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.
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!).
I’m probably leaving something out. What are your favorite popular science/math/operations research books?