs Correlation when selecting measurements.
Data might indicate increasing ice cream sales when people wear sunglasses. However, providing more sunglasses won’t necessarily boost ice cream sales because they are both caused by a third factor, ie. sunny, hot days.
Adversarial (Cobra Effect)
Choosing a proxy measurement incentivises people to make the proxy the new goal.
{{Outsourcing}}
{{Outsourcing}}
*[[Beware of shorthand]]
*[[Beware of shorthand]]
*[[Multivariate]] factors

Revision as of 09:16, 2 May 2023

JC sounds off on Management™
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When everything about a people is for the time growing weak and ineffective, it begins to talk about efficiency. ... Vigorous organisms talk not about their processes, but their aims.

G. K. Chesterton, Heretics

“When a measure becomes a target, it ceases to be a good measure.”

Goodhart’s Law

The stock-in-trade of a middle manager and the management consultant she aspires to become.

Like a key performance indicator, a second-order derivative of actual performance calculated to allow non-experts to make cavalier management decisions, usually to reduce expenditure on — aka make redundant — the person performing that function.

To be contrasted with the ineffable, inarticulable skills that are provided by a subject matter expert.

Goodhart’s law

An excellent page of resources on Goodhart’s law to be found here.

  • Regressive: using a single metric as a proxy to measure a phenomenon that is actually multivariate — caused by several factors. Here Simpson ’s paradox is not your friend.
  • Extremal: Where a the metric is a useful indicator under normal circumstances — Mediocristan, but breaks down in extremes or unusual cases — Extremistan. Paging Messrs Black and Scholes. You know, using normal distributions of independent events to model dependent events, like human behaviour of the market.
  • Adversarial: Substitute targeting the desired outcome — a senior tranche in a portfolio of mortgages that will not default in any circumstances — with one that is rated AAA. Hello, global financial crisis.


See also