Loading... The Drunkard's Walk : How Randomness Rules Our Lives (2008)by Leonard Mlodinow
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Sign up for LibraryThing to find out whether you'll like this book. No current Talk conversations about this book. George Gamow introduced me to Monte Carlo methods in a chapter of "One Two Three Infinity" (Hal's Pick of April, 2001) that I first read when I was about twelve. His vivid description and witty illustration of the path of a staggering drunk comes clearly to mind even these many decades later, and it surely inspired my research on a number of projects. Leonard Mlodinow has written a book that could well have a similar effect on its readers. Without using equations, he addresses some serious ideas, such as conditional probabilities and Bayesian statistics. His chapter on Measurement could be used for any of several science courses, and would be better than what is usually found. A scenario on conditional probability: Given that a couple with two children has one girl, the probability that they have two girls is 1/3. Maybe you were aware of that. But did you know that, if it is given that they have one girl named Florida, that the probability that they have two girls is 1/2? "The Drunkard's Walk" is full of many such seductive examples, that are not only theoretically interesting but also important in everyday life. ( ) An engaging review of probability and statistics, but I sometimes wished he would explain the mathematics behind things in more detail. Also, there was more historical information than I was expecting, which I wasn't that interested in. An interesting and very funny look at the history of statistics and how our tendency to see patterns in randomness can affect our lives and decisions. Essentially a powerful argument that winning isn't necessarily an argument for merit--when you set up a game, you will have winners and BIG winners, even when all the players are precisely the same. Important, life-altering outcomes are more random than we care to recognize. Agh, I love this book. The first time I read it, I hadn't yet encountered The Tipping Point: How Little Things Can Make a Big Difference or Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. On a second read, I discovered just how snarky Mlodinow is about them and their tendency to infer patterns where they may or may not exist. I still think he goes too far, though. Just because randomness is everywhere doesn't mean that there isn't an underlying signal; randomness doesn't imply 50-50. Mlodinow is both knowledgeable and passionate about his subject, and puts together a large number of examples in which not fully taking randomness into account has been disastrous in analysing data. He does a great job of providing an intuitive and entertaining introduction to probability and statistics, and the book is absolutely chock-full of stories which are both entertaining and illuminating. I found the book a little problematic because I think Mlodinow goes too far in the other direction. In several cases, he seems to me to conflate randomness with the total lack of signal, and this seems to me to be both problematic and dangerous. One example he uses is our reverence for the one-in-a-million entrepreneurs which succeed. He points out that given a very large sample, the probability of having one success is actually very large and implies nothing about the individual who succeeds. However, to me, it is just as dangerous to assume that there is no underlying signal (that essentially everything is blind luck) as it is to assume that everything has a deeper meaning. Oftentimes, we treat something as (totally) random because we don't have the knowledge to make a better prediction. For example, we talk about a coin flip as if it were totally random, drawn from a uniform distribution. But if you are flipping a coin and I am able to observe the angle and initial velocity, then I can do a heck of a lot better than a 50-50 guess! The same is true of the entrepreneur example. Just because we can talk about the probability of success doesn't imply that some internal mechanism doesn't exist! Mlodinow seems to me to assume that if the current features don't do a good job predicting outcomes, the outcomes must be (totally) random. He doesn't seem to consider that we might just be using the wrong features for the predictions! All the same, I think this is a wonderful read and a great reality check for all of us who assume skill and meaning rule everything in our lives.
This book is rich in handy little definitions that serve as signposts for would-be gamblers: availability bias, for instance, and the law of sample space; the lucky-guess scenario and the wrong-guess scenario; the prosecutor's fallacy, the sharpshooter effect and the law of large numbers.
References to this work on external resources. Wikipedia in English (4)Amazon.com Amazon.com Review (ISBN 0307275175, Paperback)Amazon Guest Review: Stephen HawkingPublished in 1988, Stephen Hawking’s A Brief History of Time became perhaps one of the unlikeliest bestsellers in history: a not-so-dumbed-down exploration of physics and the universe that occupied the London Sunday Times bestseller list for 237 weeks. Later successes include 1995’s A Briefer History of Time, The Universe in a Nutshell, and God Created the Integers: The Mathematical Breakthroughs that Changed History. Stephen Hawking is Lucasian Professor of Mathematics at the University of Cambridge. In The Drunkard’s Walk Leonard Mlodinow provides readers with a wonderfully readable guide to how the mathematical laws of randomness affect our lives. With insight he shows how the hallmarks of chance are apparent in the course of events all around us. The understanding of randomness has brought about profound changes in the way we view our surroundings, and our universe. I am pleased that Leonard has skillfully explained this important branch of mathematics. --Stephen Hawking (retrieved from Amazon Mon, 30 Sep 2013 13:30:14 -0400) An irreverent look at how randomness influences our lives, and how our successes and failures are far more dependent on chance events than we recognize. |
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