Summary
EA and rationalists got enamoured with forecasting and prediction markets and made them part of the culture, but this hasn’t proven very useful, yet it continues to receive substantial EA funding. We should cut it off.
My Experience with Forecasting
For a while, I was the number one forecaster on Manifold. This lasted for about a year until I stopped just over 2 years ago. To this day, despite quitting, I’m still #8 on the platform. Additionally, I have done well on real-money prediction markets (Polymarket), earning mid-5 figures and winning a few AI bets. I say this to suggest that I would gain status from forecasting being seen as useful, but I think, to the contrary, that the EA community should stop funding it.
I’ve written a few comments throughout the years that I didn’t think forecasting was worth funding. You can see some of these here and here. Finally, I have gotten around to making this full post.
Solution Seeking a Problem
When talking about forecasting, people often ask questions like “How can we leverage forecasting into better decisions?” This is the wrong way to go about solving problems. You solve problems by starting with the problem, and then you see which tools are useful for solving it.
The way people talk about forecasting is very similar to how people talk about cryptocurrency/blockchain. People have a tool they want to use, whether that be cryptocurrency or forecasting, and then try to solve problems with it because they really believe in the solution, but I think this is misguided. You have to start with the problem you are trying to solve, not the solution you want to apply. A lot of work has been put into building up forecasting, making platforms, hosting tournaments, etc., on the assumption that it was instrumentally useful, but this is pretty dangerous to continue without concrete gains.
We’ve Funded Enough Forecasting that We Should See Tangible Gains
It’s not the case that forecasting/prediction markets are merely in their infancy. A lot of money has gone into forecasting. On the EA side of things, it’s near $100M. If I convince you later on in this post that forecasting hasn’t given any fruitful results, it should be noted that this isn’t for lack of trying/spending.
The Forecasting Research Institute received grants in the 10s of millions of dollars. Metaculus continues to receive millions of dollars per year to maintain a forecasting platform and conduct some forecasting tournaments. The Good Judgment Project and the Swift Centre have received millions of dollars for doing research and studies on forecasting and teaching others about forecasting. Sage has received millions of dollars to develop forecasting tools. Many others, like Manifold, have also been given millions by the EA community in grants/investments at high valuations, diverting money away from other EA causes. We have grants for organizations that develop tooling, even entire programming languages like Squiggle, for forecasting.
On the for-profit side of things, the money gets even bigger. Kalshi and Polymarket have each raised billions of dollars, and other forecasting platforms have also raised 10s of millions of dollars.
Prediction markets have also taken off. Kalshi and Polymarket are both showing ATH/growth in month-over-month volume. Both of them have monthly volumes in the 10s of billions of dollars. Total prediction market volume is something like $500B/year, but it just isn’t very useful. We get to know the odds on every basketball game player prop, and if BTC is going to go up or down in the next 5 minutes. While some people suggest that these trivial markets help sharpen skills or identify good forecasters, I don’t think there is any evidence of this, and it is more wishful thinking.
If forecasting were really working well and was very useful, you would see the bulk of the money spent not on forecasting platforms but directly on forecasting teams or subsidizing markets on important questions. We have seen very little of this, and instead, we have seen the money go to platforms, tooling, and the like. We already had a few forecasting platforms, the market was going to fund them itself, and yet we continue to create them.
There has also been an incredible amount of (wasted) time by the EA/rationality community that has been spent on forecasting. Lots of people have been employed full-time doing forecasting or adjacent work, but perhaps even larger is the amount of part-time hours that have gone into forecasting on Manifold, among other things. I would estimate that thousands of person-years have gone into this activity.
Hits-based Giving Means Stopping the Bets that Don’t Pay Off
You may be tempted to justify forecasting on the grounds of hits-based giving. That is to say, it made sense to try a few grants into forecasting because the payoff could have been massive. But if it was based on hits-based giving, then that implies we should be looking for big payoffs, and that we have to stop funding it if it doesn’t.
I want to propose my leading theory for why forecasting continues to receive 10s of millions per year in funding. That is, it has become a feature of EA/rationalist culture. Similar to how EAs seem to live in group houses or be polyamorous, forecasting on prediction markets has become a part of the culture that doesn’t have much to do with impact. This is separate from parts of EA culture that we do for impact/value alignment reasons, like being vegan, donating 10%+ of income, writing on forums, or going to conferences. I submit that forecasting is in the former category.
At this point, if forecasting were useful, you would expect to see tangible results. I can point to you hundreds of millions of chickens that lay eggs that are out of cages, and I can point to you observable families that are no longer living in poverty. I can show you pieces of legislation that have passed or almost passed on AI. I can show you AMF successes with about 200k lives saved and far lower levels of malaria, not to mention higher incomes and longer life expectancies, and people living longer lives that otherwise wouldn’t be because of our actions. I can go at the individual level, and I can, more importantly, go at the broad statistical level. I don’t think there is very much in the way of “this forecasting happened, and now we have made demonstrably better decisions regarding this terminal goal that we care about”. Despite no tangible results, people continue to have the dream that forecasting will inform better decision-making or lead to better policies. I just don’t see any proof of this happening.
Feels Useful When It Isn’t
Forecasting is a very insidious trap because it makes you think you are being productive when you aren’t. I like to play bughouse and a bunch of different board games. But when I play these games, I don’t claim to do so for impact reasons, on effective altruist grounds. If I spend time learning strategy for these board games, I don’t pretend that this is somehow making the world better off. Forecasting is a dangerous activity, particularly because it is a fun, game-like activity that is nearly perfectly designed to be very attractive to EA/rationalist types because you get to be right when others are wrong, bet on your beliefs, and partake in the cultural practice. It is almost engineered to be a time waster for these groups because it provides the illusion that you are improving the world’s epistemics when, in reality, it’s mainly just a game, and it’s fun. You get to feel that you are improving the world’s epistemics and that therefore there must be some flow-through effects and thus you can justify the time spent by correcting a market from 57% to 53% on some AI forecasting question or some question about if the market you are trading on will have an even/odd number of traders or if someone will get a girlfriend by the end of the year.
Conclusion
A lot of people still like the idea of doing forecasting. If it becomes an optional, benign activity of the EA community, then it can continue to exist, but it should not continue to be a major target for philanthropic dollars. We are always in triage, and forecasting just isn’t making the cut. I’m worried that we will continue to pour community resources into forecasting, and it will continue to be thought of in vague terms as improving or informing decisions, when I’m skeptical that this is the case.
[Relevant context/COI: I'm CEO at the Forecasting Research Institute (FRI), an organization which I co-founded with Phil Tetlock and others. Much of the below is my personal perspective, though it is informed by my work. I don't speak for others on my team. I’m sharing an initial reply now, and our team at FRI will share a larger post in future that offers a more comprehensive reflection on these topics.]
Thanks for the post — I think it's important to critically question the value of funds going to forecasting, and this post offers a good opportunity for reflection and discussion.
In brief, I share many of your concerns about forecasting and related research, but I'm also more positive on both its impact so far and its future expected impact.
A summary of some key points:
- Much of the impact of forecasting research on specific decision-makers is not public. For example, FRI has informed decisions on frontier AI companies' capability scaling policies, has advised senior US national security decision-makers, and has informed research at key US and UK government agencies. But, we are not able to share many details of this work publicly. However, there is also public evidence that forecast
... (read more)Stripped of all AI-centred argumentation, the reply is left mostly empty. This suggests that judgmental forecasting, at least as exercised by FRI, should perhaps be thought of as a sub-domain of AI safety. In such a case, its impact would need to be evaluated in the portfolio context of all AI safety budgets, meaning a much higher hurdle rate would have to be cleared to justify its activities.
What more broadly applies to judgmental forecasting and online betting platforms -- and is also the basis for many arguments in this defence of forecasting -- is the circular reasoning regarding the field's importance, frequently repeated by the field's own and those adjacent to it. But, in contrast to the opinionated voices, the evidence is lacking. Merely stating that forecasting has informed some policy or that career decisions have been influenced is not sufficient. Similarly, whether its impact is positive or negative is taken at face value and never substantiated.
All this isn't to say that judgmental forecasting research or its funding should be dispensed with. In fact, hybrids that combine quantitative predictive models with expert judgment are among the foundational tools of large organisations' decision-making processes. However, I believe the field's association with online betting (high time we called things for what they are) as well as over-reliance on AI for its services is actually hurting it.
Hi Josh, thanks for the response.
I hate to do this, especially at the start, but I want to point out for you and others who have jobs related to forecasting that it's difficult to convince someone of something when their job relies on them not believing it. I think you should assume that you will think forecasting is more useful than it is.
As for your points, I'll respond to some of them.
I don’t disagree with some of the fundamentals of this post. Before diving into that, I want to correct a factual error:
“the Swift Centre have received millions of dollars for doing research and studies on forecasting and teaching others about forecasting”
The Swift Centre for Applied Forecasting has not received millions in funding. The majority of our earnings have been through direct projects with organisations who want to use forecasting to inform their decisions.
On your wider argument. I think forecasting has probably received too much funding and the vast majority of that has misallocated on platforms and research. I believe some funding (hundreds of thousands) to maintain core platforms like Metaculus as a public good of information. Though, services like Polymarket can probably fill most of this need in the future (but many useful, informative markets would never reach the necessary volume to be reliable).
Where I think we disagree most is in the application of forecasting and some of the achievements. We’ve worked with frontier AI labs to inform their decisions, are currently advising a U.K. Minister’s team on a central piece of their policy, and are about to start a secondm... (read more)
"We’ve worked with frontier AI labs to inform their decisions"
This feels likely net negative to me? But don't have enough information to know.
I think this post significantly overstates its conclusion and is plausibly poorly calibrated on the relative value of forecasting.
My main "directional" issues with the post as it's currently written:
- I think it overstates the amount of funding devoted to forecasting on a "worldview" basis.
- Most forecasting funding is (iiuc) not going to neartermist causes or particularly fungible with neartermist causes, so pointing to a bunch of neartermist causes to justify better funding options seems irrelevant.
- From my perspective, it seems like:
- Within Animal welfare fungible money, very little goes into forecasting e.g. less than $2M per year
- Tbh - I would probably prefer that more money went into some kinds of forecasting on the margin. For example, I think that people are generally too bullish on clean meat, and Linch/Open Phil's work investigating the difficulty of clean meat has plausibly resulted in better allocation of millions of dollars because there are, in fact, good alternatives (like cage-free campaigns).
- Within Longtermist/AI fungible money, maybe $10M/year goes into forecasting, which seems pretty reasonable to me but i think to get to 10M you need to be including projects that seems
... (read more)I work at Founders Pledge, which has made many forecasting-related grants, some of them quite recently. Like Marcus, I’ve been fairly successful at forecasting — I am a so-called superforecaster a — but have a fair amount of skepticism. My views here are personal ones, not FP’s.
I have some agreements and disagreements with this post. The main point of agreement I have is with Marcus’ “vibe” here: I think forecasting’s apparent status and prominence among EAs outstrip either its prima facie promisingness or the to-date empirical support for its use.
I’m not sure that I agree that too much has been spent on forecasting, and I definitely don’t agree that enough time has passed that we’d know by now whether this work has been useful. We’re talking about a very short period of time here.
I think we’re at risk of conflating a bunch of different kinds of forecasting work:
- Investments in calibration: Funding new techniques or experiments in more effective forecasting
- Investments in diffusion: Broadly, attempts to “make forecasting a thing” by supporting e.g. new platforms
- Investments in capacity: Attempts to propagate or institutionalize formal forecasting at influential institutions
- Investments
... (read more)For context: I founded Inkling Markets in 2006 out of Y Combinator running internal prediction markets for companies and governments, and then Cultivate Labs in 2014, which has participated in some of the projects this community has funded. So I've watched this play out for 20 years. Before IARPA ACE and Tetlock's Superforecasters, before FRI, before most of what's mentioned in this thread existed.
Two things I think this debate is missing:
1) On whether the OP/CG money was wasted. Several commenters imply specific grants were boondoggles and this was recently mentioned in Nuño’s forecasting newsletter as well. The stated goal of much of that funding was to influence decision-making inside governments, particularly the US Government.
Anyone who has actually tried this knows it's an extremely expensive and difficult endeavor. For example, just getting in the door to talk to the people who have budgets to spend requires former senior officials on your team to make introductions. These are some of the highest demand people in Washington because of their networks. Then if you get through the door and eventually get to yes, the procurement and contracting takes months or doesn't end up eve... (read more)
[COI: I work at the Swift Centre as a forecaster, I have worked for a prediction market, I am very involved in forecasting. It is not my current work however, which is on community notes]
A few things points attempting to say things other commenters haven't, though I largely agree with the critical comments and the things they agree with Marcus on:
I agree that the $100M doesn't seem super well allocated. Not because forecasting is useless, but because the money flowed to big institutions and platforms rather than smaller, weirder, mechanism-design bets. I like Metaculus, but it has absorbed a lot of money in the last 5 years and not clearly changed much. I don't know if I think FRI has been worth it, I am glad someone has done the research but, again, how much are we talking? I would have preferred smaller projects were funded on the margin. Coefficient's strategy in forecasting has felt poor to me, often ignoring the community who in my view come up with the most interesting projects and going for marginal spending on incumbents.
Nobody funds mechanism design or institutional epistemics. I recently spoke to someone at a household name enormous tech company who described their ... (read more)
I tend to agree with the OP, but think there are a couple of other points about subsidising prediction markets which could have had more emphasis
- Forecasts are a market in which people trade money, which makes it easy for them to function on a for profit model if there is significant interest in participation. Even if prediction markets are objectively highly valuable, it is not clear there is sufficient altruism-relevant benefit in forecasting quality coming from subsidised rather than non-subsidised platforms to justify the subsidy [1]
- Forecasting for profit is zero sum,[2] which means every superforecaster is balanced out by an equal and opposite amount of money collectively lost by others who are less "well calibrated". Many people are perhaps happy to net lose money gambled for entertainment or signalling purposes (though perhaps they could part with their cash in other ways which deliver more positive outcomes...), but others may be developing gambling habits which can be extremely self destructive[3]. I guess this links to Marcus' "feels like doing something useful where it isn't" point, but it can be much worse than simply a distraction. Negative externalities can b
... (read more)For Kalshi specifically, it seems to have essentially become a backdoor to deregulate sports gambling in every US state. The mass deregulation of gambling in the US this decade feels harmful and like something we’ll probably really regret (legalisation seems fine but not like this).
It doesn’t seem popular to criticise the gambling aspects of prediction markets here, but it does seem strange to me that EAs seem to care a lot about reducing harms from tobacco and alcohol, but seem indifferent to gambling.
As someone who has dedicated their PhD to researching forecasting, I think this article raises an important point—but states it too bluntly, which ultimately muddles its central argument.
First, some context. Broadly speaking, all decisions are made on the basis of expectations about the future. It follows that anything which shifts those expectations can affect decisions in meaningful ways. This is easy to overlook, but it matters for how we evaluate forecasting interventions.
Furthermore, it is not easy to observe the effects of research. Consider the work of the Forecasting Research Institute. Much of what FRI does is research, and most of the benefits of research accrue in the future and are not easily traced back to any single input. The same difficulty applies more generally: since we cannot observe the counterfactual world in which a forecasting intervention was never made, measuring its effect is genuinely hard. How many decisions were improved? We may never know with precision.
That said, I do think the author raises a valid concern. We should be highly skeptical of the large investments of time and money by philanthropic actors and others in this space. Since many people fin... (read more)
Hi Marcus. Thanks for the post. I broadly agree.
Coefficient Giving's (CG's) Forecasting Fund has recently been closed.
I think this is more likely to make forecasting grants useful. They will presumably be assessed with the criteria used to evaluate the non-forecasting grants of the respective fund.
@NunoSempere wrote about the end of CG's Forecasting Fund in the last edition of the Forecasting Newsletter. Only paid subscribers can check the relevant section.
Right.
What are the other 3 on your Mount Rushmore?
I have at least three reasons to be hopeful:
That's not to say that every project previously funded around forecasting was a good use of money. I would probably agree with you regarding most of the projects you have in mind, while disagreeing with the title and framing which is way too broad.
Related comment I made 2 years ago and ensuing discussion: https://forum.effectivealtruism.org/posts/ziSEnEg4j8nFvhcni/new-open-philanthropy-grantmaking-program-forecasting?commentId=7cDWRrv57kivL5sCQ
I definitely have my own gripes about EA/rationalist attitudes towards forecasting (see here), but maybe your objection is a level confusion:
- I think when people talk about "leveraging forecasting into better decisions", they're saying: "'Better' decisions just are decisions guided by the normatively correct beliefs. Namely, they're decisions that make reasonable-seeming tradeoffs between possible outcomes given the normatively correct beliefs about the plausibility of those outcomes. So our decisions will be more aligned with this standard of 'better' if our beliefs are formed by deferring to well-calibrated forecasts."
- E.g. they're saying, "When navigating AI risk, we'll make decisions that we endorse more if those decisions are guided by the credences of folks who've been unusually successful at forecasting AI developments."
- (At least, that's the steelman. Maybe I'm being too charitable!)
- Whereas you seem to be
... (read more)I strongly agree with the author's viewpoint, and I also strongly agree that long-term predictions in chaotic systems (such as predictions about events three years in advance) are, in most cases, a form of self-comfort, a resistance to the uncertainty of the future. Essentially, it's a psychological comfort of seeking certainty, rather than a rigorous, systematic argument.
Specifically, in complex dynamics, there's the concept of the Lyapunov exponent, a classic application in meteorology. Any weather forecast exceeding 14 days is almost indistinguish... (read more)
I think one should distinguish between several things here:
This post really belabours the first and second bullet point, perhaps because that is where a lot of money has gone to, but there can be a lot of value in the third.
Yea, this is fair. I am much more sympathetic to non-PM forecasting than I am PM/judgemental forecasting. The ideas in this post were really developed in 2023/2024 when I saw EAs spending a ton of time on Manifold/Metaculus, investing at high valuations, generally revering prediction markets for decision making, etc. whereas what I was seeing was completely different.
I really believe in following the money. I think if we spend $100M on forecasting and $90M of it went to prediction market-style forecasting, I think it's fair to basically lump it all together. It'd be one thing if PMs were a small experiment within broad forecasting, but its been the main thing.
I suspect that the main use of forecasting is if you need a probability for something and you don't really have time to look into it yourself or you wouldn't trust your judgement even if you did.
Curious what readers here think!
Ideally read/skim both @Marcus Abramovitch's post and @Jhrosenberg's response (currently top comment) before voting.
Note this will obviously not be representative, it's just a quick opinion poll.
This isn't something I've thought a ton about but I think forecasting should plausibly still receive funding in a specific way:
Funders should either pay forecasters to make predictions on important questions, or subsidize prediction markets on those questions.
I don't think forecasting is a "solution seeking a problem." There are tons of important but hard-to-predict questions that I'd like better forecasts on! The problem is that the ecosystem hasn't done a great job of turning dollars into good forecasts.
For example, most of my Metaculus questions are thi... (read more)
"I don’t think there is very much in the way of “this forecasting happened, and now we have made demonstrably better decisions regarding this terminal goal that we care about”."
I assume some people disagree with this strong claim. One example I've heard was AGI timelines and their influence on AI safety field priorities - though I guess one could answer that certain reports or expert opinions where disproportionately more useful than prediction markets.
On a different point, I appreciated Eli Lifland's past comment on many intellectual activities (such as g... (read more)
I agree with some of this. But let me attempt a conciliatory take: less of forecasting money and effort should go to platforms and tournaments, but more should go to identifying existing, nascent forecasts (people using the word "probably" or "unlikely" about empirical matters) and creating markets (even unsubsidized Manifold markets would be helpful on the margin). I think it would be very helpful for someone to go through popular EA forum posts and org research documents and do this systematically.
Prediction markets seem to be a great business (mostly gambling with all the problems associated with it) so “funding” in the sense of investing in them could be sensible while “funding” in the donation sense not. (And then later donation to AMF or similar).
In general, I’m hesitant to donate to stuff that’s plausibly just a really good business in its own right.
I'm reconsidering this point. It seems intuitive, but what is the strongest argument that this is "wrong"?
I share the expressed concern but respectfully disagree with the major suggestion.
First, «overrating» is a perception problem, not purely an industry problem. People are free to believe in things, and sometimes they overrate them. The forecasting community did not force anyone to fund platforms over applied work. That was a series of decisions made by funders who could have chosen differently. Blaming the field for how it was funded seems like misplaced accountability.
Second, I am genuinely troubled by the premise of «tangible result delivery» as the prima... (read more)
I like bets involving donations, and investments as alternatives to forecasting without money on the line.
Off topic, but one additional thing I noticed about this list:
Is the glaring la... (read more)
Counterargument: the internet had its theoretical underpinnings start approximately 1959-1960, with first grants for ARPANET in 1966-1969.
The whole thing was then not very useful until the 1990's.
You could pick earlier dates for theroetical underpinnings of the internet if you wanted, too.
I think prediction markets are more similar to the internet than to cryptocurrency: they require a mix of technology and infrastructure but also a change in human habits. Theoretically... (read more)
A bit tangential to the main thrust of this post, but I have been wondering lately about some the regulatory aspects around prediction markets. Recently there was the scandal of a soldier who allegedly made $400,000 from insider information about the Maduro raid. There is particular interest in the US on banning sports betting, which is seen (accurately) as another form of gambling. Minnesota might ban prediction markets entirely.
Stepping back from the merits of this specific proposal, I see it as a part of a troubling broader anti-innovation trend. We hav... (read more)