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Press & News - Good Judgment Inc.
webCredibility Rating
3/5
Good(3)Good quality. Reputable source with community review or editorial standards, but less rigorous than peer-reviewed venues.
Rating inherited from publication venue: Good Judgment
Good Judgment Inc. applies superforecasting techniques that some AI safety researchers use to estimate AI development timelines and risk probabilities; this press page is primarily a corporate media archive with limited direct AI safety content.
Metadata
Importance: 22/100press releasehomepage
Summary
Good Judgment Inc. is the commercial spinoff of the Good Judgment Project, a superforecasting research initiative that emerged from IARPA's Aggregative Contingent Estimation (ACE) program. This press page aggregates media coverage and news about the company's forecasting products and research. Good Judgment's work on calibrated probability estimation is relevant to AI safety efforts around forecasting AI development timelines and risks.
Key Points
- •Good Judgment Inc. commercializes superforecasting methods developed through the Good Judgment Project, founded by Philip Tetlock
- •The organization provides probabilistic forecasting tools and training used by governments and corporations for strategic decision-making
- •Superforecasting methodology is relevant to AI safety for estimating timelines, risk probabilities, and policy outcomes
- •Press coverage reflects growing institutional interest in structured forecasting for complex geopolitical and technological questions
- •The company's Superforecaster network and methods inform efforts to reduce uncertainty in long-range forecasting relevant to existential risk
Cited by 2 pages
| Page | Type | Quality |
|---|---|---|
| Good Judgment (Forecasting) | Organization | 50.0 |
| Philip Tetlock | Person | 73.0 |
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Superforecasting & Good Judgment In The Press/News
About > Press & News
The latest buzz on Superforecasting
Superforecasting's enormous potential has caught the attention of businesses, government, and the press.
Read the latest news about Superforecasting and Good Judgment.
The Good Judgment team is available for interviews, speaking opportunities, and other engagements.
Please contact us to discuss.
Media Contact:
[email protected]
Featured Stories
WP Intelligence: Forecasting in the age of AI and prediction markets
Washington Post (27 March 2026)
Most people make forecasts based on gut instinct and headlines. Superforecasters do something different: they follow a disciplined process, combine perspectives across a team, and keep score so they actually get better over time.
Good Judgment’s CEO Dr. Warren Hatch sat down with Washington Post to discuss what that process looks like in practice, how AI fits into the forecasting workflow, and why prediction markets and Superforecaster teams approach the same problems in fundamentally different ways.
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Kalshi and Polymarket Create New Competition for Professional Economists
The New York Times (February 2026)
While exploring prediction markets, professional economists, and the future of forecasting, this NYT article says, “It may also be true that neither individual experts nor a collective of thousands are the best at predicting the future. Over the past decade, a group called Good Judgment has developed a model of selecting people with good track records of figuring out what will happen.”
Our CEO Dr. Warren Hatch discusses why Superforecasters have an edge when the data is sparse and the environment is in flux.
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What the “superforecasters” predict for major events in 2026
The World Ahead 2026, The Economist (November 2025)
Following another successful collaboration last year, Good Judgment’s Superforecasters were invited to contribute their forecasts to The Economist ’s forward-looking guide, The World Ahead 2026 . Their forecasts focus on the chance of an in-orbit fuel transfer between two Starship vehicles, the likely balance of power after the US midterms, US tariffs, the Russia-Ukraine conflict, and even a Nobel Peace Prize winner.
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Publication Downloads
“Superforecasters: A Decade of Stochastic Dominance,” C. Karvetski (2021)
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“Assessing the Accuracy of Geopolitical Forecasts from the US Intelligence Community’s Prediction Market,” S. Goldstein et al. (2015)
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“Forecasters Who Think Again Are More Accurate,” C. Karvetski (2022)
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“Superforecasting the Fed’s Target Range,” C. Karvetski (2023)
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