forecasting
SolidSuperforecasting with calibrated reasoning, Brier score tracking, and prediction ledger management
AI & Automation 77 stars
13 forks Updated today MIT
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Quality Score: 90/100
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# 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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