I strongly agree about the value of breaking down the causes of oneās estimates, and about estimates building on new sources of info being particularly interesting. And I tentatively agree with your other points.
Two things Iād add:
Beard et al. have an interesting passage relevant to the idea of āBreaking down the causes of oneās estimatesā:
Another approach is what Tonn and Stiefel refer to as āEvidential Reasoningā. This involves specifying the effect every piece of evidence has on oneās beliefs about the survival of humanity. Importantly, these probabilities should only reflect the change that this evidence makes, not oneās initial prior beliefs, allowing others to assess them independently. As such, they will only be āimprecise probabilitiesā that describe a small portion of the overall probability space, where the contribution each piece of evidence makes to oneās belief and its complement need not sum to 1. For instance, one might reason that evidence about the adaptability of humans to environmental changes suggests a 30 % probability that we will survive the next 1000 years, but only a 10 % probability that we will not.4 Combination functions can then be used to aggregate these imprecise probabilities to return the overall probability of extinction within this period. This method not only helps assessors determine the probability of extinction, but also provides others with information about the sources of evidence that contributed to this decision and the opportunity to determine how additional information might affect this.
I think an intro post on how to do estimates of this type better could be valuable. I also think it would likely benefit by drawing on the insights in (among other things) the sources I linked to in this sentence: āSome discussion of good techniques for forecasting, which may or may not apply to such long-range and extreme-outcome forecasts, can be found here, here, here, here, and here.ā And Beard et al. is also relevant, though much of what it covers might be hard for individual forecasters to implement with low effort.
I strongly agree about the value of breaking down the causes of oneās estimates, and about estimates building on new sources of info being particularly interesting. And I tentatively agree with your other points.
Two things Iād add:
Beard et al. have an interesting passage relevant to the idea of āBreaking down the causes of oneās estimatesā:
I think an intro post on how to do estimates of this type better could be valuable. I also think it would likely benefit by drawing on the insights in (among other things) the sources I linked to in this sentence: āSome discussion of good techniques for forecasting, which may or may not apply to such long-range and extreme-outcome forecasts, can be found here, here, here, here, and here.ā And Beard et al. is also relevant, though much of what it covers might be hard for individual forecasters to implement with low effort.