forecasting

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Superforecasting with calibrated reasoning, Brier score tracking, and prediction ledger management

AI & Automation 77 stars 13 forks Updated today MIT

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Skill Content

# Forecasting ## Purpose Make specific, falsifiable predictions with calibrated confidence levels. Track accuracy over time using Brier scores. Apply superforecasting methodology (Tetlock/Good Judgment Project) to any domain — technology trends, project outcomes, market shifts, competitive moves, risk assessment. ## When to Use - User asks for a prediction or forecast on any topic. - Strategic reflection identifies a decision that depends on uncertain futures. - Surplus compute is available and a prediction review is due. - A previously made prediction is approaching its resolution date. - Deep reflection surfaces a trend worth formally tracking. ## Superforecasting Principles 1. **Triage** — Focus on questions where effort improves accuracy. Ignore questions that are either trivially knowable or fundamentally unknowable. 2. **Fermi decomposition** — Break big questions into smaller, estimable components. "Will X happen?" → "What's the base rate? What's different this time? What signals would I expect to see?" 3. **Balance inside and outside views** — Start with the reference class (base rate from historical analogues), then adjust with specific evidence. Never skip the outside view. 4. **Update incrementally** — Bayesian updating. New evidence shifts confidence by small amounts, not dramatic swings. Avoid overreaction. 5. **Calibration over precision** — A well-calibrated 60% is better than an overconfident 90%. Your 70% predictions should come tru...

Details

Author
WingedGuardian
Repository
WingedGuardian/GENesis-AGI
Created
2 months ago
Last Updated
today
Language
Python
License
MIT

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