Ah, at last we come to the end. This chapter is about the knowns, the known unknowns and the unknown unknowns. While that’s a somewhat hard to follow sentence, it does make for a nice contingency matrix, and is really the crux of the Black Swan argument. Here’s my take:
One of the most dangerous mistakes you can make is to confuse unfamiliar and improbable. What you don’t know CAN hurt you.
This chapter had some great stuff about climate change and models of climate change. One of the more interesting parts (to me) was the review of the motivations of various countries when it comes to climate change treaties. Not every country arrives at the table on the same page, no matter what their leaders believe:
Chapter 11 has a lot on bubbles and why they develop. Interestingly, Silver actually uses the whole “two ways to be wrong” thing directly, in order to point out that a trader who loses money in one way (selling only to have the market go up) is much more likely to be penalized than a trader who loses money with everyone else (buying only to have the market crash). This is why traders are so hesitant to acknowledge a bubble….they know that going against the crowd will get them far more penalized than making the same mistake as everyone else. Explains a lot, if you think about it.
Chapter 10 was about poker, and how to make money playing poker. Apparently the key is to make it easy for lots of inexperienced people to play. When websites that made it easy to play got shut down, fewer inexperienced people made the effort and many previously “successful” players discovered they were now the fish. It’s a good reminder to keep an eye on the skill level of your competition in addition to your own.
Chapter 9 has some interesting anecdotes about the quest to create a chess program that could beat Gary Kasparov. It covers some of the limits of humans and machines, and how they are almost better when used in tandem.
The eternal struggle continues.
Chapter 7 was about extrapolation and predictions that influence their own accuracy. One of my favorite examples was predictions about disease spread: