This post is very much in accordance with my experience. I've never been able to develop any non-frequentist intuitions about probability, and even simple problems sometimes confuse me until I translate them into explicit frequentist terms. However, once I have the reference classes clearly defined and sketched, I have no difficulty following complex arguments and solving reasonably hard problems in probability. (This includes the numerous supposed paradoxes that disappear as soon as the problem is stated in clear frequentist terms.)
Moreover, I'm still at a loss to understand what meaning the numerical values of probabilities could have except for the frequentist ratios that they imply. I raised the question in a recent discussion here, but I didn't get any satisfactory answers.
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I thought that a major point of heuristics and biases program, at least for economics, was that they were systematic and in a sense "baked-in" as default. If these errors are artifacts of tweaks/wording then that really undermines hope of theoretical extension. The value of this kind of knowledge becomes lopsided towards marketers, magicians, and others trying to manipulate or trick people more effectively.
On the other hand, I think the idea of using the error data as clues as to neural architecture and functioning is great! It seems that neuroscience-clustered research is focused mostly bottom-up and rarely takes inspiration from the other direction.
This raises an interesting point. We can do arithmetic in our heads, some of us more spectacularly than others. Do you mean to say that there is no way to employ/train our brains to do rational thinking more effectively and intuitively?
I had always hoped that we could at least shape our intuition enough to give us a sense for situations where it would be better to calculate - though it's costly and slower. We do not always have our tools (although I guess in the future this is less and less likely).
"Do you mean to say that there is no way to employ/train our brains to do rational thinking more effectively and intuitively?"
I don't don't know whether RickJS meant to say that or not. But this blog post suggests to me a way forward: whenever confronted with questions about likelihood or probability, consciously step back and assess whether a frequentist analysis is possible. Use that approach if it is. If not, shift toward Bayesian views. But in either case, also ask: can I really compute this accurately, or is it too complex? Some things you can do well enough in your head, especially when perfect accuracy isn't necessary (or even possible). Some things you can't.
Maybe if you started kids in their junior year in high school, they might be pretty skilled at telling which was which (of the four possibilities inherent in what I outline above) by the end of their senior year.