Thought experiment: If a bicycle cannot fly, is it a defective bicycle?
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Keep in mind the models had a range from 100K at the low end, with mitigation, to over 1 million without. If the actual death toll comes in at 140K with mitigation, would you say the models were wrong or right? I think you would say they were damned good.
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But if we miss the bottom estimate by exactly the same amount on the low side, and the final tally is 60K dead, you call that a gross miss. Yet it would be a miss by exactly as much as the 140K, only in the other direction.
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I contend there is an illusion here that is skewing opinions. If you compare the revised 60K estimate to 100K, it looks like a big difference. But if you consider the range of uncertainty was 900K, depending on mitigation, then 60K and 100K are in the same ballpark.
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End of conversation
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Cuz, math is hard.
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Yes, they should be fired. If I build a model that billions of people will use to make trillions of $ in decisions, and I say the range is X-Y, it damn sure need to be between X-Y. If not I don't know what I am doing. If I don't know what I am doing, firing seems appropriate.
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The initial lower bounds were way above that.
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My brain is just not big enough to get it
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60k isn't far from 100k. Landing outside a massive range makes the model look weak. There's no way around that. What's driving complaints though, is: Pretending mitigation wasn't factored in. Suddenly saying the US was plenty early/great at it. Overrun hospitals were a certainty.
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No model is complete with showing the inherent Plus and Minus in the Probabilities of each of the model assumptions. Average modelers usually believe each of their assumed variables are deterministic and they know the exact value to apply. But, the variance is always explainable
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The initial U.K. model (that we’ve dumped) had 2M deaths. We’ve only recently changed to a new model and then lowered that one too
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