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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Potentially great ways forecasting can improve the longterm future, published by Linch on March 14, 2022 on The Effective Altruism Forum. Summary See companion post here. In addition to the EA Early Warning Forecasting Center I outlined in my other post, I think there are several ways forecasting may be very useful for longtermism, including: Forecasting as a way to amplify EA research Prediction-evaluation setups as a way to improve EA grantmaking Large-scale broad forecasting as an EA outreach intervention Large-scale forecasting tournaments as a talent training and vetting pipeline The dream: high-quality, calibrated, long-range forecasting (ideally also at scale and on-demand) Finally, an entirely different theory-of-change for forecasting is to consider broad, mass-appeal forecasting as a general epistemics intervention, that is, improving the thinking and reasoning quality of society at large. I think this is potentially pretty interesting and promising, and probably net positive, but am generally uncertain of the sign. This is mostly because I worry about squandering the epistemic edge that current broadly altruistic and EA(-adjacent) actors have over the rest of the world. Further work, from myself and others, would consider and prioritize both among this list and within specific organizational choices in this list (including further research, starter projects or organizations to initiate, grants that are worth making, etc). Further work should also include a red-teaming of this vision, and other refinements. Most of the points in this post will not seem original to people extremely acquainted with the EA forecasting space. Nonetheless, I thought it may be helpful to bring much of it in one place, as well as include my current opinionated best guesses and judgements. Forecasting as a way to amplify EA research There are a number of questions that come up during research for which I think forecasting could be one useful tool. Structural properties of such questions may include them being more amenable to broadly outside-view-style reasoning as opposed to deep internal models, being relatively “clean”, having a semi-objective resolution criteria, being more evaluative than generative (e.g., not questions that require coming up with new policy ideas), being about the future, etc. Broadly, ways to use forecasting to improve EA research can be decomposed into two categories: The researcher personally comes up with forecasting questions and forecasts questions that are relevant to her own work. As much as possible, the researcher identifies forecastable questions in her own work and then delegates answering such questions to external forecasters, or forecasting aggregation processes. It’s also plausible to me that some of the work in identifying and operationalizing the relevant forecasting questions can be delegatable as well. There are a number of reasons for why researchers may prefer delegating subsets of their work to external forecasters or forecasting aggregation processes. The two most important to me are: a) for some subset of questions, top forecasters may be much better at getting the correct predictions to them than current EA researchers, and b) speaking loosely, forecaster time is usually less valuable than EA evaluation time (at least / especially after accounting for the fact that it’s probably easier to use money to buy extra forecaster time than to buy extra EA evaluation time). I’m excited about more work in this general direction because it’s moderately impactful, highly tractable, and very easy to do initial experiments with, compared to other ideas on this list. Some researchers at Rethink Priorities have been experimenting with a number of different ways to use forecasting to improve our research. For example, we partnered with Metaculus to h...