I recently gave a talk at the 3rd Oxford Workshop on Global Priorities Research

This covered a few topics:

1. How we can think about and use Expected Loss to understand and optimize judgemental forecasting setups.
2. Some thoughts around how (1) applies to our decisions about long-term forecasting.
3. A few slides on Foretold and how similar systems could be used to help with (1) and (2).

I recently recorded a quick version of this talk on YouTube. As always, I'd be curious to get feedback on these ideas.

Original Talk Abstract

The last several decades have brought a large number of contributions to statistical and judgemental forecasting endeavors. The next several decades may bring significantly more.

As things get more advanced, not only will our understanding of how to predict distant events improve, but our understanding of the accuracy of these forecasts will improve as well. This should help us to resolve questions regarding when and how much to trust these forecasts.

This talk will present an overview of what we can expect if things go well and how thinking about this can inform our current actions when optimizing for the long term future.

Related Links:

Foretold

Expected Loss Graphs

Blog posts on "Amplifying generalist research via forecasting":

https://www.lesswrong.com/posts/cLtdcxu9E4noRSons/part-1-amplifying-generalist-research-via-forecasting-models

https://www.lesswrong.com/posts/FeE9nR7RPZrLtsYzD/part-2-amplifying-generalist-research-via-forecasting