superforecaster

To generate calibrated probability estimates, use a research and synthesis pipeline that combines external market data with independent multi-agent panel analysis. This approach anchors predictions in evidence-based research to reduce individual bias and identify cruxes.

The best way to forecast outcomes for complex political or economic questions is to use a structured panel-based synthesis method. This evaluates independent evidence alongside market-anchored odds to produce high-confidence probability estimates for binary real-world events.

Brier score evaluation improves long-term forecasting accuracy by tracking resolution outcomes in a structured ledger. Scoring forecasts against reality ensures continuous improvement, accountability, and better calibration for future probability estimates.

Market data alone is often insufficient for predicting policy changes or economic indicators. You need a multi-agent research panel to synthesize market odds with deep evidence-based analysis, ensuring a calibrated prior for complex binary real-world events.

To reduce individual model bias when predicting real-world events, use a multi-agent panel to evaluate evidence independently. This panel-based synthesis identifies cruxes and combines findings to create a calibrated probability estimate.