Cover of Superforecasting: The Art and Science of Prediction

Superforecasting: The Art and Science of Prediction

Philip E. Tetlock; Dan Gardner

I keep this at #56 because the strongest practical treatment of calibrated judgment, forecasting, and learning from prediction errors. I have not finished learning what it asks of me.

The long version

Tetlock and Gardner examine the habits of people who make unusually accurate, well-calibrated forecasts. Superforecasters break questions into parts, combine base rates with case-specific evidence, express uncertainty numerically, update frequently, and avoid turning a prediction into a defense of identity or expertise. It reminds me that how a decision becomes an offer, a system, or a life with consequences does not happen only to other people.

Why it is here

It offers a measurable discipline for judgment under uncertainty. Instead of admiring confident punditry, you learn to formulate resolvable questions, keep score, update incrementally, and discover whether your confidence deserves trust. The test is whether it changes what I notice after I close the book.

How to read it

Join or create a forecasting log. Make ten time-bounded predictions with probabilities, identify base rates, and schedule updates. Score outcomes with a Brier score and write a short postmortem that separates bad reasoning from bad luck. I will keep a note of the sentences that make me want to look away.

What it taught me

  1. 01

    Good forecasts combine outside-view base rates with inside-view details.

  2. 02

    Calibration and resolution can be scored rather than judged by rhetorical confidence.

  3. 03

    Frequent small updates and diverse teams often outperform grand theories.

Before and after

After

Where it leads

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Expert Political Judgment

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The Signal and the Noise

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More about the author

Philip E. Tetlock is a political psychologist known for long-running research on expert prediction and forecasting tournaments. Dan Gardner is a journalist and author who specializes in risk, uncertainty, and decision-making.

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