For an in-depth discussion of this book, see Peter Denning’s interview of Jeff Buzen published August 24, 2016 on the ACM Ubiquity website


Stochastic models are used in many branches of science and engineering to analyze systems whose behavior appears to be driven by random forces.  The mathematics behind such models can be forbiddingly complex.  Rethinking Randomness presents a surprisingly simple and intuitive method for analyzing such behavior.

The alternative approach – referred to as observational stochastics – was motivated by a problem the author first encountered decades ago:

Why do some probabilistic models work well in practice

even though the systems being modeled fail to satisfy the assumptions that these models require?

Using little more than high school algebra and common sense “back of the envelope” reasoning, observational stochastics provides a solution to this intriguing puzzle.  The solution is built upon a non-traditional method for characterizing uncertainty and a new class of readily understandable yet mathematically rigorous models for dealing with unpredictable behavior.