We used the geometric mean of the samples with the minimum and maximum removed to better deal with extreme outliers, as described in our previous post

I don’t see how that’s consistent with:

What is the probability that Russia will use a nuclear weapon in Ukraine in the next MONTH?

Aggregate probability: 0.0859 (8.6%)

All probabilities: 0.27, 0.04, 0.02, 0.001, 0.09, 0.08, 0.07

What is the probability that Russia will use a nuclear weapon in Ukraine in the next YEAR?

Aggregate probability: 0.2294 (23%)

All probabilities: 0.38, 0.11, 0.11, 0.005, 0.42, 0.2, 0.11

I get that the first of those should be 0.053. Haven’t run the numbers on the latter, but pretty sure the geometric mean should be smaller than 23% from eyeballing it. (I also haven’t run the numbers on other aggregated numbers in this post.)

Now fixed. The exercise also make me realize that these numbers are very sensitive to individual forecasters’ estimates, so we are looking to add more forecasters to our aggregate.

I don’t see how that’s consistent with:

I get that the first of those should be 0.053. Haven’t run the numbers on the latter, but pretty sure the geometric mean should be smaller than 23% from eyeballing it. (I also haven’t run the numbers on other aggregated numbers in this post.)

Geometric mean of the odds.

Aaand, you are right, I was missing the last estimate. Will change, though probably after the next hour.

Now fixed. The exercise also make me realize that these numbers are very sensitive to individual forecasters’ estimates, so we are looking to add more forecasters to our aggregate.