“This book
is less about what we know than about the difference between what we know and
what we think we know”, writes Nate Silver somewhere at the end of his book. He
could not have phrased it more beautifully. Our brain is a remarkable
instrument that, when it comes down to predicting almost anything, contains a
substantial amount of techniques to mislead us. Of course, these techniques
often helped us to survive in tougher times, but they fail us miserably in the
area of future-watching. Just as an example: apparently we have a bad habit of
neglecting certain risks just because they’re too hard to measure…. Not sure
how far this will get us in terms of survival, however…
The solution? Statistics. According to Nate Silver, that is. He’s not saying statistics can
predict everything (or, for that matter, anything) –‘all statistic models are
faulty, but some are useful’- but at least it can provide us with a more
accurate picture of the (possible) future(s).
Chapter by
chapter Nate applies this thought on very specific subjects, ranging from the
American elections (his specialty), to weather forecasting, earthquakes,
economic growth and climate change, to more tangible topics such as poker,
chess, or beating the stock market (which, he concludes, is impossible).
Fascinating
material, and some conclusions linger on for quite some time. For instance: the
level of uncertainty of prediction increases when the system we study is
non-linear and dynamic (which sounds intuitive at first, but is very often
forgotten, for instance when governments predict next year’s GDP), or when the
data we start with is not accurate enough (like with weather forecasting). We
also need to watch out not to ‘overfit’ our models (adapting it with the data
we have at hand), as we often do with predicting earthquakes. Sometimes we can
use models developed for one system onto another system, like applying the
power-law distribution we use for earthquakes onto the event of future
terrorist attacks (which results in a somewhat bewildering conclusion).
The central
thought Nate Silver uses across his book is the formula of Bayes which, for the
curious people among you, looks like this:
[xy] / [xy + z(1-x)]
Where
X = the
initial likeliness you would attribute to an event.
Y = the ‘positive
possibility’ (the likeliness of an event without taking x into account)
Z = the ‘negative
possibility’ (the likeliness of the event not taking place).
Believe it
or not, but with this formula you can even calculate the likeliness that your partner
is cheating on you! The key figure in the formula is the ‘x’, the likeliness you
grant to an event accuring –which implies that the overall likeliness gets more
accurate as you gather more experience or knowledge about the event (if your
partner cheated on you in the past, the ‘x’ would increase, and hence the
overall likeliness in a particular event as well).
Strangely
enough this thought process can be applied to virtually any system (or
prediction), though dependent on a number of factors it can produce totally
different outcomes, with different levels of accuracy. The world will never be
predictable, but with a sensible use of statistics it can become a little more
so…
Fascinating
reading…
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