Tetlock: Superforecasting
Summary
Philip Tetlock's research on superforecasting demonstrates how careful probabilistic thinking and systematic approaches can significantly enhance forecasting accuracy in uncertain domains like epidemiology.
Review
Tetlock's work on superforecasting provides a groundbreaking approach to improving predictive accuracy by emphasizing disciplined, probabilistic reasoning over traditional expert intuition. By studying individuals who consistently outperform expectations in forecasting complex events, he reveals that effective prediction requires breaking down complex problems, updating beliefs based on new evidence, and avoiding cognitive biases. The methodology centers on training forecasters to think in probabilities, actively update their views, and maintain intellectual humility. While the approach has shown remarkable success in geopolitical and economic predictions, its application to emerging domains like pandemic forecasting demonstrates its potential for addressing high-stakes uncertainty. However, the method is not without limitations, as it requires significant cognitive effort, ongoing training, and may not always capture black swan events or fundamental paradigm shifts.
Key Points
- Probabilistic thinking is more accurate than binary or expert-driven predictions
- Continuous belief updating and intellectual humility are critical for good forecasting
- Systematic approaches can significantly improve predictive accuracy across complex domains