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on November 11, 2012
Excellent book!!! People looking for a "how to predict" silver bullet will (like some reviewers here) be disappointed, mainly because Silver is too honest to pretend that such a thing exists. The anecdotes and exposition are fantastic, and I wish we could make this book required reading for, say, everyone in the country.
During election season, everyone with a newspaper column or TV show feels entitled to make (transparently partisan) predictions about the consequences of each candidate's election to unemployment/crime/abortion/etc. This kind of pundit chatter, as Silver notes, tends to be insanely inaccurate. But there are also some amazing success stories in the prediction business. I list some chapter-by-chapter takeaways below (though there's obviously a lot depth more to the book than I can fit into a list like this):
1. People have puzzled over prediction and uncertainty for centuries.
2. TV pundits make terrible predictions, no better than random guesses. They are rewarded for being entertaining, and not really penalized for being wrong.
3. Statistics has revolutionized baseball. But computer geeks have not replaced talent scouts altogether. They're working together in more interesting ways now.
4. Weather prediction has gotten lots better over the last fifty years, due to highly sophisticated, large-scale supercomputer modeling.
5. We have almost no ability to predict earthquakes. But we know that some regions are more earthquake prone, and that in a given region an earthquake of magnitude n happens about ten times as often as an earthquake of magnitude (n+1).
6. Economists are terrible at predicting quantities such as next year's GDP. Predictions are only very slightly correlated with reality. They also tend to be overconfident, drastically underestimating the margin of error in their guesses. Politically motivated predictions (such as those released by White House, historically) are even worse.
7. The spread of a disease like the flu is hard to predict. Sometimes we overreact because risk of under-reacting seems greater.
8. A few professional sports gamblers are able to make make a living by spotting meaningful patterns before others do, and being right slightly more than half the time.
9. Kasparov thought he could beat Deep Blue. Couldn't. Interesting tale of humans/computers trying to outguess each other.
10. Nate Silver made a living playing online poker for a few years. When the government tightened the rules, the less savvy players ("fish") stopped playing, and he found he couldn't make money any more. So he started FiveThirtyEight.
11. Efficient market hypothesis: market seems very efficient, but not perfectly so. Possible source of error: most investment is done by institutions, and individuals at these institutions are rewarded based on short term profits. Rational employees may have less career risk when they "bet with the consensus" than when they buck a trend: this may increase herding effects and makes bubbles worse. Note: Nate pointedly does not claim that one can make money on Intrade by betting based on FiveThirtyEight probabilities. But he stresses that Intrade prices are themselves probably heavily informed by poll-based models like the ones on FiveThirtyEight.
12. Climate prediction: prima facie case for anthropic warming is very strong (greenhouse gas up, temperature up, good theoretical reason for former causing latter). But lots of good reason to doubt accuracy of specific elaborate computer models, and most scientists admit uncertainty about details.
13. We failed to predict both Pearl Harbor and September 11. Unknown unknowns got us. Got to watch out for loose Pakistani nukes and other potential catastrophic surprises in the future.
During election season, everyone with a newspaper column or TV show feels entitled to make (transparently partisan) predictions about the consequences of each candidate's election to unemployment/crime/abortion/etc. This kind of pundit chatter, as Silver notes, tends to be insanely inaccurate. But there are also some amazing success stories in the prediction business. I list some chapter-by-chapter takeaways below (though there's obviously a lot depth more to the book than I can fit into a list like this):
1. People have puzzled over prediction and uncertainty for centuries.
2. TV pundits make terrible predictions, no better than random guesses. They are rewarded for being entertaining, and not really penalized for being wrong.
3. Statistics has revolutionized baseball. But computer geeks have not replaced talent scouts altogether. They're working together in more interesting ways now.
4. Weather prediction has gotten lots better over the last fifty years, due to highly sophisticated, large-scale supercomputer modeling.
5. We have almost no ability to predict earthquakes. But we know that some regions are more earthquake prone, and that in a given region an earthquake of magnitude n happens about ten times as often as an earthquake of magnitude (n+1).
6. Economists are terrible at predicting quantities such as next year's GDP. Predictions are only very slightly correlated with reality. They also tend to be overconfident, drastically underestimating the margin of error in their guesses. Politically motivated predictions (such as those released by White House, historically) are even worse.
7. The spread of a disease like the flu is hard to predict. Sometimes we overreact because risk of under-reacting seems greater.
8. A few professional sports gamblers are able to make make a living by spotting meaningful patterns before others do, and being right slightly more than half the time.
9. Kasparov thought he could beat Deep Blue. Couldn't. Interesting tale of humans/computers trying to outguess each other.
10. Nate Silver made a living playing online poker for a few years. When the government tightened the rules, the less savvy players ("fish") stopped playing, and he found he couldn't make money any more. So he started FiveThirtyEight.
11. Efficient market hypothesis: market seems very efficient, but not perfectly so. Possible source of error: most investment is done by institutions, and individuals at these institutions are rewarded based on short term profits. Rational employees may have less career risk when they "bet with the consensus" than when they buck a trend: this may increase herding effects and makes bubbles worse. Note: Nate pointedly does not claim that one can make money on Intrade by betting based on FiveThirtyEight probabilities. But he stresses that Intrade prices are themselves probably heavily informed by poll-based models like the ones on FiveThirtyEight.
12. Climate prediction: prima facie case for anthropic warming is very strong (greenhouse gas up, temperature up, good theoretical reason for former causing latter). But lots of good reason to doubt accuracy of specific elaborate computer models, and most scientists admit uncertainty about details.
13. We failed to predict both Pearl Harbor and September 11. Unknown unknowns got us. Got to watch out for loose Pakistani nukes and other potential catastrophic surprises in the future.
on May 26, 2017
The author Nate Silver does a great job weaving more technical statistical concepts early in the book, so as not to lose readers early on. However I thought this would lead to more a detailed technical discussion later on, which the author said it would, but it never really transpired. Instead he kept to analogies and keeping the science of prediction in context. Which there's really nothing wrong with, if you're someone looking for that ... just not exactly what I wanted nor expected.
Nonetheless it's a great book, and Silver bears the hallmark of someone who is intellectually curious and genuinely interested in making his analytical tool better, rather than attaching his ego to the outcome. As part of that, he's refreshingly candid in his opinion of others. Well researched and covers a lot of area including sports, weather, financial meltdown's, chess, and others. The best section for me was on chess, where he displayed both his story telling skills (retelling of chess master Kasparov's loss to IBM was both compelling and insightful), and more in depth technical discussion which chess lends itself to. The book seemed to run out of steam toward the end, with some chapters going on longer than I thought necessary, particularly poker and efficient markets.
He shares some of my core beliefs that statistics/data is not enough, if you really want to understand something and make good forecasts you need to understand its underlying structure. And that the proper relationship between man and machine is symbiotic, rather than one taking over the other. Those, and the importance of thinking probabilistically, are the core takeaways.
Nonetheless it's a great book, and Silver bears the hallmark of someone who is intellectually curious and genuinely interested in making his analytical tool better, rather than attaching his ego to the outcome. As part of that, he's refreshingly candid in his opinion of others. Well researched and covers a lot of area including sports, weather, financial meltdown's, chess, and others. The best section for me was on chess, where he displayed both his story telling skills (retelling of chess master Kasparov's loss to IBM was both compelling and insightful), and more in depth technical discussion which chess lends itself to. The book seemed to run out of steam toward the end, with some chapters going on longer than I thought necessary, particularly poker and efficient markets.
He shares some of my core beliefs that statistics/data is not enough, if you really want to understand something and make good forecasts you need to understand its underlying structure. And that the proper relationship between man and machine is symbiotic, rather than one taking over the other. Those, and the importance of thinking probabilistically, are the core takeaways.
on January 12, 2017
Nate Silver is best known for using polling data to call political elections. He missed on the Trump win, but was pretty good up until then.
The Signal and the Noise is a well written, well researched and well reasoned book about forecasting and the various mistakes that prognosticators make. He addresses failures as the inability of economists and others to foresee the bursting of the housing bubble and the chaos it created in 2008. Other themes include easier-to-predict subjects such as future performance of major league baseball players and the success (or not) of poker players. In these later two, he has real world experience as he developed software to predict baseball player performance and made a living as a professional poker player.
Other forecasting areas that he writes about include weather (a modern success); earthquakes (not so much due to difficulties in differentiating the signal from the noise); the spread of infectious diseases (difficult to model due to human behaviour); and climate change (right on warming but uncertain about effects).
One of the over all themes involves the Bayes Theorem. This requires an a priori hunch about the chances of an event that is refined by future observations and experimenting.
There were sections I like more than others, but this may correlate more with my affinity for the subjects rather than Silver's reporting. I particularly like the section on Climate Change research. It was thoughtful and open-minded. As he does throughout the book, he looks at the facts and the stats and interviews the people involved in the research.
The Signal and the Noise is a well written, well researched and well reasoned book about forecasting and the various mistakes that prognosticators make. He addresses failures as the inability of economists and others to foresee the bursting of the housing bubble and the chaos it created in 2008. Other themes include easier-to-predict subjects such as future performance of major league baseball players and the success (or not) of poker players. In these later two, he has real world experience as he developed software to predict baseball player performance and made a living as a professional poker player.
Other forecasting areas that he writes about include weather (a modern success); earthquakes (not so much due to difficulties in differentiating the signal from the noise); the spread of infectious diseases (difficult to model due to human behaviour); and climate change (right on warming but uncertain about effects).
One of the over all themes involves the Bayes Theorem. This requires an a priori hunch about the chances of an event that is refined by future observations and experimenting.
There were sections I like more than others, but this may correlate more with my affinity for the subjects rather than Silver's reporting. I particularly like the section on Climate Change research. It was thoughtful and open-minded. As he does throughout the book, he looks at the facts and the stats and interviews the people involved in the research.
on March 12, 2017
This book had it's moments but overall it was not great. The baseball, climate change and poker chapters dragged on an on. The practice of rain bias in weather forecasting was really interesting. It explains that weather forecasters are more likely to say it might rain when the actual chance is low because if they say it's sunny and it rains you'll be way more disappointed than if they say it will rain and it doesn't.
on May 7, 2016
Early in "The Signal and the Noise," author Nate Silver reminds us how often we make predictions and forecasts in our day-to-day lives. Doing so is an unavoidable task of living, and this superb book takes the reader through the pitfalls inherent in prognostication and how better to avoid them by recognizing what is irrelevant (the noise) and what is germane (the signal).
There are many types of errors that lead to incorrect forecasts, and Silver discusses how people let their biases overly control their thoughts, how people use incomplete information to come up with predictions, and how they ignore pertinent warning signs. Silver outlines personality traits that make for both good and bad forecasters and talks about the types of errors that lead to incorrect predictions and the importance of objectivity.
The author shows that the concept behind Bayes' theorem, thinking probabilistically, is the key in revising our predictions as we get new information to make them better. Silver takes the reader on a breezy ride through the fields of politics, the housing bubble of the last decade, baseball, weather forecasting, earthquakes, economics, pandemics, gambling, chess, poker, stock markets, climate change, and terrorism to illustrate the concepts he puts forth.
Silver acknowledges that luck plays a role in some areas of our lives, but stresses that being more fundamentally sound in our prediction abilities can always help us ignore the noise, pay attention to the signal, and make better decisions. He closes with a short recapitulation of the main concepts he introduces.
The author is a well-known liberal, but he is an honest broker and teaches his concepts without making the case for any political ideology. "The Signal and the Noise" is great fun, especially for math lovers, but also for anyone with an interest in one of the areas he covers in the thirteen chapters he uses to illustrate his concepts. One prediction I can be sure in seeing come true is that the vast majority who invest the time to read this book will be glad that they did so.
There are many types of errors that lead to incorrect forecasts, and Silver discusses how people let their biases overly control their thoughts, how people use incomplete information to come up with predictions, and how they ignore pertinent warning signs. Silver outlines personality traits that make for both good and bad forecasters and talks about the types of errors that lead to incorrect predictions and the importance of objectivity.
The author shows that the concept behind Bayes' theorem, thinking probabilistically, is the key in revising our predictions as we get new information to make them better. Silver takes the reader on a breezy ride through the fields of politics, the housing bubble of the last decade, baseball, weather forecasting, earthquakes, economics, pandemics, gambling, chess, poker, stock markets, climate change, and terrorism to illustrate the concepts he puts forth.
Silver acknowledges that luck plays a role in some areas of our lives, but stresses that being more fundamentally sound in our prediction abilities can always help us ignore the noise, pay attention to the signal, and make better decisions. He closes with a short recapitulation of the main concepts he introduces.
The author is a well-known liberal, but he is an honest broker and teaches his concepts without making the case for any political ideology. "The Signal and the Noise" is great fun, especially for math lovers, but also for anyone with an interest in one of the areas he covers in the thirteen chapters he uses to illustrate his concepts. One prediction I can be sure in seeing come true is that the vast majority who invest the time to read this book will be glad that they did so.
on January 11, 2017
I bought this based on a review I'd read in the WSJ and by a reviewer I trusted. A mistake.
While it is comparitively well written and I enjoyed the writing, the author fails to really answer the why. It merely demonstrates failures, usually with interesting baseball examples, but most often with the same player. I finally got tired of seeing why they made bad predictions about a player the author loved.
Yes, the author explains that there was not enough data measured for various predictions, in particular the drive of a player or politician, or the prevailing winds in a political race. But, I think the entire book boils down to a few pages explaining various cases of immeasurable intangibles that make predicting hazardous and treacherous. Intangibles that can change predictions greatly, e.g., there were three economists in a room ...
While it is comparitively well written and I enjoyed the writing, the author fails to really answer the why. It merely demonstrates failures, usually with interesting baseball examples, but most often with the same player. I finally got tired of seeing why they made bad predictions about a player the author loved.
Yes, the author explains that there was not enough data measured for various predictions, in particular the drive of a player or politician, or the prevailing winds in a political race. But, I think the entire book boils down to a few pages explaining various cases of immeasurable intangibles that make predicting hazardous and treacherous. Intangibles that can change predictions greatly, e.g., there were three economists in a room ...
on June 30, 2013
If you are hoping for some simple guidelines that will help you distinguish actionable information (signal) from random information (noise), as I was, I expect that you'll be disappointed. I came away from the book with the belief that the author was honest. He's giving you his best analysis and his best approaches to making predictions. Distinguishing signal from noise requires, in the author's words, "both scientific knowledge and self-knowledge...." One has to know one's own biases before one can honestly collect, review and analyze any body of information to make a prediction. He argues in favor of Bayes' Theorem as the preferred statistical approach to make predictions. Do not worry if, like me, you are not a statistician or find formulas challenging. The author has a light touch in this area and explains that he prefers Bayes' Theorem because it explicitly requires one to express one's initial belief (bias perhaps) in forming a prediction.
Mr. Silver does not view predictions as "one and done." The process of prediction may, and frequently does, require the modification of predications as more, or new, information is gathered. He expresses this as the process of making predictions "less and less wrong." In some areas that he considers, such as earthquake prediction, the process of being less and less wrong has been highly limited to virtually non-existent. In other areas, success has been more measurable. The author got his start analyzing baseball statistics and had success with some of his statistical findings. The book Moneyball by Michael Lewis describes some of these successes. He next applied his statistical skills to political predictions and enjoyed success there as well. But, in both of these areas there was a considerable body of statistical information and a history of third-party predictions that could be analyzed. Even in these areas the author honestly recognizes that the ability to predict has limitations.
Mr. Silver considers chess and looks at the success the supercomputer Deep Blue had against Garry Kasparov. He found one surprising observation about the first win that Deep Blue had against Mr. Kasparov. He considers some of the limitations of the computer's analytical capacity.
One section describes Mr. Silver's experiences in playing poker and the predictive approaches necessary to succeed in this endeavor. I wasn't sure if this section was all that helpful, but it did further his thesis that one must recognize one's own limitations (and have a firm grasp on the rules of the game) to have any chance of a useful prediction.
Good prediction may require imagination and require one to set aside current world views to look at matters anew. This may be especially helpful in trying to predict matters such as terrorists' activities.
I recommend the book if for no other reason than it will challenge you to consider your own biases and preconceived notions. I appreciated what I feel is his honest approach to the subject. I came away with a belief that I have to look hard at the information that I have gathered, the sources of the information, and the analytical to which I have subjected the information before I try to predict an outcome.
Mr. Silver does not view predictions as "one and done." The process of prediction may, and frequently does, require the modification of predications as more, or new, information is gathered. He expresses this as the process of making predictions "less and less wrong." In some areas that he considers, such as earthquake prediction, the process of being less and less wrong has been highly limited to virtually non-existent. In other areas, success has been more measurable. The author got his start analyzing baseball statistics and had success with some of his statistical findings. The book Moneyball by Michael Lewis describes some of these successes. He next applied his statistical skills to political predictions and enjoyed success there as well. But, in both of these areas there was a considerable body of statistical information and a history of third-party predictions that could be analyzed. Even in these areas the author honestly recognizes that the ability to predict has limitations.
Mr. Silver considers chess and looks at the success the supercomputer Deep Blue had against Garry Kasparov. He found one surprising observation about the first win that Deep Blue had against Mr. Kasparov. He considers some of the limitations of the computer's analytical capacity.
One section describes Mr. Silver's experiences in playing poker and the predictive approaches necessary to succeed in this endeavor. I wasn't sure if this section was all that helpful, but it did further his thesis that one must recognize one's own limitations (and have a firm grasp on the rules of the game) to have any chance of a useful prediction.
Good prediction may require imagination and require one to set aside current world views to look at matters anew. This may be especially helpful in trying to predict matters such as terrorists' activities.
I recommend the book if for no other reason than it will challenge you to consider your own biases and preconceived notions. I appreciated what I feel is his honest approach to the subject. I came away with a belief that I have to look hard at the information that I have gathered, the sources of the information, and the analytical to which I have subjected the information before I try to predict an outcome.
on August 30, 2016
Quite possibly the most interesting book I have read in sometime, Nate Silver's The Signal and the Noise is a wide ranging work which revolves around one theme: what can people accurately predict? It turns out very little. In fact, this work is replete with examples of how falsely optimistic experts are about their forecasting ability. Silver’s overall goal is to work within the parameters of what is possible to predict, and give the best possible approximation of how this can be done.
Certain events are fairly easier to predict than others. Baseball, one of Silver’s favorite examples, is a game played under proscribed circumstances, with the rules well-known, and the statistics of the game results recorded for many decades. In this data rich and rule laden environment, forecasting is far easier than, say, in more complex systems. Earthquakes are sometimes anticipated by minor quakes, but sometimes they aren’t. There simply is no way to adequately predict the behavior of fault lines many miles under the earth. They are too complex for our models.
Yet complexity isn’t the only measure of our ability to predict. Silver claims that meteorological predictions is one of the great success stories of this book. The atmosphere is a complex system, but it is governed by simple processes that can be observed and recorded. In the last 30 years, with advances in computer modeling, forecasting has improved dramatically. Weather prediction, often the butt of jokes, is actually a very successful forecasting method.
All in all, Silver’s book is a paean to Bayesian method of forecasting. Created by Scottish clergyman Thomas Bayes in the seventeenth century, “Bayes' theorem is a formula that describes how to update the probabilities of hypotheses when given evidence.” That is, it allows us to further change and refine our predictions based on new evidence. A common sense approach, but one that has not always been taken.
Certain events are fairly easier to predict than others. Baseball, one of Silver’s favorite examples, is a game played under proscribed circumstances, with the rules well-known, and the statistics of the game results recorded for many decades. In this data rich and rule laden environment, forecasting is far easier than, say, in more complex systems. Earthquakes are sometimes anticipated by minor quakes, but sometimes they aren’t. There simply is no way to adequately predict the behavior of fault lines many miles under the earth. They are too complex for our models.
Yet complexity isn’t the only measure of our ability to predict. Silver claims that meteorological predictions is one of the great success stories of this book. The atmosphere is a complex system, but it is governed by simple processes that can be observed and recorded. In the last 30 years, with advances in computer modeling, forecasting has improved dramatically. Weather prediction, often the butt of jokes, is actually a very successful forecasting method.
All in all, Silver’s book is a paean to Bayesian method of forecasting. Created by Scottish clergyman Thomas Bayes in the seventeenth century, “Bayes' theorem is a formula that describes how to update the probabilities of hypotheses when given evidence.” That is, it allows us to further change and refine our predictions based on new evidence. A common sense approach, but one that has not always been taken.
on December 8, 2012
I got hooked on Nate Silver and his FiveThirtyEight during the recent presidential election. The fact that his predictions about the outcome were dead-on were inspiring but his new book, "The Signal and the Noise" raises his thinking to a higher and broader level. In it he not only comments about politics but many other areas of life where forecasts are needed or welcomed, making this book fascinating.
Silver begins with the 2008 housing bubble and why it burst, leading him later to reveal that economic forecasting is little better than it was a generation or two ago. He then goes on to include a chapter related to political forecasting using professor Philip Tetlock's description of writers and thinkers...they are either hedgehogs (people who tend to forecast poorly) or foxes (who do just the opposite). He amusingly points out that Dick Morris is a hedgehog and then Silver goes on to "shake up" the panelists on the McLaughlin Group...one of the few humorous parts of the book.
The author spends some time on weather forecasting and how, unlike economic predictions, it has gotten much better over the years. He relates the fact that predicting the landfall of Hurricane Katrina was much more accurate than it could have been twenty years before the storm hit New Orleans in 2005.
I particularly liked three chapters. One is a chapter in which Silver describes Bob Voulgaris and his success in predicting the outcomes of basketball games. This one is a real treat as the author spends a good amount of time telling us why Voulgaris comes out on top more often than not. The second chapter involves computerized chess matches with regard to how either side (real player or computer) is prone to make mistakes. The third chapter is about poker...Silver's own initial success in it, who really is good at it and how that lead to his beginning FiveThirtyEight.
What Silver points out often is that aggregate forecasts are always better than individual predictions, but not necessarily better than just one forecast. Even though there are a few times during the book when the narrative gets a little dry (algorithms, mostly!) the author has a propensity to give examples in layman's terms and this helps the reader in better understanding the points he is driving home.
"The Signal and the Noise" became a far more interesting book than I thought it would be when I began it....enjoyable and educational from start to finish. I highly recommend it and eagerly await his FiveThirtyEight in 2016!
Silver begins with the 2008 housing bubble and why it burst, leading him later to reveal that economic forecasting is little better than it was a generation or two ago. He then goes on to include a chapter related to political forecasting using professor Philip Tetlock's description of writers and thinkers...they are either hedgehogs (people who tend to forecast poorly) or foxes (who do just the opposite). He amusingly points out that Dick Morris is a hedgehog and then Silver goes on to "shake up" the panelists on the McLaughlin Group...one of the few humorous parts of the book.
The author spends some time on weather forecasting and how, unlike economic predictions, it has gotten much better over the years. He relates the fact that predicting the landfall of Hurricane Katrina was much more accurate than it could have been twenty years before the storm hit New Orleans in 2005.
I particularly liked three chapters. One is a chapter in which Silver describes Bob Voulgaris and his success in predicting the outcomes of basketball games. This one is a real treat as the author spends a good amount of time telling us why Voulgaris comes out on top more often than not. The second chapter involves computerized chess matches with regard to how either side (real player or computer) is prone to make mistakes. The third chapter is about poker...Silver's own initial success in it, who really is good at it and how that lead to his beginning FiveThirtyEight.
What Silver points out often is that aggregate forecasts are always better than individual predictions, but not necessarily better than just one forecast. Even though there are a few times during the book when the narrative gets a little dry (algorithms, mostly!) the author has a propensity to give examples in layman's terms and this helps the reader in better understanding the points he is driving home.
"The Signal and the Noise" became a far more interesting book than I thought it would be when I began it....enjoyable and educational from start to finish. I highly recommend it and eagerly await his FiveThirtyEight in 2016!
