David Bernardās post Uncertainty over time and Bayesian updating is super relevant to understand the extent to which effects decay over time. The main results are below. I think David underestimated how much effects decay due to assuming a āConstant variance priorā. Without any information, I think my actions can have a much greater effect in 10 years that in 10 M years. So I would assume the variance of the prior decreases over time, in which case the signal would be more heavily discounted than in Davidās analysis.
Years until posterior expected value is x% of signal
Hi Richard,
David Bernardās post Uncertainty over time and Bayesian updating is super relevant to understand the extent to which effects decay over time. The main results are below. I think David underestimated how much effects decay due to assuming a āConstant variance priorā. Without any information, I think my actions can have a much greater effect in 10 years that in 10 M years. So I would assume the variance of the prior decreases over time, in which case the signal would be more heavily discounted than in Davidās analysis.
Years until posterior expected value is x% of signal