Review: Superforecasting

This is a review of Superforecasting: The Art and Science of Prediction by Philip Tetlock and Dan Gardner.

Although “Destined to become a modern classic” from the book jacket description might be a bit overstating the book’s impact, I did enjoy the reading it and came away inspired and energized. The style is approachable and authentic.

Beliefs are hypotheses to be tested—not treasures to be guarded.

In this book we learn who superforecasters are, why they are good at what they do, and how anyone can mimic their approach to improve their thinking.

81eccldB55LThe future, in the near term, can be predicted. This skill can be learned, practiced, and improved—and some people are much better at it than others.

Superforecasters are smart, but not genius-level and are comfortable with numbers and statistics. They live in perpetual beta. They exercise caution, nuance, and healthy skepticism while developing techniques and habits of mind to bring smart thinking for the future (and now). They are constantly belief-updating and fact-checking. In short, they are teachable.

They have:

1. A healthy appetite for information
2. A willingness to revisit and revise when new information arises
3. An ability to synthesize material from very different sources
4. An ability to think in fine gradations
5. A growth mindset: determination, self-reflection, and willingness to learn from mistakes
6. Awareness of their biases
7. Grit

Their methods:

1. Gather evidence from a variety of sources
2. Think probabilistically
3. Work in teams
4. Keep score
5. Be willing to admit error and change course

One favorite thread of mine in this book was how it addressed other impactful books like Thinking Fast and Slow from Daniel Kahneman and Black Swan and Antifragile by Nassim Taleb. As another book about meta-cognition (thinking about thinking) the authors weave elements of the other works into this one while both agreeing and disagreeing with their philosophies and techniques. The book feels pragmatic and up-to-date.

For example, Taleb’s black swans are unimaginable and impactful. In that view, forecasting will only interest short-term thinkers because it can’t predict black swans. However, the authors of Superforecasting argue—and I agree—that incremental change can be profoundly impactful. One style risks a lot for a rare huge win while the other pays off slowly, modestly, and more often.

Takeaway lessons for my work include: 1) think clearly, not too fast, and do the needed research 2) be willing to adjust and learn from evidence and new information 3) models are valuable even if not 100% accurate—they are simplified in order to explain and predict, and 4) keep going, keep learning.