FWIW, different communities treat it differently. It's a no-go to ask for upvotes at https://hckrnews.com/ but is highly encouraged at https://producthunt.com/.
So it's fair to say that FFI-supers were selected and evaluated on the same data? This seems concerning. Specifically, on which questions the top-60 were selected, and on which questions the below scores were calculated? Did these sets of questions overlap?
The standardised Brier scores of FFI superforecasters (–0.36) were almost perfectly similar to that of the initial forecasts of superforecasters in GJP (–0.37).  Moreover, even though regular forecasters in the FFI tournament were worse at prediction than GJP forecasters overall (probably due to not updating, training or grouping), the relative accuracy of FFI's superforecasters compared to regular forecasters (-0.06), and to defence researchers with access to classified information (–0.1) was strikingly similar.
More as food for thought... but maybe "broad investor base" is a bit of exaggeration? Index funds are likely to control a significant fraction of these corporations, and it's unclear if the board members they appoint would represent ordinary people. Especially when owning ETF != owning actual underlying stocks.
From an old comment of mine:
Due to the rise of index funds (they "own" > 1/5 of American public companies), it seems that an alternative strategy might be trying to rise in the ranks of firms like BlackRock, Vanguard, or SSGA. It's not unprecedented for them to take action (partly for selfish reasons); here are examples of BlackRock taking stances on environmental sustainability and coronavirus cure/vaccine.
Here is a paper exploring the potential implications of the rise of index funds and their stewardship: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3282794
The table here got all messed up. Could it be fixed?
Thanks for highlighting Beadle (2022), I will add it to our review!
I wonder how FFI Superforecasters were selected? It's important to first select forecasters who are doing good and then evaluate their performance on new questions to avoid the issue of "training and testing on the same data."
If you think there is a 50% chance that your credences will say go from 10% to 30%+. Then you believe that with a 50% probability, you live in a "30%+ world." But then you live in at least a 50% * 30%+ = 15%+ world rather than a 10% world, as you originally thought.