After realizing how few book I read last year, I’ve decided to make a conscious effort to read more. As a way to help me keep it up I’ve decided it would be fun to add “reviews” for each of the books I’ve read. Of course, I’m bad at reviews so I’m taking a slightly different approach, giving a brief summary and then describing the crazy ideas I had while reading the book.
Summary: Suppose I told you that I ran a study where I had experts and monkeys try to predict the price of a stock a month in the future. My findings where that monkeys were no better than experts. What would you say? Would you give up any idea of predicting the price? What if I then told you that there was a group in my study, monkeys raised on vegetarian diets, who did perform better than the experts and overall proportion of experts? Would this change your mind about the predictability of stock priced? What if I had told you instead that the subgroup that outperformed were had instead of being vegetarian monkeys, they were Harvard educated monkeys?
Superforecasting: The Art and Sciences of Prediction by Philip E. Tetlock and Dan Gardner considers these questions for expert predicting world events. Previous research by these and other researchers had showed that self described experts were no better, on average, to other groups of people. But this of course does not mean that there aren’t subgroups of people in these studies who might actually be reproducibly good at predictions. Superforecasting is the story of such a group.
Rating: Worth the time! Assuming you buy the methodology, some ideas for the characteristics of becoming a better predictor.
Speculation: In 8th grade I had a teacher who, like Babe Ruth point his bat to the bleachers, predicted both the fall of the Berlin Wall and the Tienanmen square revolt. Imagine you had the ability to predict macro political changes, but instead of like my teacher, the predictions pointed to a world you did not want to see. How could your psyche with stand this? What is the psychological toll of seeing the future but being unable to impact it enough?
by Elinor Ostrom – Best non-fiction I read this year. Wonkish.
by Pedro Domingos – Not quite a popularization, and not enough detail for the technical minded, but a good look into the views of a leading machine learner.
by Michelle Alexander – I tend to avoid political books that I agree with, but this one really drives home how large a problem the U.S. faces in dealing with the mass incarceration of black Americans.
by Jon Gertner – hit or miss, not enough analysis of what made Bell labs special for my liking
by Sam Quinones – Good journalism.
by Joshua Foer – Mostly focuses on the political story, not enough on the data side of the world.
by Neal Stephenson –Three books in one, first book is Stephenson in top form.
by Ramez Naam – Conclusion to the Nexus trilogy, whole trilogy is worth reading.
by Cixin Liu, translated by Ken Liu –A slow unraveling of a mysterious nearby world. Actually think the next book (below) is slightly better.
by Cixin Liu, translated by Joel Martinsen – Second in series, am eagerly awaiting translation of third.
by Michael JC Brown Virtualization to the extreme.
by Stephen Moss Conclusion to the fear saga, felt it was not up to the standards of the first book.
by James S.A. Corey – Book 5 of the Expanse. Split story line and no real clean finish, but excellent nonetheless.
by James S.A. Corey – Amos backstory.
by James S.A. Corey
by James S.A. Corey
by James S.A. Corey
by Hugh Howey
by Hugh Howey
by Hugh Howey
by Hugh Howey
by Hugh Howey
by James R Wells
by Michael Glaviano
by Anthony J Melchiorri
by David Simpson
by Joshua Dalzelle
by Peter Watts