Olivier Sibony

This book has some merits, but being interesting isn't one of them. It is repetitive and filled with statistical discussions. I love, absolutely love, statistics, but there are ways to discuss them that isn't just plain boring. Also, some of the statistical data they presented seem to support their conclusion, but...and this is a big but...the effect was small enough that it likely didn't meet the criterion of being important. Significance isn't enough; is the difference big enough to cover the deviation and the overlap? And even if it is, does it matter? If I'd finished the book, perhaps they'd have convinced me it did, but I couldn't slog through any more of it, even though their major premise is accurate. The world does have a lot of noise in our judgement, causing one person to judge vastly different than another, and even the same person to vary depending on the environment. I'm not sure AI is the answer, though, even though they are enthusiastic. The biases that develop quickly in AI seem to make that a risky proposition. Overall, I don't recommend it.… (mere)

I found the first 200 pages of this book to be almost impenetrable and frequently forgot a sentence shortly after reading it.

That said, the book and its import improve.

If you’ve read Kahneman’s earlier work, Thinking Fast and Slow, you’ll be familiar with the use of a core metaphor to the argument. While the book says it’s about “Noise” it’s really about the statistical sources of bad judgments.

Noise is the shorthand systems engineers use to explain flaws in the system.

Kahneman et al want us to take a systems view of bad judgments, and bad judges. There is hope for them yet.

Forestalling judgment until the evidence is collected, breaking down complex judgments to their constituent parts, employing baseline comparisons, and employing objective referees will all yield better judgments in business, in law and medicine, and in life.

I certainly hope so. I have trouble just dealing with the volume of judgments I am called upon to make everyday in business.

There is a lot here to think about, especially about the people who are the experts we rely upon, and how they frequently get important things wrong.… (mere)