LIBRARY OF CONGRESS CATALOGING IN PUBLICATION DATA. Silver, Nate. The signal and the noise: why most predictions fail but some don't / Nate Silver. The New York Times Book Review Nate Silver built an innovative system for predicting The Signal and the Noise and millions of other books are available for. Editorial Reviews. usaascvb.info Review. site Best Books of the Month, September The Signal and the Noise, Nate Silver's brilliant and elegant tour of the modern science-slash-art of forecasting, shows what happens when Big.

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PDF | On Feb 1, , Ken Simonson and others published Nate Silver, The Signal and the Noise: Why So Many Predictions Fail—But Some. Nate Silver's The Signal and the Noise: Why So Many. Predictions Fail but Some Don't. By Donna Suchy. Donna Suchy is the principal patent attorney at. During the presidential election, the forecaster Nate Silver did something that I imagine happens rarely in the world of po- litical bloggers – he became part .

This means people are starting to really care about modeling, both how it can help us remove biases to clarify reality and how it can institutionalize those same biases and go bad. The first step to get people to think critically about something is to get them to think about it at all. Moreover, the book serves as a soft introduction to some of the issues surrounding modeling. Silver has a knack for explaining things in plain English. While he only goes so far, this is reasonable considering his audience. You might think infidelity is rare, for example, but after a quick poll of your friends and a quick Google search you might have collected enough information to reexamine and revise your estimates. The bad news Having said all that, I have major problems with this book and what it claims to explain. It would be reasonable for Silver to tell us about his baseball models, which he does. It would be reasonable for him to tell us about political polling and how he uses weights on different polls to combine them to get a better overall poll. He does this as well. He also interviews a bunch of people who model in other fields, like meteorology and earthquake prediction, which is fine, albeit superficial. Let me give you some concrete examples from his book.

But their intentions are kind of irrelevant. Silver ignores politics and loves experts Silver chooses to focus on individuals working in a tight competition and their motives and individual biases, which he understands and explains well.

For him, modeling is a man versus wild type thing, working with your wits in a finite universe to win the chess game. That was gone by From my experience working first in finance at the hedge fund D.

And modelers rarely if ever consider the feedback loop and the ramifications of their predatory models on our culture.

Why do people like Nate Silver so much? To be crystal clear: my big complaint about Silver is naivete, and to a lesser extent, authority-worship. He gets well-paid for his political consulting work and speaker appearances at hedge funds like D. Silver is selling a story we all want to hear, and a story we all want to be true. From this vantage point, the happier, shorter message will win every time.

This raises a larger question: how can the public possibly sort through all the noise that celebrity-minded data people like Nate Silver hand to them on a silver platter? It would be great if substantive data scientists had a way of getting together to defend the subject against sensationalist celebrity-fueled noise. Can we get a little peer review here, people? If you see someone using a model to make predictions that directly benefit them or lose them money — like a day trader, or a chess player, or someone who literally places a bet on an outcome unless they place another hidden bet on the opposite outcome — then you can be sure they are optimizing their model for accuracy as best they can.

But if you are witnessing someone creating a model which predicts outcomes that are irrelevant to their immediate bottom-line, then you might want to look into the model yourself. An unfortunate weakness is the number of cation almost two decades ago. Experts can cellent book. It makes a solid contribution in the field now provide communities with forecasts up to 72 of prediction, fully analyzing why predictions often hours in advance of hurricane landfall and move- fail and highlighting the importance of Bayesian rea- ment, with an average miss of miles, useful soning to discipline our thinking and modeling.

The for evaluating evacuation options. Predictions made author uses a highly transparent style, illustrating his more than 20 years ago had an average miss of points with a wide array of modern and historic ex- miles and much less advance warning.

This reviewer can forecast that the book will The book provides useful lessons for practi- be valued by risk analysts based on the signals de- tioners of risk communication, consumers of risk tected from his reading. Tetlock PE. Expert Political Judgment: How Good Is It? How Can We Know? Princeton, NJ: Princeton Press, The author thanks Tony Cox, Michael Green- 4. Berlin I, Hardy H. The Hedgehog and the Fox: Princeton University Press, Katherine McComas, and Warner North for their 5.

Kahneman D.

Thinking Fast and Slow. New York: Farrar, helpful comments. Straus and Giroux, Slovic P. Perception of risk. Science, ; Tversky A, Kahenman D. Judgment under uncertainty: Heuristics and biases. Fischhoff B. Risk perception and communication unplugged: Silver N.

Why So Many Predictions Twenty years of progress. Risk Analysis, ; 15 2: Based on the evidence, we develop a theory of the coin bias.

With that theory, we can make predictions going forward as to the ratio of heads and tails. This is the fundamental idea behind the various machine-learning algorithms.

Note that inductive reasoning is the core of the scientific method Jaynes, Take climate change, for example. Based on models of atmospheric chemistry, scientists developed climate models that is, theories that use deductive reasoning to predict changes in the weather: If climate change were occurring, we would expect certain trends in global temperature readings, more variation in local temperatures, and more severe weather events.

As each of these events has occurred, they have provided evidence for climate change that raised confidence in the theory.

Actually, the same works for Newton's theory of gravity for human scale physics. The evidence for it was the precise measurements of the planetary orbits and a whole bunch of experiments. It is used now to derive how to steer probes to Mars. There is so much evidence that we simply take it as fact. Yes, gravity and climate change are both theories.

One just has more evidence than the other, so we are more confident in one than the other. The difference is a matter of degree, not kind. Summary Let's summarize all of this: First and foremost, predictions are based on understanding the likelihood of what might occur in the future.

Hence, making predictions is an exercise in applied probability. How to legitimately apply probability theory has been a source of bitter argument for centuries between the Frequentist and Bayesian schools. Today, it is widely recognized that the Frequentist approach is too limited and the Bayesian approach is more useful.

The Bayesian perspective on how to apply probability is much broader than applying Bayes' theorem, which is in fact a trivial consequence of the definition of condition probability. The Bayesian perspective allows you to reason about quantities about which you are uncertain and does not necessarily arise from repeated samples of measuring a defined population. Most modern predictive techniques, such as machine learning, are Bayesian in spirit.

The practice of prediction starts with some initial theory of how a system might perform. The theory will have some uncertain parameters that are described by random variables.

The theory could be scientific, raw expert opinion or the output of some neutral exploration of data. The prediction is some sort of measurement of the behavior of the system.

You use inductive reasoning to determine to what extent we should believe the theory. If the theory implies something that turns out to be false, then the theory is invalid and is discarded. If the theory implies a bunch of facts that are true, then we used inductive reasoning, based on conditional probabilities, to gain confidence in the theory. In the course of this inductive reasoning, we use actual measurements of the system behavior to update the parameters as random variables.

As the theory becomes more probable and the parameters have narrower variances, it can be used to make high-confidence predictions. The biased coin example is a very simple instance.

The system involves flipping a coin to see how many heads and tails result. The theory is that the system behaves according to a binomial distribution with a parameter, b. We treated b as a random variable and did apply explicit Bayesian refinement methods going from prior to posterior distributions to see that the theory did apply to get tight ranges on b. With this understanding of the behavior of flipping the coin, we can with confidence predict how the coin will behave in the future and maybe win lots of bar bets.

We can do the same with software project management. As discussed above, we might want to predict when a project is likely to be complete. In this case, the random variable would be time-to-complete.

There are two kinds of evidence that we can use: Duration of previous similar projects The duration, dependencies, and status of project tasks Although Bayesian techniques can be applied to either, the second class of evidence arguably accounts better for the performance of the given team working on the specific software content.

So we start with a process model agile, for example of how the team will execute. This process will have a set of input parameters, including number and initial prior estimates of the effort of the planned features.

These can be used with simulation techniques to give us the distribution of time-to-complete. This is the deductive step. As the work becomes complete and the tasks are better understood, this information serve as evidence to carry out Bayesian refinement and update the distribution to get, hopefully, a narrower prediction.

This is the inductive step. Therefore, predictive methods require both deductive and inductive reasoning.