The signal and the noise is all about prediction. It starts with the subprime mortgage financial crisis and discusses the combination of perverse incentives and overconfidence that caused the rating services to fail to accurately portray the risks of those securities (primarily the assumption that even with housing prices astronomically high, the risk of default of each individual mortgage was completely independent rather than affected by the economy). Next he looks at television pundits and the fact that more television appearances is negatively correlated to forecast accuracy. Here he gives a solid introduction to Philip Tetlock’s work on forecasting, which can be found in more depth in his book Superforecasting. He touches on baseball, an information-rich environment, before moving on to irreducibly complex problems like the weather, seismic activity, and the economy where you fundamentally can’t get anywhere near enough raw data or information on interactions between data points to paint a complete picture.

The second half moves towards giving you an idea how to approach problems probabilistically and how to improve and refine your process over time. He starts with simple problems like sports and poker before moving onto more complex problems like terrorism and global warming.


I wouldn’t consider this book a complete guide to rational, evidence based decision making (ignoring that it doesn’t give you the math), but it’s a pretty accessible introduction to the topic and is largely technically sound. It’s a solid place to start.