Metric: Difference between revisions

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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