Comment by 317070
9 hours ago
It's because the models have different purposes in decision making, in ways which decision makers don't often appreciate.
Models are still great at helping us understand the world and are in many ways the best thing we have. The problem is that today we are overly relying on them to make real policy interventions on an ecology (e.g. what is a "healthy" amount of animals to cull or fish) based on an ecosystem, which is just a poor model.
If we could get rid of this idea of "stability" and "equilibrium" in economics and ecology, I would be a happy man.
> Ironically, the experts, the ones building the "wrong" models, tend to be the ones most aware of this
Exactly why I'm here :-)
Decision making is risk management, and is often simpler than making predictions. You need to think in terms of utility functions and you need to stay far away from the catastrophic regions. You often don't need super accurate predictions for that.
One example is pandemics. The thing is pandemics are extremely fat tailed in terms of fatalities. You need a lot of data to fit any outbreak model and no one has time for that. But the reaction to diseases out breaks is very simple. Is it super deadly and contagious? If yes, shut everything down! This is essentially what Asian countries like Vietnam and Taiwan did during Covid and they handled it way better than the West. I remember arguing about outbreak models, which is a completely useless activity.
Maybe the best way to illustrate our point is to use weather models, as it's pretty tangible for regular people.
Weather models are ridiculously advanced running on super computers crunching a massive global network of real time data. But they still aren't anywhere close to perfect.
That being said, just because it rains on your birthday when it was said to be sunny, doesn't mean you delete your weather app, call meteorology pseudoscience, and start a substack of "the forecast was wrong again" blog posts.
People intuitively grasp this foolishness because they constantly interact with weather models. But for things they have almost no contact with, it's easy to write it off on a single "bad forecast"
(I'll also admit the caveat that not every model is as good (or bad) as weather models, another dimension at play to throw a wrench in peoples gears)
The weather is easy to measure though, which leads to a continuing improvement of the models. In a lot of fields it’s very difficult to make experiments and measure the results. In those fields models can stay very bad for a long time.
I love this analogy... The Heresy of Meteorology! Everything You Thought You Knew about Weather is Wrong. How Weather Reports are Sapping the Joy From Your Life. The Truth about Weather! The Misguided Science of Predicting Mother Nature.
It sounds funny until you meet people who actually hold those beliefs.
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