That's an interesting response, thanks! I think where I disagree is that I think people are pretty smart, at least in one thing, which is survival -- the proof of that is that those who were not, quickly exited the gene pool. That is a powerful filtering that tunes our estimators.
I agree with your last paragraph, but I think it supports my point. I wear a mask because it has zero cost, and it may save my life. When I took this decision, I didn't estimate any probabilities and I haven't used Bayes theorem. Understanding what a mask does exactly and how aerosol transmission of viruses work precisely is almost irrelevant to my decision -- I could be improving my knowledge ("my priors") by studying virology, but there would still be so many uncertainties, that it would hardly influence my decision.
I agree with you, but my point is more broadly that in reality we often don't go through the steps "1. estimate probability" -> "2. make a decision based on the probability distribution", because step 1. is so error-prone and intractable, that we typically jump directly to step 2. and try to limit our downside.
Of course you could look back and say, given the fact that I took some decision, what would have been my prior if I had used Bayes theorem, but my point is that we don't actually use it for taking the decision.
There is always a prior only if you really care about computing probabilities. The implicit assumption in Bayesian data analysis is that you go first to "best possible estimate of probability", then to "decision based on that". My point was that you usually need not do the first step.
Example: I wear a bicycle helmet because it costs me next nothing and it possibly saves my life. I don't do any Bayesian analysis implicitly or explicitly, because on one side there is an outcome with value minus infinity, so it hardly matters what probability I multiply it with.
Controversial opinion: Bayes Theorem is overrated. In real life usually we have no idea about priors, and we have close to zero chance to get any good estimate of the true probability of something. But we can still get by fine for the most part, by focusing on limiting possible loss and staying on the safe side with large margins.
Many of the claimed cognitive biases go away under this view. One textbook example of Bayes theorem is how doctors overestimate the probability of being positive for a disease. But what are the priors? Maybe those who visit the doctor did something risky the day before or are feeling funny. Maybe the cost of false positive is negligible compared to the cost of a false negative, etc. People are less stupid than what the TED talk crowd claims.
That integrity of the West during and before the cold war included some nasty moves as well (Vietnam to name one). In fact, I find your thesis jingoism at its best.
I agree that in Berlin it is not as _major_ problem as in some other large cities, but still, it is telling that it is widely accepted as normal that all building corners that face the sidewalk are constantly pissed by dogs with the coloring of the walls clearly visible everywhere. As for poop, 90% of the times people pick it up, the rest can still ruin your day if you step into it.
Yet sidewalks in cities around most of the developed world drown in dog/cat poop and pee, something that would have seemed strange a generation ago, and still seems if you travel from a less pet-crazy place.
You can encode as moderately large (low hundreds of clauses/variables) SAT instances questions of Ramsey-theory and other unsolved combinatorics and those instances will not be solved by any heuristic.
I agree with your last paragraph, but I think it supports my point. I wear a mask because it has zero cost, and it may save my life. When I took this decision, I didn't estimate any probabilities and I haven't used Bayes theorem. Understanding what a mask does exactly and how aerosol transmission of viruses work precisely is almost irrelevant to my decision -- I could be improving my knowledge ("my priors") by studying virology, but there would still be so many uncertainties, that it would hardly influence my decision.