All views are my own rather than those of any organizations/groups that I’m affiliated with. Trying to share my current views relatively bluntly. Note that I am often cynical about things I’m involved in. Thanks to Adam Binks for feedback. 

Edit: See also child comment for clarifications/updates. 

Edit 2:  I think the grantmaking program has different scope than I was expecting; see this comment by Benjamin for more.

Following some of the skeptical comments here, I figured it might be useful to quickly write up some personal takes on forecasting’s promise and what subareas I’m most excited about (where “forecasting” is defined as things I would expect to be in the scope of OpenPhil’s program to fund).

1. Overall, most forecasting grants that OP has made seem much lower EV than the AI safety grants (I’m not counting grants that seem more AI-y than forecasting-y, e.g. Epoch, and I believe these wouldn’t be covered by the new grantmaking program). Due to my ASI timelines (10th percentile ~2027, median ~late 2030s), I’m most excited about forecasting grants that are closely related to AI, though I’m not super confident that no non-AI related ones are above the bar.
2. I generally agree with the view that I’ve heard repeated a few times that EAs significantly overrate forecasting as a cause area, while the rest of the world significantly underrates it.
   1. I think EAs often overrate superforecasters’ opinions, they’re not magic. A lot of superforecasters aren’t great (at general reasoning, but even at geopolitical forecasting), there’s plenty of variation in quality.
      1. General quality: Becoming a superforecaster selects for some level of intelligence, open-mindedness, and intuitive forecasting sense among the small group of people who actually make 100 forecasts on GJOpen. There are tons of people (e.g. I’d guess very roughly 30-60% of AI safety full-time employees?) who would become superforecasters if they bothered to put in the time.
         1. Some background: as I’ve written previously I’m intuitively skeptical of the benefits of large amounts of forecasting practice (i.e. would guess strong diminishing returns).
      2. Specialties / domain expertise: Contra a caricturized “superforecasters are the best at any forecasting questions” view, consider a grantmaker deciding whether to fund an organization. They are, whether explicitly or implicitly, forecasting a distribution of outcomes for the grant. But I’d guess most would agree that superforecasters would do significantly worse than grantmakers at this “forecasting question”. A similar argument could be made for many intellectual jobs, which could be framed as forecasting. The question on whether superforecasters are relatively better isn’t “Is this task answering a forecasting question“ but rather “What are the specific attributes of this forecasting question”.
         1. Some people seem to think that the key difference between questions superforecasters are good at vs. smart domain experts are in questions that are *resolvable* or *short-term*. I tend to think that the main differences are along the axes of *domain-specificity* and *complexity*, though these are of course correlated with the other axes. Superforecasters are selected for being relatively good at short-term, often geopolitical questions.
         2. As I’ve written previously: It varies based on the question/domain how much domain expertise matters, but ultimately I expect reasonable domain experts to make better forecasts than reasonable generalists in many domains.
            1. There’s an extreme here where e.g. forecasting what the best chess move is obviously better done by chess experts rather than superforecasters.
            2. So if we think of a spectrum from geopolitics to chess, it’s very unclear to me where things like long-term AI forecasts land.
            3. This intuition seems to be consistent with the lack of quality existing evidence described in Arb’s report (which debunked the “superforecasters beat intelligence experts without classified information” claim!).
   2. Similarly, I’m skeptical of the straw rationalist view that highly liquid well-run prediction markets would be an insane societal boon, rather than a more moderate-large one (hard to operationalize, hope you get the vibe). See here for related takes. This might change with superhuman AI forecasters though, whose “time” might be more plentiful.
3. Historically, OP-funded forecasting platforms (Metaculus, INFER) seem to be underwhelming on publicly observable impact per dollar (in terms of usefulness for important decision-makers, user activity, rationale quality, etc.). Maybe some private influence over decision-makers makes up for it, but I’m pretty skeptical.
   1. Tbh, it’s not clear that these and other platforms currently provide more value to the world than the opportunity cost of the people who spend time on them. e.g. I was somewhat addicted to Metaculus then later Manifold for a bit and spent more time on these than I would reflectively endorse (though it’s plausible that they were mostly replacing something worse like social media). I resonate with some of the comments on the EA Forum post mentioning that it’s a very nerd-sniping activity; forecasting to move up a leaderboard (esp. w/quick-resolving questions) is quite addicting to me compared to normal work activities.
4. I’ve heard arguments that getting superforecasted probabilities on things is good because they’re more legible/credible because they’re “backed by science”. I don’t have an airtight argument against this, but it feels slimy to me due to my beliefs above about superforecaster quality.
5. Regarding whether forecasting orgs should try to make money, I’m in favor of pushing in that direction as a signal of actually providing value, though it’s of course a balance re: the incentives there and will depend on the org strategy.
6. The types of forecasting grants I’d feel most excited about atm are, roughly ordered, and without a claim that any are above OpenPhil’s GCR bar (and definitely not exhaustive, and biased toward things I’ve thought about recently):
   1. Making AI products for forecasting/epistemics in the vein of FutureSearch and Elicit. I’m also interested in more lightweight forecasting/epistemic assistants.
      1. FutureSearch and systems in recent papers are already pretty good at forecasting, and I expect substantial improvements soon with next-gen models.
      2. I’m excited about making AIs push toward what's true rather than what sounds right at first glance or is pushed by powerful actors.
      3. However, even if we have good forecasting/epistemics AIs, I’m worried that it won’t convince people of the truth since people are irrational and often variance in their beliefs is explained by gaining status/power, vibes, social circles, etc. It seems especially hard to change people’s minds on very tribal things, which seem correlated with the most important beliefs to change.
         1. AI friends might actually be more important than AI forecasters for epistemics, but that doesn’t mean AI forecasters are useless.
      4. I might think/write more about this soon. See also Lukas's Epistemics Project Ideas and ACX on AI for forecasting
   2. Judgmental forecasting of AI threat models, risks, etc. involving a mix of people who have AI / dangerous domain expertise and/or very strong forecasting track record (>90th percentile superforecaster), ideally as many people as possible who have both. Not sure how helpful it will be but it seems maybe worth more people trying.
      1. In particular, forecasting that can help inform risk assessment / RSPs seems like a great thing to try. See also discussion of the Delphi technique in the context of AGI risk assessment here. Malcolm Murray at GovAI is running a Delphi study to get estimates of likelihood and impact of various AI risks from experts.
      2. This is related to a broader class of interventions that might look somewhat like a “structured review process” in which one would take an in-depth threat modeling report and have various people review and contribute their own forecasts in addition to qualitative feedback. My sense is that when superforecasters reviewed Joe Carlsmith’s p(doom) forecast in a similar vein that the result wasn’t that useful, but the exercise could plausibly be more useful with better quality reviews/forecasts. It’s unclear whether this would be a good use of resources above the usual ad-hoc/non-forecasting review process, but might be worth trying more.
   3. Forecasting tournaments on AI questions with large prize pools: I think these historically have been meh (e.g. the Metaculus one attracted few forecasters, wasn’t fun to forecast on (for me at least), and I’d guess significantly improved ~no important decisions), but I think it’s plausible things could go better now as AIs are much more capable, there are many more interesting and maybe important things to predict, etc.
   4. Crafting forecasting questions that are cruxy on threat models / intervention prioritization between folks working on AI safety
      1. It’s kind of wild that there has been so little success on this front. See frustrations from Alex Turner “I think it's not a coincidence that many of the "canonical alignment ideas" somehow don't make any testable predictions until AI takeoff has begun.” I worry that this will take a bunch of effort and not get very far (see Paul/Eliezer finding only a somewhat related bet re: their takeoff speeds disagreement), but it seems worth giving a more thorough shot with different participants.
      2. I’m relatively excited about doing things within the AI safety group rather than between this group and others (e.g. superforecasters) because I expect the results might be more actionable for AI safety people.

I incorporated some snippets of a reflections section from a previous forecasting retrospective above, but there’s a little that I didn’t include if you’re inclined to check it out.

I think forecasting is attractive to many people in EA like myself because EA skews towards curious people from STEM backgrounds who like games. However, I’m yet to see a robust case for it being an effective use of charitable funds (if there is, please point me to it). I’m worried we are not being objective enough and trying to find the facts that support the conclusion rather than the other way round.

I’m glad to see the debate on decision relevance in the comments! I think that if we end up considering forecasting a successful focus area in 5-10 years, thinking hard about the value-add to decision-making will likely have played a crucial role in this success.
 
As for my own view, I do agree that judgmental / subjective probability forecasting hasn’t been as much of a success story as one might have expected about 10 years ago. I also agree that many of the stories people tell about the impact of forecasting  naturally raise questions like “so why isn’t this a huge industry now? Why is this project a non-profit?”. We are likely to ask questions of this kind to prospective grantees way more often than grantmakers in other focus areas. 
However, I (unsurprisingly) also disagree with the stronger claim that the lack of a large judgmental forecasting industry is conclusive evidence that forecasting doesn’t provide value, and is just an EA hobby horse. While I don’t have capacity to engage in this debate deeply, a few points of rebuttal:

• I do think there have been some successes. For instance, the XPT mentioned in this comment certainly affected the personal beliefs of some people in the EA community, and thereby had an influence on resource allocation and career decisions. 
• Forecasting, as such, is a large industry. I’d assign considerable weight to the idea that making judgmental forecasting a success of the kind that model-driven forecasting approaches have been in areas like finance, marketing or sports, is a harder, but solvable task. There might simply be a free-riding problem for investing the resources necessary for figuring out the best way to make it work. 
• As a related indirect argument, forecasting has a pretty straightforward a priori case (more accurate information leads to better decision-making), and there are plenty of candidate explanations for why its widespread adoption would have been difficult despite forecasting having the potential to be widely useful (e.g. I’m sympathetic to the points made by MaxRa here). Thus, even after updating on the observation that judgmental forecasting hasn’t conquered the world yet, I don’t think we should assign high confidence that it will forever stay a niche industry. 
• As others have pointed out, only a fairly small fraction of Open Phil’s spending has gone into forecasting so far (about 1%), and this is unlikely to dramatically change in the future. The forecasting community doesn’t need to become a multi-billion industry to justify that level of spending. 

I'm in the process of writing up my thoughts on forecasting in general and particularly EA's reverence for forecasting but I feel, similar to @Grayden that forecasting is a game that is nearly perfectly designed to distract EAs from useful things. It's a combination of winning, being right when others are wrong and seemingly useful, all wrapped into a fun game.

I'd like to see tangible benefits to more broad funding of forecasting that seems to be done in t he millions and tens of millions of dollars.

I would also be the type of person you would think would be a greater fan of forecasting. I'm the number one forecaster on Manifold and I've made tens of thousands of dollars on Polymarket. But I think we should start to think of forecasting as more of a game that EAs like to play, something like Magic the Gathering that is fun and has some relations to useful things but isn't really useful by itself.

As the program is about forecasting, what is your stance on the broader field of foresight & futures studies? Why is forecasting more promising than some other approaches to foresight?

This will be a total waste of time and money unless OpenPhil actually pushes the people it funds towards achieving real-world impact. The typical pattern in the past has been to launch yet another forecasting tournament to try to find better forecasts and forecasters. No one cares, we already know how to do this since at least 2012!

The unsolved problem is translating the research into real-world impact. Does the Forecasting Research Institute have any actual commercial paying clients? What is Metaculus's revenue from actual clients rather than grants? Who are they working with and where is the evidence that they are helping high-stakes decision makers improve their thought processes?

Incidentally, I note that forecasting is not actually successful even within EA at changing anything: superforecasters are generally far more relaxed about Xrisk than the median EA, but has this made any kind of difference to how EA spends its money? It seems very unlikely. 

I‘m really excited about more thinking and grant-making going into forecasting!

Regarding the comments critical of forecasting as a good investment of resources from a world-improving perspective, here some of my quick thoughts:

1. Systematic meritocratic forecasting has a track record of outperforming domain experts on important questions - Examples: Geopolitics (see Superforecasting), public health (see COVID), IIRC also outcomes of research studies

2. In all important domains where humans try to affect things, they are implicitly forecasting all the time and act on those forecasts. Random examples: - "If lab-grown meat becomes cheaper than normal meat, XY% of consumers will switch" - "A marginal supply of 10,000 bednets will decrease malaria infections by XY%" - Models of climate change projections conditional on emmissions

3. In many domains humans are already explicitly forecasting and acting on those forecasts - Insurance (e.g. forecasts on loan payments) - Finance (e.g. on interest rate changes) - Recidivism - Weather - Climate

4. Increases in use of forecasting has the potential to increase societal sanity - Make people more able to appreciate and process uncertainty in important domains - Clearer communication (e.g. less talking past one another by anchoring discussion on real world outcomes) - Establish feedback loops with resolvable forecasts ➔ stronger incentives for being correct & ability to select people who have better world models

That said, I also think that it's often surprisingly difficult to ask actionable questions when forecasting, and often it might be more important to just have a small team of empowered people with expert knowledge combined with closely coupled OODA loops instead. I remember finding this comment from Jan Kulveit pretty informative:

In practice I’m a bit skeptical that a forecasting mindset is that good for generating ideas about “what actions to take”. “Successful planning and strategy” is often something like “making a chain of low-probability events happen”, which seems distinct, or even at tension with typical forecasting reasoning. Also, empirically, my impression is that forecasting skills can be broadly decomposed into two parts—building good models / aggregates of other peoples models, and converting those models into numbers. For most people, the “improving at converting non-numerical information into numbers” part has initially much better marginal returns (e.g. just do calibration trainings...), but I suspect doesn’t do that much for the “model feedback”.

Source: https://ea.greaterwrong.com/posts/by8u954PjM2ctcve7/experimental-longtermism-theory-needs-data#comment-HgbppQzz3G3hLdhBu

Awesome to hear! I'm happy that OpenPhil has promoted forecasting to its own dedicated cause area with its own team; I'm hoping this provides more predictable funding for EA forecasting work, which otherwise has felt a bit like a neglected stepchild compared to GCR/GHD/AW. I've spoken with both Ben and Javier, who are both very dedicated to the cause of forecasting, and am excited to see what their team does this year!