The Signal and the Noise: The Art and Science of Prediction
By Nate Silver (2013)
The most calamitous failures of prediction usually have a lot in common. We focus on those signals that tell a story about the world as we would like it to be, not how it really is. We ignore the risks that are hardest to measure, even when they pose the greatest threats to our well-being. We make approximations and assumptions about the world that are much cruder than we realize. We abhor uncertainty, even when it is an irreducible part of the problem we are trying to solve.
My Notes
We can never make perfectly objective predictions.
We must become more comfortable with probability and uncertainty.
- Known knowns: an accurate answer can be given
- Known unknowns: verbalised/thought but likelihood not known
- Unknown unknowns: not verbalised/thought
Systems:
… everything affects everything else and the systems are perpetually in motion.
… cause and effect are all blurred together…
… a sort of purgatory wherein it is not quite random but also not quite predictable.
Distributions:
There’s a much larger difference between the very worst players and the average ones than between the average ones and the best.
Biases, mistakes:
- Paying more attention to the information we agree with
- Detecting a “signal” that isn’t really there (overfitting)
- Overconfidence
- When we have strong priors, our beliefs can be very resilient
Evaluate decisions on the process, not the end result:
Play well and win; play well and lose; play badly and lose; play badly and win: every poker player has experienced each of these conditions so many times over that they know there is a difference between process and results.