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A Brief Summary and Review
on October 2, 2012
*A full executive summary of this book is available at newbooksinbrief dot com.
Making decisions based on an assessment of future outcomes is a natural and inescapable part of the human condition. Indeed, as Nate Silver points out, "prediction is indispensable to our lives. Every time we choose a route to work, decide whether to go on a second date, or set money aside for a rainy day, we are making a forecast about how the future will proceed--and how our plans will affect the odds for a favorable outcome" (loc. 285). And over and above these private decisions, prognosticating does, of course, bleed over into the public realm; as indeed whole industries from weather forecasting, to sports betting, to financial investing are built on the premise that predictions of future outcomes are not only possible, but can be made reliable. As Silver points out, though, there is a wide discrepancy across industries and also between individuals regarding just how accurate these predictions are. In his new book `The Signal and the Noise: Why So Many Predictions Fail--but Some Don't' Silver attempts to get to the bottom of all of this prediction-making to uncover what separates the accurate from the misguided.
In doing so, the author first takes us on a journey through financial crashes, political elections, baseball games, weather reports, earthquakes, disease epidemics, sports bets, chess matches, poker tables, and the good ol' American economy, as we explore what goes into a well-made prediction and its opposite. The key teaching of this journey is that wise predictions come out of self-awareness, humility, and attention to detail: lack of self-awareness causes us to make predictions that tell us what we'd like to hear, rather than what is true (or most likely the case); lack of humility causes us to feel more certain than is warranted, leading us to rash decisions; and lack of attention to detail (in conjunction with self-serving bias and rashness) leads us to miss the key variables that make all the difference. Attention to detail is what we need to capture the signal in the noise (the key variable[s] in the sea of data and information that are integral in determining future outcomes), but without self-awareness and humility, we don't even stand a chance.
While self-awareness requires us to make an honest assessment of our particular biases, humility requires us to take a probabilistic approach to our predictions. Specifically, Silver advises a Bayesian approach. Bayes’ theorem has it that when it comes to making a prediction, the most prudent way to proceed is to first come up with an initial probability of a particular event occurring (rather than a black and white prediction of the form ‘I believe x will occur’). Next, we must continually adjust this initial probability as new information filters in.
The level of certainty that we can place on our initial estimate of the probability of a particular event (and the degree to which we can accurately refine it moving forward) is limited by the complexity of the field in which we are making our prediction, and also the amount and quality of the information that we have access to. For instance, in a field like baseball, where wins and losses mostly comes down to two variables (the skill of the pitchers, and the skill of the hitters), and where there is an enormous wealth of precise data, prediction is relatively straightforward (but still not easy). On the other hand, in a dynamic field such as the American economy, where the outcomes are influenced by an enormous number of variables, and where the interactions between these variables can become incredibly complex (due to things like positive and negative feedback), probabilities become a whole lot more difficult to pin down precisely (though they often remain possible on a general and/or long-term scale).
It is also important to recognize that while additional information can help us no matter what field we are trying to make our prediction in, we must be careful not to think that information can stand on its own. Indeed, additional information (when it is not met with insightful analysis) often does nothing more than draw our attention away from the key variables that truly make a difference. In other words, it creates more noise, which can make it more difficult to identify the signal. It is for this reason that predictive models that rely on statistics and statistics alone are often not very effective (though they do often help a seasoned expert who is able to apply insightful analysis to them).
In the final stage of the book Silver explores how the lessons that he lays out can be applied to such issues as global warming, terrorism and bubbles in financial markets. Unfortunately, each of these fields is a lot noisier than many of us would like to think (thus making them very difficult to predict precisely). Nevertheless, the author argues, within each there are certain signals that can help us make better predictions regarding them, and which should help make the world a safer and more livable place.
If you are hoping that this book will make you a fool-proof prognosticator, you are going to be disappointed. A key tenet of the book is that this is simply not possible (no matter what field you are in). That being said, Silver makes a very strong argument that by applying a few simple principles (and putting in a lot of hard work in identifying key variables) our predictive powers should take a great boost indeed. A full executive summary of this book is available at newbooksinbrief dot com; a podcast discussion of Silver's treatment of Bayes' theorem is also available.