Astral Codex Ten

Forecasting Research Institute

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Forecasting Research Institute

Article

Forecasting Research Institute is a recurring organization in the Astral Codex Ten archive, appearing 10 times across 10 issues between December 19, 2022 and February 02, 2026. The archive places it in contexts such as “Philip Tetlock and others have founded the Forecasting Research Institute to study prediction”; “Ezra is research director at the Forecasting Research Institute”; “co-founder of the Forecasting Research Institute”. It most often appears alongside Metaculus, Manifold, OpenAI.

Metadata

  • Category: Organizations
  • Mention count: 10
  • Issue count: 10
  • First seen: December 19, 2022
  • Last seen: February 02, 2026

Appears In

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

Open Thread 255
December 19, 2022 · Original source
1: Philip Tetlock and others have founded the Forecasting Research Institute to study prediction, including prediction of existential risk. They are looking to hire “research and data analysts, content editors, and RAs”, all roles remote, see here for details.
Inline links: Forecasting Research Institute, see here for details
Who Predicted 2022?
January 24, 2023 · Original source
7th: Ezra Karger. Ezra is research director at the Forecasting Research Institute. This contest is an amateurish retreading of work that FRI’s Philip Tetlock already did much more formally years ago, and we feel honored that he entered at all. I’m not sure why it should be the case that forecasting researchers are also excellent forecasters, but Ezra has adequately demonstrated this at least in his own case.
Inline links: Forecasting Research Institute
Berkeley Meetup On Sunday, Special Guest Philip Tetlock
July 19, 2023 · Original source
Why: Philip Tetlock, co-author of Superforecasting and co-founder of the Good Judgment Project and the Forecasting Research Institute, is in town and has kindly agreed to come to an ACX meetup.
Inline links: Superforecasting, Good Judgment Project, Forecasting Research Institute
The Extinction Tournament
July 20, 2023 · Original source
This month’s big news in forecasting: the Forecasting Research Institute has released the results of the Existential Risk Persuasion Tournament (XPT). XPT was supposed to use cutting-edge forecasting techniques to develop consensus estimates of the danger from various global risks like climate change, nuclear war, etc.
Inline links: released the results of the Existential Risk Persuasion Tournament
23
October 31, 2023 · Original source
But also, the media is a dignified, official institution, and it prefers interacting with other dignified, official institutions. It likes being able to say “a professor from Harvard said X”, and not “this guy who does really well betting on Manifold says X”. He talked about wanting to quote a superforecaster from Samotsvety Forecasts, a leading prediction group, but expected his editors to ask why these people with the weird Russian name were relevant or trustworthy. It’s easier to cite someone who is “a fellow at the Forecasting Research Institute”, which has the same kind of official ring as “a professor at Harvard”.
Inline links: Samotsvety Forecasts
In Continued Defense Of Effective Altruism
November 28, 2023 · Original source
Sparked a renaissance in forecasting, including major roles in creating, funding, and/or staffing Metaculus, Manifold Markets, and the Forecasting Research Institute.
Inline links: Metaculus, Manifold Markets, Forecasting Research Institute
Who Predicted 2023?
March 05, 2024 · Original source
Ezra Karger is an economist at the Federal Reserve Bank of Chicago, and the research director at the Forecasting Research Institute.
Inline links: Forecasting Research Institute
Ezra Karger: …is a possible exception to the above claim. He’s a non-random individual - director of the Forecasting Research Institute - and has previously placed very highly in contests like these (he placed 7th in last year’s ACX contest). Based on this, I suspect his performance was mostly repeatable skill and not just luck. He outscored all but four of our 4,215 Blind Mode and Full Mode participants, which puts him above the 99.9th percentile. Since he entered Full Mode, he was allowed to do complicated technical things, and he described his method as:
24
May 13, 2024 · Original source
People changed their minds a little over time, but not in a very consistent way that mattered much in the end. What was the “client feedback”? The report says: Client feedback was provided to the Superforecasters on December 21. The client posed questions to the Superforecasters about their assessments up to that date and asked for their reactions to several studies and articles. In the days following the client engagement, the Superforecasters lowered their confidence in the natural zoonosis hypothesis from 73% to 67%, although zoonosis remained the most likely potential cause in their assessment. But following an active engagement with recent genomic studies and historical base rates of zoonotic spillovers, those numbers began to return to earlier levels. January also saw increased attention to the geopolitical context and transparency issues, particularly related to research activities in Wuhan Is this bad? I’m imagining a pro-lab-leak client saying “But what about [this list of pro-lab-leak arguments]?” and then the superforecasters read them and adjust. In one sense, it’s good that they got to see more arguments; on the other, it seems like a potential route by which clients could bias the results - probabilities never quite got back to where they were before the feedback, though they got pretty close. The last-minute spike for zoonosis might be the Rootclaim debate results, which were released on 2/18. So maybe the client feedback and the Rootclaim results both slightly affected the numbers, but mostly the superforecasters started out pro-zoonosis and stuck to their guns. Dan Schwarz and the FutureSearch team say that forecasting has a “rationale-shaped hole”. Despite the report making this sound like a pretty intense process, we don’t get much information about details: In their extensive discussions , Good Judgment’s Superforecasters assessed base rates and historical patterns, existing evidence and scientific analysis, geopolitical context and transparency concerns, trust in intelligence communities, and methodological constraints. 1. Base Rates and Historical Patterns: The Superforecasters frequently referenced base rates, i.e., the history of pandemics emerging from natural zoonosis versus the history of laboratory leaks, to anchor their probabilities. For the former, they discussed how the base rates are changing as the climate warms and as expanding human populations push farther into natural environments that previously saw little human presence. For the latter, they acknowledged that it has only been 12 years since the advent of CRISPR gene- editing tools, and the base rate of lab leaks in the short synthetic biology era is not yet well established. 2. New Evidence and Scientific Analysis: Throughout the period, the Superforecasters adapted their forecasts in light of new scientific evidence, including genomic analyses of SARS-CoV-2 and its relation to bat viruses, and the debate over potential laboratory manipulation. 3. Geopolitical Context and Transparency Concerns: The geopolitical implications of the virus’s origins, particularly in relation to China’s transparency and the involvement of international research institutions, played a significant role in the analysis. Concerns over data veracity, and over the political ramifications of determining that the pandemic’s origins were other than zoonosis, were extensively debated. 4. Trust in Intelligence: Commentary on trust in intelligence communities and discussions about the impact of geopolitical biases on the interpretation of evidence illustrated the complex interplay between science, politics, and human behavior in assessing the pandemic’s origins. 5. Methodological Critiques and the Evaluation of Evidence: The Superforecasters engaged in methodological critiques of the evidence base, including the scrutiny of laboratory practices and biocontainment levels [...] In the end, most Superforecasters were in rough agreement on issues like the base rates of zoonotic spillover. Where they most often disagreed was on the interpretation of actions by Chinese officials and whether their actions reflected how an authoritarian government would react in any crisis over which it did not have full control, or whether those actions were indicative of attempts to cover up a biomedical research-related accident that allowed the SARS-CoV-2 virus to enter circulation in China and, ultimately, the entire globe. Probably it would be too much to ask for to get a transcript of all their discussions - then they’d be nervous saying things that might make them look bad to an audience. What would be a good balance between getting more information and not imposing on their time? Forecasting is an unusually legible and easy-to-judge domain. One of the theories of change for forecasting was to use it to identify smart people with good reasoning, then turn them loose on less well-behaved problems. This is one of the first big attempts to do this at scale. How did it work? We can’t tell, because it’s inherently an illegible and hard-to-judge domain. Darn. I don’t know what I expected. Notes From A Local Optimum Austin’s concern - that forecasting has reached a local optimum - is widely shared. We have some good sites: Manifold, Metaculus, Polymarket, GJO, etc - all doing good work. We have good-ish probabilities for a few important questions. Every so often a news source cites them. Sometimes a decision-maker looks at them behind the scenes, maybe. Is this all there is? The FutureSearch team says the next step is to focus on “rationale”. We need to use forecasting not just to get a raw probability, but to explain what’s going on and why we think something. Then instead of just convincing policy-makers to trust forecasts, we can tell them why something is true, or inform their discussions even if they’re not willing to blindly trust a number. Is this a betrayal of the forecasting ethos? The original dream was that instead of a bunch of people giving arguments, we could just test who was right. Now we’re going back to the arguments? People have argued forever; what does forecasting add to that? Well, they add the knowledge that the arguments are from people who have been right a lot before and are incentivized to be right again. Still, it’s not a natural fit. Probably it’s relevant here that FutureSearch’s forecasting AI does a really good job of this by default, in a way humans can’t match. Nuno’s yearly forecasting roundup doesn’t have a single thesis, but the first part is a well-supported complaint that most forecasting sites aren’t good business. They either burn VC money, burn EA donations, or converge towards casinos to support themselves. He gives an honorable exception to Cultivate Labs, which sells prediction market software rather than the results themselves. Open Philanthropy (billionaire Dustin Moskovitz’s EA-aligned charitable foundation) has at least given forecasting a vote of confidence, recently choosing to promote it to one of their main donation areas. Still, they got a lot of pushback on the decision, for example SuperDuperForecasting here: 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. And Marcus Abramovich here: 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. Eli Lifland has a long and hard-to-summarize comment here, response from Ozzie Gooen here, podcast between them on “Is Forecasting A Promising EA Cause Area?” here. I’m split on this. My previous hope was that the field would gradually grow, without any qualitative changes or discontinuities, until it became big enough that journalists and policy-makers were aware of it and took it seriously (compare eg the growth of the Internet as a scholarly resource). I think the strongest argument against this is Manifold’s relatively flat user numbers. Is there a new hope? I think if nothing else, forecasting might be useful as a testing ground: First, to create forecasting AIs (like FutureSearch) which can then get consulted on a variety of questions, eg by policy-makers. The biggest holdup has always been the need to gather 20 or 50 or however many hard-to-find superforecasters for whatever question you’re asking, and then trust their advice even though they’re fallible fleshbag humans. If you can use the 20 to 50 superforecasters to inspire an AI, and then test the AI and prove it’s good, people might be more interested. This is especially true if the AI can branch out beyond traditional forecasting questions. Once we have a few of these, we can start comparing the next generation of AIs to the previous generation, and skip the superforecasters.
Inline links: forecasting has a “rationale-shaped hole”, Nuno’s yearly forecasting roundup, Cultivate Labs, choosing to promote it to one of their main donation areas, here, superforecasters are generally far more relaxed about Xrisk, here, @Grayden, here, here, “Is Forecasting A Promising EA Cause Area?” here
Mantic Monday: The Monkey's Paw Curls
January 13, 2026 · Original source
The second is to conclude that prediction markets’ role in God’s plan was only to provide the foundation for AI superforecasters - the training data, the benchmarking arena, and the pot of money that rewards innovation. Once AI superforecasters are developed, then (for all that the rest of us care), the markets themselves can wither away into the sports gambling casinos they so desperately want to become. The Forecasting Research Institute’s linear extrapolation shows AIs are on track to match top humans “by late 2026”
Inline links: AIs are on track
4: Forecasting Research Institute has established a Longitudinal Expert AI Panel of scientists and forecasters to map changing expert opinion on AI over time. Experts predict “significantly less AI progress than leaders of frontier AI companies” but “much faster AI progress than the general public.
Inline links: Longitudinal Expert AI Panel
Open Thread 419
February 02, 2026 · Original source
GreeneiBot2, the contest’s highest-ranking bot, who placed 9th out of 2975 participants, mostly human. Another bot, metac-o1+asknews, came in 11th. Some work from the Forecasting Research Institute suggests that bot forecasters will overtake humans sometime in late 2026, and these AIs are clearly chomping at the bit to make it happen. If you’re the person or team behind either of these, send me an email and I’ll profile you.