The map is not the territory. In the process of measurement the deaths due to a pesticide you need a complex model about causality. That model means you have an abstraction.
If you get your drug unblinded by giving it strong side effects it will perform better against placebo. It’s a way to Goodhart the gold standard in our way to establish the causality of whether a drug helps a patient.
Any model of the causality of deaths due to your pesticide will be subject to Goodharting.
You do it to the extend that you have a causal model in your head that links the two. If you take the issue of toxic pesticides, new pesticides got used and a lot of our bees died. Whether or not there’s a correlation is subject to public debate. That’s how real-world examples look like.
The map is not the territory. In the process of measurement the deaths due to a pesticide you need a complex model about causality. That model means you have an abstraction.
If you get your drug unblinded by giving it strong side effects it will perform better against placebo. It’s a way to Goodhart the gold standard in our way to establish the causality of whether a drug helps a patient.
Any model of the causality of deaths due to your pesticide will be subject to Goodharting.
Why? Doesn’t goodharting require optimization?
Suppose a) the world was nuked, and b) everyone died. Would you call A the cause of B?
You do it to the extend that you have a causal model in your head that links the two. If you take the issue of toxic pesticides, new pesticides got used and a lot of our bees died. Whether or not there’s a correlation is subject to public debate. That’s how real-world examples look like.
Suppose a) the world was nuked, and b) everyone died. Would you call A the cause of B?
Editing posts afterwards to remove statements is not a great way to have rational debate.
It wasn’t removed, it was moved.
Suppose we jettisoned causality. What exactly do you think can, and cannot, be measured?