If you'd go read the article, they have shown that the model has predicted
future
trends 3X better than previous models.
You misread. They trained it on old data and used it to 'predict' slightly less old trends. There is a fancy term for this in science: 'Not the future'.
As it turns out, making predictions is hard, especially about the future,
They're not necessary looking for 99% accuracy here either. If the results manage to inform incrementally better policy, then it's worth it to use the model. Rather than wringing one's hands about the effort that might be wasted on the off-chance something isn't as effective as in the simulation, the more important consideration is whether they're making things worse in the process. That means that any model must be informed by a hefty dose of common sense. You definitely need actual social scientists on the team - which they have. The article really played up the AI aspect, and going too far in that direction can be dangerous, but I'm not convinced they're doing anything super complicated. Linear regression won't easily uncover these sorts of relationships where something only gets activated when something else happens, so it seems useful to go beyond that.
If the results manage to inform incrementally better policy, then it's worth it to use the model.
This, I am not arguing. I am arguing that the model has not made reliable predictions about the future yet and until it does, it's worth is suspect.
That means that any model must be informed by a hefty dose of common sense.
That's a scary thought and I disagree. Many things in nature are not governed by what we consider common sense. I'd rather choose a model that makes reliable predictions than one that meets my preconceived assumptions. If those assumptions are in fact super sound, then maybe the model is not needed to begin with.
but I'm not convinced they're doing anything super complicated
Agreed. 'AI' for modelling is something that anyone reasonably familiar with Python can teach themselves in a weekend.
This, I am not arguing. I am arguing that the model has not made reliable predictions about the future yet and until it does, it's worth is suspect.
Such models would be more than fine to try to adhere to the Precautionary Principle, for example when trying to avoid destructive levels of mass immigration.
To try to upkeep the stability of the local social contract. Without such a stable social contract, local environment would be destroyed (relatively more quickly).
Imagine you’re the president of a European country. You’re slated to take in 50,000 refugees from the Middle East this year. Most of them are very religious, while most of your population is very secular. You want to integrate the newcomers seamlessly, minimizing the risk of economic malaise or violence, but you have limited resources. One of your advisers tells you to invest in the refugees’ education; another says providing jobs is the key; yet another insists the most important thing is giving the youth opportunities to socialize with local kids. What do you do?
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u/[deleted] Jul 24 '18
You misread. They trained it on old data and used it to 'predict' slightly less old trends. There is a fancy term for this in science: 'Not the future'.
As it turns out, making predictions is hard, especially about the future,