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by mitthrowaway2
17 days ago
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Addendum: The closest thing I can think of that matches the GP's description is conditional probabilities. For example, if it rains tonight then I am 70% sure it will rain tomorrow too. I am also 80% sure it will rain tonight. And I am 100% sure that if it doesn't rain tonight, it also will not rain tomorrow. Then I can chain these together: The chance of rain tomorrow is P(Rain tomorrow) = P(Rain tomorrow | rain tonight) * P(Rain tonight) = 0.8*0.7 = 0.56. You can assign conditional probabilites to the correctness of your model itself. Then the phrasing is more like "I am 70% confident that it will rain tomorrow, conditional on my understanding of meteorology being correct" and "I am 80% sure that I understand meteorology correctly". Then you also need to add in a term for P(Rain | not understanding correctly). |
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you may replace "my model is wrong" by e.g " my certainty that no one is using cloud-activation technology".
Now, I'm not native in English and I see how someone (especially with a math background) might find this sentence confusing:
> you are predicting something (e.g rain tomorrow), then if you believe it will rain with probability 0.7 but you are 80% sure of your belief, you won’t say 0.7; you will say something else: 0.8 × 0.7 + 0.2 × something_you_believe = 0.58. Coherence forces you to collapse your uncertainty into your probability at each forecast.
Turns out it's very hard about math non-formally for generalist audience and might not have done the best job about it. It should read
> you are predicting something (e.g rain tomorrow), then your model believe it will rain with probability 0.7 but you are 80% sure of your model, then you won’t report 0.7; you will report : 0.8 × 0.7 + 0.2 × something_you_believe = 0.58. Coherence forces you to collapse your uncertainty into your probability at each forecast.