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Automated forecasting works by collecting signals from sources like financial reports and building logical reasoning chains that consider base rates and evidence to generate calibrated predictions with accuracy tracking.

Yes, you can generate predictions for domains including technology, finance, geopolitics, and climate by configuring the forecasting engine to focus on a specific area and time horizon such as a 3-month outlook.

You track the accuracy of predictions by scoring historical outcomes against the initial forecasts, providing calibration insights to measure whether the predicted probabilities reliably match the actual frequency of events.

The best way to mitigate bias in prediction models is by enforcing structured reasoning throughout the forecasting process, ensuring that logical arguments weigh base rates and collected signals objectively rather than relying on intuition.

Superforecasting utilizes tools for web search, memory storage, and knowledge graph manipulation to aggregate diverse signals, which supports the structured reasoning necessary for reliable forecasting.