I’m not sure but my guess of the argument of the OP is that:
Let’s say you are an unbiased forecaster. You get information as time passes. When you start with a 60% prediction that event X will happen, on average, the evidence you will receive will cause you to correctly revise your prediction towards 100%.
Scott Alexander noted curiosity about this behaviour; Eliezer Yudkowsky has confidently asserted it is an indicator of sub-par Bayesian updating.
I’m not sure but my guess of the argument of the OP is that:
Let’s say you are an unbiased forecaster. You get information as time passes. When you start with a 60% prediction that event X will happen, on average, the evidence you will receive will cause you to correctly revise your prediction towards 100%.
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