Recently Read Books 2014

I thought I would take a minute and let you know the books that I have read thus far in 2014. Each of these is excellent in its own way and I don’t know about you but I try to put some thought into each selection since I am making an investment of my time. I have read The Black Swan and The Signal And The Noise twice and I really believe I need to read The Black Swan a third time to get a complete understanding.

(1) The Black Swan     Nassim Nicholas Taleb

(2) The Smartest Kids In The World     Amanda Ripley

(3) The Signal And The Noise     Nate Silver

(4) The Sixth Extinction     Elizabeth Kolbert

(5) The Sports Gene     David Epstein

(6) Average Is Over    Tyler Cowen


Some Finishing Thoughts From The Signal And The Noise By Nate Silver

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These are some quotes from the book which I wanted to share in which I feel are important for each of us who make prediction or forecast to explore:

From page 183:

“Getting feedback about how well our predictions have done is one way—perhaps the essential way—to improve them. Economic forecasters get more feedback that people in most other professions, but they haven’t chosen to correct for their bias toward overconfidence.”

From page 191:

“A forecaster should almost never ignore data, especially when she is studying rare events like recessions or presidential elections, about which there isn’t very much data to begin with. Ignoring data is often a tip-off that the forecaster is overconfident, or is overfitting her model—that she is interested in showing off rather than trying to be accurate.”

I encourage our readers to tackle chapter 8 entitled Less And Less And Less Wrong which gives a great introduction and analysis of Baye’s Theorem and how to use it in everyday analysis for all types of disciplines.

From page 267:

“The need for prediction arises not necessarily because the world itself is uncertain, but because understanding it fully is beyond our capacity.”

From page 292:

“Nevertheless, a commitment to testing ourselves—actually seeing how well our predictions work in the real world rather than in the comfort of a statistical model—is probably the best way to accelerate the learning process.”

From page 307:

“As Arthur Conan Doyle once said, “Once you eliminate the impossible, whatever remains, no matter how improbable, must be the truth.” This is sound logic, but we have a lot of trouble distinguishing the impossible from the highly improbable and sometimes get in trouble when we try to make too fine a distinction.”

From page 388:

“The goal of any predictive model is to capture as much signal as possible and as little noise as possible. Striking the right balance is not always so easy, and our ability to do so will be dictated by the strength of the theory and the quality and quantity of the data. In economic forecasting, the data is very poor and theory is weak, hence Armstrong’s argument that “the more complex you make the model the worse the forecast gets.”

From page 448:

“This book is less about what we know than about the difference between what we know and what we think we know. It recommends a strategy so that we might close that gap. The strategy requires one giant leap and then some small steps forward. The leap is into the Bayesian way of thinking about prediction and probability.”

These are just several thoughts in which are readers may digest, but I would suggest whole-heartily that you read it inspectionally and then turn around and read it analytically.

The Fourth Installment Of The Signal And The Noise By Nate Silver

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I wanted to share some information from the chapter entitled Desperately Seeking Signal on page 172 as it speaks on the predictability of earthquakes. I love this excerpt:

“Even if we had a thousand years of reliable seismological records, however, it might be that we would not get all that far. It may be that there are intrinsic limits on the predictability of earthquakes.

Earthquakes may be an inherently complex process. The theory of complexity that the late physicist Per Bak and others developed is different from chaos theory, although the two are often lumped together. Instead, the theory suggests that very simple things can behave in strange and mysterious ways when they interact with one another.

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Bak’s favorite example was that of a sandpile on a beach. If you drop another grain of sand onto the pile (what could be simpler than a grain of sand?), it can actually do one of three things. Depending on the shape and size of the pile, it might stay more or less where it lands, or it might cascade gently down the small hill toward the bottom of the pile. Or it might do something else: if the pile is too steep, it could destabilize the entire system and trigger a sand avalanche. Complex systems seem to have this property, with large periods of apparent stasis marked by sudden and catastrophic failures. These processes may not literally be random, but they are so irreducibly complex (right down to the last grain of sand) that it just won’t be possible to predict them beyond a certain level.”

I find this very fascinating in attempting to uncover where the next Black Swan may be hiding. I know that it is very difficult to see but I still find understanding complex adaptive systems to be most interesting.

Key To Forecasting From Nate Silver In The Signal And The Noise

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The author in the chapter on baseball makes the point of what is the key to forecasting. On page 100, this is what he says:

“The key to making a good forecast, as we observed in chapter 2, is not in limiting yourself to quantitative information. Rather, it’s having a good process for weighing the information appropriately. This is the essence of Beane’s (The Oakland Athletics General Manager) philosophy: collect as much information as possible, but then be as rigorous and disciplined as possible when analyzing it.”

This immediately made me go back and think about process vs. outcome which Michael Mauboussin really stresses. I am working on starting a new business and I must remember to focus on being rigorous and disciplined in my decision making. I plan on going back to Mauboussin and reading to make sure I haven’t missed anything.

Question To Ponder From The Signal And The Noise By Nate Silver

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I am about halfway through analytically rereading The Signal And The Noise and I very impressed how the author has presented his information thus far. However, I am still misled somewhat because I have not gotten to the point of understanding the signal from the noise in forecasting. So the question is: How do I differentiate the signal from the noise?

Currently Analytically Rereading: The Signal And The Noise By Nate Silver

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I ran across this on page 53 and felt like it would be on interest to our readers. The discussion here is in having the right attitude for making predictions and that we should be a fox instead of a hedgehog. First, today we will share some of the characteristics of being a fox and being a hedgehog. Below is from Figure 2-2 from page 54.

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One of the interesting thoughts I had when I read this was if I wanted to become a better forecaster and I was a hedgehog, what would be required to make myself into a fox? It seems there would need to be quite a bit of change in a person’s personality and mental make-up. Am I thinking about this correctly? Any thoughts from our readers?

Risk And Uncertainty From The Signal And The Noise By Nate Silver

English: Nate Silver in Washington, D.C.

English: Nate Silver in Washington, D.C. (Photo credit: Wikipedia)

I was sitting at my desk last evening and picked up The Signal And The Noise by Nate Silver and on page 29 ran across the best comparison between risk and uncertainty. I have never heard it explained better.

Risk, as first articulated by the economist Frank H. Knight in 1921, is something that you can put a price on. Say that you’ll win a poker have unless your opponent draws to an inside straight: the chances of that happening are exactly 1 chance in 11. This is risk. It is not pleasant when you take a “bad beat” in poker, but at least you know the odds of it and can account for it ahead of time. In the long run, you’ll make a profit from your opponents making desperate draws with insufficient odds.

Uncertainty, on the other hand, is risk that is hard to measure. You might have some vague awareness of the demons lurking out there. You might even be acutely concerned about them. But you have no real idea how many of them are or when they might strike. Your back-of-the-envelope estimate might be off by a factor of 100 or by a factor or 1,000; there is no good way to know. This is uncertainty. Risk greases the wheels of a free-market economy; uncertainty grinds them to a halt.”

I know in my life, I certainly need to apply these principles in my decision-making process in business decisions and as well as using tax payer dollars in county decisions.