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Books on Making Better Decisions - Good Judgment Inc.
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Good(3)Good quality. Reputable source with community review or editorial standards, but less rigorous than peer-reviewed venues.
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Good Judgment Inc. is the organization that ran the Good Judgment Project, a landmark forecasting tournament; their recommended reading is relevant to AI safety researchers interested in forecasting AI risks and improving epistemic practices.
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Importance: 35/100blog postreference
Summary
Good Judgment Inc. curates a reading list of books focused on improving forecasting, probabilistic thinking, and decision-making under uncertainty. The list draws on expertise from the Superforecasting research tradition and highlights resources relevant to calibrated reasoning and judgment. It serves as a practical guide for those seeking to improve their epistemic practices.
Key Points
- •Curated booklist from Good Judgment Inc., the organization behind Superforecasting and the Good Judgment Project
- •Focuses on probabilistic thinking, calibration, and reducing cognitive biases in decision-making
- •Relevant to AI safety practitioners who need strong forecasting and epistemic hygiene skills
- •Draws from behavioral economics, statistics, and judgment research traditions
- •Supports the broader goal of making better predictions about complex, uncertain outcomes including AI risks
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Good Judgment (Forecasting) | Organization | 50.0 |
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[Uncategorized](https://goodjudgment.com/category/uncategorized/ "Go to the Uncategorized category archives.") \> Books on Making Better Decisions
# Books on Making Better Decisions: Good Judgment’s Back-to-School Edition
Since the publication of Tetlock and Gardner’s seminal _Superforecasting: The Art and Science of Prediction_, many books and articles have been written about the ground-breaking findings of the Good Judgment Project, its corporate successor Good Judgment Inc, and the Superforecasters.
This is not surprising: Decision-makers have a lot to learn from the Superforecasters. Thanks to being actively open-minded and unafraid to rethink their conclusions, the Superforecasters have been able to make accurate predictions where experts often failed. They know how to think in probabilities (or “in bets”), reduce the noise in their judgments, and mitigate cognitive biases such as overconfidence. As Tetlock and Good Judgment Inc have shown, these are skills that can be [learned](https://goodjudgment.com/services/upcoming-workshops/).
Here is a short list of eight notable books that present a wealth of information on ways to evaluate an uncertain future and improve decision-making.
- **_[Superforecasting: The Art and Science of Prediction](https://www.goodreads.com/book/show/23995360-superforecasting)_** by Philip E. Tetlock and Dan Gardner (2015)
In 2011, IARPA—the research arm of the US intelligence community—launched a massive competition to identify cutting-edge methods to forecast geopolitical events. Four years, 500 questions, and over a million forecasts later, the Good Judgment Project (GJP)—led by Philip Tetlock and Barbara Mellers at the University of Pennsylvania—emerged as the undisputed victor in the tournament. GJP’s forecasts were so accurate that they even outperformed those of intelligence analysts with access to classified data. One of the biggest discoveries of GJP were the Superforecasters: GJP research found compelling evidence that some people are exceptionally skilled at assigning realistic probabilities to possible outcomes—even on topics outside their primary subject-matter training.
In their _New York Times_ bestseller, _Superforecasting_, our cofounder Philip Tetlock and his colleague Dan Gardner profile several of these talented forecasters, describing the attributes they share, including open-minded thinking, and argue that forecasting is a skill to be cultivated, rather than an inborn aptitude.
- **_[Noise: A Flaw in Human Judgment](https://www.goodreads.com/book/show/55339408-noise)_** by Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein (2021)
Noise, defined as unwanted variability in judgments, can be corrosive to decision-making. Yet, unlike its better-known companion, bias, it often remains undetected—and therefore unmitigated—in decision processes. In addition to research-based insights into bet
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