At the end of the semester, I often recommend fun popular science books to my students about how to approach problems and make better decisions using math, operations research, and critical and quantitative reasoning. My list is growing. Here is my list in no particular order.
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Moneyball: The Art of Winning an Unfair Game by Michael Lewis. This is a great introduction to building models, collecting data, finding “bargains” in the market, drawing conclusions from the models, and differentiating between a good process and good outcomes. Everyone should read Moneyball.
Scorecasting: The Hidden Influences Behind How Sports are Played and Games are Won by Tobias Moskowitz and Jon Wertheim. This is a more mathy book than Moneyball that is hard to put down. Tobias and Jon dissect many papers that quantify sports and sports decision making. They address home field advantage, umpire bias, metrics that are not useful (such as blocked shots in basketball), and elasticity in fandom.
In Pursuit of the Traveling Salesman by Bill Cook. This is the most specialized book on the list, but do not be intimidated. It is highly accessible and is well worth your while. Bill does a wonderful job explaining optimization concepts (often using pictures) and introducing you to the people who made scientific breakthroughs. I thought that maybe one chapter might be tough for someone who is unfamiliar with optimization, but even in that chapter Bill does a superb job of stepping the reader through the steps of various algorithms. I recommend the print version for following along with the many figures and pictures in the book. Read more in my review here.
How Not to be Wrong: The Power of Mathematical Thinking by Jordan Ellenberg. This was my favorite book from 2014. This book is a joy to read by anyone who is even remotely mathematically literate. Jordan’s writing is fun to read and his examples are very relevant. I loved the parts about how not all lines are straight lines – some are curves. This book was well-reviewed by various newspapers. I like Scientific American blogger Evelyn Lamb’s more mathy review.
The Signal and the Noise: Why So Many Predictions Fail — But Some Don’t by Nate Silver. This is a popular science book about prediction and science communication. There are many good takeaways about election forecasting, accuracy of weather predictions, Sabermetrics (Moneyball!), and online poker. It’s a fun read, but I found the mathy parts to be somewhat shallow. I prefer How Not to be Wrong, where Jordan did a better job digging into the math while also remaining accessible.
The Black Swan: The Impact of the Highly Improbable by Nassim Nicholas Taleb. I’ll be honest, I have a love hate relationship with this one (my review simply stated “it was OK”) but it’s worth a read. The discussion of extreme risks is very good, but Taleb is too critical of other people’s models. He loses sight of the fact that all models are wrong, but some are useful.
Traffic: Why We Drive the Way We Do (and What It Says About Us) by Tom Vanderbilt. This is a delightful book about networks and the psychology of driving that I suspect will appeal to my blog readers. Tom is a journalist, and is really good at writing about science. I reviewed the book here and have another post here. Traffic contains many interesting tidbits of knowledge that make for good chit chat during awkward party conversations (I’m not always the life of the party, but Traffic helps!).
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I’m probably leaving something out. What are your favorite popular science/math/operations research books?
Punk Rock Operations Research 
December 10th, 2014 at 8:51 am
I’ve just finished “How not to be wrong”, it is indeed a great read striking a fine balance between details and big picture.
Two other related books I got recently:
Dataclysm: Who We Are (When We Think No One’s Looking)
by Christian Rudder
What If?: Serious Scientific Answers to Absurd Hypothetical Questions Hardcover by Randall Munroe
I read the second one, it is of course on the lighter side of science, but a fun read nonetheless.
December 10th, 2014 at 9:37 am
I would recommend the book Predictive Analytics from Eric Siegel. Nice overview about the topic and a good starting point for further researche.
December 10th, 2014 at 10:17 am
The Drunkard’s Walk by Leonard Mlodinow – some nice probability anecdotes
December 10th, 2014 at 11:33 am
The best book like these that I read this year was ‘how to predict the unpredictable’ by poundstone http://www.theguardian.com/books/2014/sep/17/how-to-predict-the-unpredictable-william-poundstone-review
Happiness by design by Dolan I also really enjoyed
December 10th, 2014 at 4:46 pm
I started reading The Theory That Would Not Die by Sharon McGrayne, a book about the history of Bayes’ rule. So far it has been quite enjoyable as it includes cases where the theorem has been applied and it also contains a brief history of the controversy surrounding Bayesian stats.
December 10th, 2014 at 5:08 pm
Poundstone has some good older ones on voting and Kelly criterion too. “More than you know” by Mauboussin is another older (last 10 years) title with a bunch (40+) of short but sweet chapters. Simon Singh had good one last year on Simpsons math.
December 11th, 2014 at 4:29 pm
Sue ‘The Theory That Would Not Die’ has the peculiar distinction of being the worst popular science book I have ever finished
January 6th, 2015 at 11:52 am
It isn’t mathy, but there’s a lot of operations research beneath the surface of “The Box: How the Shipping Container Made the World Smaller and the World Economy Bigger” by Marc Levinson
December 16th, 2016 at 8:58 pm
1. John A. Adam. “Guestimation”, “Guestimation 2.0”, “A Mathematical Nature Walk.” His altest book discusses mathematical models of urban issues, “X and the City”
2. Steven Vogel has several books on scaling issues in mathematical biology.
3. Phillip Ball has a trilogy of books, “Branches”, “Flow”, and “Shapes.”