The Signal and the Noise: The Art and Science of Prediction Paperback – 18 Apr 2013
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Outstanding... I was hooked (Tim Harford Financial Times)
One of the more momentous books of the decade (The New York Times Book Review)
A lucid explanation of how to think probabilistically (Guardian)
The inhabitants of Westminster are speed-reading The Signal and the Noise... They will find the book remarkable and rewarding (Sunday Telegraph)
Is there anything now that Nate Silver could tell us that we wouldn't believe? (Jonathan Freedland)
Fascinating... our age's Brunel (Bryan Appleyard Sunday Times)
A surprisingly accessible peek into the world of mathematical probability (Daily Telegraph)
The Galileo of number crunchers (Independent)
A 34-year old Delphic Oracle (Daily Beast)
About the Author
Nate Silver is a statistician and political forecaster at The New York Times. In 2012, he correctly predicted the outcome of 50 out of 50 states during the US presidential election, trumping the professional pollsters and pundits. He was named one of TIME's 100 Most Influential People in the world, and one of Rolling Stones' top Agents of Change. He lives in Brooklyn, New York.
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The Nobel prize-winning physicist Niels Bohr famously said 'Prediction is very difficult, especially if it's about the future'. This pretty much sums up the first half of the book. Yes, the detail about the financial crisis, weather forecasting, earthquakes etc is mildly interesting, but in relation to prediction, you will be wading through a lot of noise to extract the signal ('human nature makes us over-confident predictors', 'without either good theory or good empirical data, you may as well just guess','the most confident pundits are usually the worst' etc).
The substance of the book comes in twenty pages in the middle, where Silver introduces Bayesian logic (I learnt in maths classes at school when I was fourteen so it wasn't new to me, and it doesn't need 200 pages of build up). The best section is where Silver contrasts Bayesian logic to Fisherian logic. Fisher created the maths that is used almost universally in medical and social science research to prove the efficacy of a treatment or theory. Silver explains how flawed this maths is - which is presumably why two thirds of the positive findings claimed in medical journals cannot be replicated. This is pretty heady stuff.
Silver claims that the second half of the book is about how to make predictions better. It is mostly more examples of failure, this time in chess, investment, climate and terrorism, with a few asides that might be considered signals ('testing is good', 'groups/markets tend to make better predictions than individuals'). The exception is the section on poker, which delivers the strongest message in the book: good gamblers think in probabilities (rather than dead certs) - when these probabilities diverge from the odds on offer by a suitable margin, they may place a bet. Bad poker players lose a lot more than good poker players make. The best is the enemy of the good...
Of course, the point of the book is that there is no silver bullet - good prediction requires detail, nuance, hard work, honesty and humility. It would be wrong to expect a check list for success at the end, and naturally, there isn't one. Even so, you are left with a craving for clarity.
'The Signal and the Noise' is a pleasant enough read, but it is mostly anecdote. Rather ironically, you are left to sort out the signal from noise yourself.
My two main criticisms for non Americna readers is that it is quite US centric (I don't care about baseball, and the general moneyball story is impossible to avoid) and the main philosophical stuff (which was most useful and ineresting to me) makes up a small portion of the book, the majority with various examples where he makes the same arguments with interview of different people that are somewhat non-questioning.
He gives some useful examples throughout the book, covering meteorology, earthquakes, transmision of viruses, but it still feels as if it could have been cut. The stuff on Bayes is interetsing but really skates over the issue of how you come up with a Bayesian prior when you can't iteratively improve them because you do not have many data points. Given the time he spends looking at the financial crisis, this is a flaw as it reduces the "wow, Bayes is really useful" impact when it cannot offer that much resolution to the problem of predicting economic and financial crises, the key predictive failure he cites.
Even so, as a way of getting people to think a bit more deeply about what it is to make a prediction and how to know if it was well constructed, and how to integrate concepts of epistemology, it is a useful introductory book.
However, the chapters on weather forecasting, earth tremors, the stock exchange (I'll never think about investing the same way again!) and global warming were very interesting.
Nate makes a good argument for the perils and pitfalls of prediction. The overly optimistic forecasts are slashed down with his gentle reprimands and he explains in much detail why and how they are wrong. He also has big issues with the amount of data v.s what is relevant. It's no good having tons of information (the noise), if you don't understand what you've gathered.....to make a prediction (the signal).
His favourite form of prediction includes something called Bayesian reasoning, which is almost the basis of the whole book.
There are charts galore, lots of 'case examples' and the ins and outs of how he-got-to-where-he-is-now by actually immersing himself in his field (election & baseball forecasts) ....I was a little concerned to read about how he played on-line poker, winning thousands of $'s, then losing a big chunk in 2006/7 but with that admission, there came some humility where he describes (in great detail) not only how he won, but also WHY he lost so much....
I enjoyed the book, mostly....
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