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Good Judgment Open - Forecasting Platform
webgjopen.com·gjopen.com/
Relevant to AI safety researchers interested in forecasting AI capability timelines, governance outcomes, and policy developments; the Superforecasting methodology offers lessons for structured reasoning under uncertainty applicable to long-term AI risk assessment.
Metadata
Importance: 42/100tool pagetool
Summary
Good Judgment Open is a crowd-sourced forecasting platform where participants predict geopolitical, economic, and technological events, with top performers earning the 'Superforecaster' designation. Founded by Philip Tetlock, whose research demonstrated that structured probabilistic thinking can dramatically improve prediction accuracy. The platform serves as both a competitive forecasting community and a research tool for studying human judgment under uncertainty.
Key Points
- •Crowd-sourced platform for probabilistic forecasting on real-world events, including technology and AI-related questions
- •Based on Philip Tetlock's research showing that disciplined forecasters can significantly outperform expert intuition and prediction markets
- •Top performers earn 'Superforecaster' status, validated through track record of calibrated, accurate predictions
- •Useful for AI governance and safety communities to track and forecast AI development milestones and policy outcomes
- •Provides a framework for calibrated uncertainty quantification relevant to AI risk and capability timeline assessments
Review
Good Judgment Open represents an innovative approach to predictive analytics by leveraging collective intelligence and crowd-sourced forecasting. The platform allows participants to make probabilistic predictions about complex global events across political, economic, and technological domains, with challenges sponsored by prestigious organizations like UBS Asset Management and Harvard Kennedy School. The platform's methodology is rooted in the work of Philip Tetlock, a renowned expert in forecasting who has demonstrated that carefully selected and trained individuals can consistently outperform traditional expert predictions. By creating a competitive environment where users can track their accuracy and develop their forecasting skills, Good Judgment Open contributes to understanding collective intelligence and improving predictive capabilities. While the platform offers an engaging approach to forecasting, its limitations include potential biases in participant selection and the challenge of accurately predicting complex, multi-dimensional global events.
Cited by 5 pages
| Page | Type | Quality |
|---|---|---|
| AI Risk Portfolio Analysis | Analysis | 64.0 |
| Good Judgment (Forecasting) | Organization | 50.0 |
| AI-Human Hybrid Systems | Approach | 91.0 |
| Prediction Markets (AI Forecasting) | Approach | 56.0 |
| AI System Reliability Tracking | Approach | 45.0 |
Resource ID:
ad946fbdfec12e8c | Stable ID: ZDgyNjA2Yz