From the library
Superforecasting
Philip E. Tetlock, Dan Gardner, 2015
In one paragraph
Tetlock's tournament findings on who forecasts well and why. The contribution is that accuracy is trainable: granular probabilities, frequent small updates, breaking questions into parts, and keeping score. It is the empirical answer to the pessimism of the bias literature. Most useful if you make predictions anyone later checks. Least useful for questions that cannot be scored, which is where most consequential judgement actually lives.
Where this sits in the operating system
Systems Over Goals
Continuous Improvement & Feedback Loops
Mental Models & Decision Frameworks
Systems Over Goals
Key insights
Accuracy is trainable. Granular probabilities, frequent small updates, and keeping score are what separate the top forecasters.
Break a question into parts, estimate each, recombine. Most bad forecasts are one undivided guess.
The empirical answer to the bias literature's pessimism: judgement does improve when it is measured.
Recognise it early
The failure and the correction sit side by side deliberately. Read the left column asking whether any of it is already true of you.
Muddy, unscorable questions
No base rates used
Stale forecasts
Groupthink
No scoring/learning
The same idea, argued differently
Brian Christian, Tom Griffiths
Peter Drucker
Gene Kim, Kevin Behr, George Spafford
Ryan Deiss
The full breakdown
Before you close this
Naming the specific moment you will act roughly doubles the odds you do.
Reference
- Author
- Philip E. Tetlock, Dan Gardner
- Published
- 2015
- Category
- Business
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