Do you have any thoughts on the best way to aggregate forecasts of quantities which are not between 0 and 1 (e.g. global number of deaths during 2030)?
Depends on whether you are aggregating distributions or point estimates.
If you are aggregating distributions, I would follow the same procedure outlined in this post, and use the continuous version of the geometric mean of odds I outline in footnote 1 of this post.
If you are aggregating point estimates, at this point I would use the procedure explained in this paper, which is taking a sort of extremized average. I would consider a log transform depending on the quantity you are aggregating. (though note that I have not spent as much time thinking about how to aggregate point estimates)
I am aggregating arrays of Monte Carlo samples which have N samples each. There is a sense in which each sample is one point estimate, but for large N (I am using 10^7) I guess I can fit a distribution to each of the arrays.
Without more context, I’d say that fit a distribution to each array and then aggregate them using a weighted linear aggregate of the resulting CDFs, assigning a weight proportional to your confidence on the assumptions that produced the array.
Hi Jaime,
Do you have any thoughts on the best way to aggregate forecasts of quantities which are not between 0 and 1 (e.g. global number of deaths during 2030)?
Depends on whether you are aggregating distributions or point estimates.
If you are aggregating distributions, I would follow the same procedure outlined in this post, and use the continuous version of the geometric mean of odds I outline in footnote 1 of this post.
If you are aggregating point estimates, at this point I would use the procedure explained in this paper, which is taking a sort of extremized average. I would consider a log transform depending on the quantity you are aggregating. (though note that I have not spent as much time thinking about how to aggregate point estimates)
Thanks!
I am aggregating arrays of Monte Carlo samples which have N samples each. There is a sense in which each sample is one point estimate, but for large N (I am using 10^7) I guess I can fit a distribution to each of the arrays.
Without more context, I’d say that fit a distribution to each array and then aggregate them using a weighted linear aggregate of the resulting CDFs, assigning a weight proportional to your confidence on the assumptions that produced the array.
Thank you. Feel free to check this for more context.