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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The end result might be a Bayesian updating process, but the internal machinery is very different.
Human brains don’t seem particularly well suited to calculating priors and percentages but they seem particularly well suited to generating explanatory narratives.
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Mathematically inclined people seem to enjoy talking about Bayes’ Theorem, and they seem to be able to explicitly calculate priors/percentages.
But I wonder if it’s the only way to get there. It seems to go against the grain of the mind (eww maths; yay stories).
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For an entertaining example of this, check out ’s story of investing in a pachinko company.
Totally Bayesian in thinking style; totally not Bayes’ Theorem in execution.
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I think the most important part of Bayesian thinking is the idea of priors.
Without priors, you are effectively left with: changing your opinions based on new information.
My feeling is that a visual demonstration like this is best way to teach someone
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This is absolutely something I do, albeit not consistently. I’m not sure if I consider priors but I tend to hold beliefs loosely and proceed anyways until further evidence appears.
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I am a professional investor and we use Bayesian updating all the time but without the math. We create pre-mortems on the investment working out and not working out. Then we create a catalyst path. We review the investment thesis as we hit various points in that path
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Oh, this is wonderful to hear! I can totally see how that is intuitive and doable.