Something that I’m chewing on that I haven’t quite pinned down (or properly developed a view on): Bayesian thinking may well be the most effective way to think when faced with uncertainty, but Bayes’s Theorem may be the wrong way to teach it.
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This tweet brought to you by the observation that some of the most intuitive Bayesian thinkers I know don’t explicitly update using percentages.
Instead they seem to do something different. Many of them seem to generate multiple explanatory stories instead and hold them loosely.
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I'm generally very explicit, but I don't understand how you can practically use Bayesian thinking in an explicit way.
Do people really try to assign percentages to events and literally update their priors?
Building stories and intuiting which is correct feels way more natural.
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Well, the superforecasters all seem to do it that way!