Another important consideration that is not often mentioned (here and in our forecast) is how much more/less impact you expect to have after a full-out Russia-NATO nuclear war that destroys London.

Asking forecasters about their expertise, or about their thinking patterns is not useful in terms of predicting which individuals will prove consistently accurate. Examining their behaviors, such as belief updating patterns, as well as their psychometric scores related to fluid intelligence offer more promising avenues. Arguably the most impressive performance in our study was for registered intersubjective measures, which rely on comparisons between individual and consensus estimates. Such measures proved valid as predictors of relative accuracy.

From the conclusion of this new paper https://psyarxiv.com/rm49a/

Terri Griffith [thinks](https://econjwatch.org/File+download/1236/UnderappreciatedWorksSept2022.pdf?mimetype=pdf Research Team Design and Management for Centralized R&D is their most neglected paper. They summarize it as follows:

It is a field study of 39 research teams within a global Fortune 100 science/technology company. As we write in the abstract, we demonstrate that “teams containing breadth of both research and business unit experience are more effective in their innovation efforts under two conditions: 1) there must be a knowledge-sharing climate in the team (arguably allowing the team to have access to the knowledge developed through the members’ breadth of experience) and 2) the team leader also has a breadth of research and business experience allowing for the member breadth to be knowledgably managed.” With 13 years perspective, I still find these results valuable and often share them in my innovation management courses.

And the FLI award is probably worth mentioning.

A slightly edited section of my comment on the earlier draft:

I lean skeptical about "relative pair-wise comparisons" after participating: I think people were surprised by their aggregate estimates (e.g., I was very surprised!); I think later convergence was due to common sense and mostly came from people moving points between interventions and not from pair-wise anything;

I think this might be because I am unconfident about eliciting distributions with Squiggle. As I don't have good intuition about how a few log-normals with 80% probability between xx and yy would compare to each other after aggregations (probably this is common, see 2a). After I did my point estimates + my CI via Squiggle for everything alltogether, I think they didn't match each other that well. Maybe that's because lognormal is right-skewed and fairly heavy-tailed?

For sharing updates on Slack I would recommend https://geekbot.com/

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

A few considerations against, tied by generalism enables scale theme:

(1) There are a lot of domains where one can become an expert: it feels infeasible to train and select very capable forecasters in all of them. Being generally thoughtful person/forecaster allows to somewhat successfully go into areas outside your immediate expertise.

Training/selecting experts in a few especially important niches (e.g., AI, biosecurity, and certain topics in geopolitics) seems good and feasible.

(2) But at times of crisis, experts' time is much more valuable than generalist's time. Even now, it's often the case that competent forecasters are quite busy with their main jobs — it's not unlikely that competent forecaster-experts should be doing something different from forecasting.

To add to Eli's comment, I think on such complex topics, it's just common for even personal estimates to fluctuate quite a bit. For example, here is an excerpt from footnote 181 of Carlsmith report:

[...] And my central estimate varies between ~1-10% depending on my mood, what considerations are salient to me at the time, and so forth. This instability is yet another reason not to put too much weight on these numbers.