- MP3 CD
- Verlag: Brilliance Audio; Auflage: MP3 Una (15. Juli 2014)
- Sprache: Englisch
- ISBN-10: 1491531428
- ISBN-13: 978-1491531426
- Größe und/oder Gewicht: 13,3 x 1,3 x 17,1 cm
- Durchschnittliche Kundenbewertung: Schreiben Sie die erste Bewertung
- Amazon Bestseller-Rang: Nr. 2.109.176 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
Rock Breaks Scissors: A Practical Guide to Outguessing and Outwitting Almost Everybody (Englisch) MP3 CD – 15. Juli 2014
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"An ingenious guide to outsmarting others by predicting their choices when they are trying to be unpredictable." -- Kirkus (Starred Review)
"Of all of the books I've received for The Post, none has received as much over-the-shoulder reads than ROCK BREAKS SCISSORS."
―Susannah Callahan, The New York Post
Praise for Are You Smart Enough to Work at Google? "Poundstone displays his scientific knowledge, mathematical fluency, and knack for explaining the arcane in playfully precise sentences."―Bloomberg Businessweek
"A smart, engagingly written account of how to capitalize on other peoples' predictability . . . clearly explained and easily accessible to the general reader. An enlightening book."―Booklist
"Delightful, fun, and worth a read."―Seth Godin
"Incredibly gratifying....There's an art to these invasive questions, as Poundstone reveals in this neat little manifesto."―New Scientist -- Dieser Text bezieht sich auf eine andere Ausgabe: Gebundene Ausgabe.
Über den Autor und weitere Mitwirkende
William Poundstone is the author of thirteen previous books, including Are You Smart Enough to Work at Google?, How Would You Move Mount Fuji?, and Fortune's Formula. He has written for the New York Times, Harper's, Harvard Business Review, and the Village Voice, among other publications, and is a frequent guest on TV and radio. He lives in Los Angeles. Follow Poundstone on Twitter (@WPoundstone) and learn more at his website, home.williampoundstone.net.
-- Dieser Text bezieht sich auf eine andere Ausgabe: Taschenbuch.
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I am very intrigued by the topic as it brings together, game theory, number theory, and probability theory. Well done. Like it.
Charan Langton complextoreal.com
Added: I went back and looked at "machines that think" just out of curiosity. This is a book about history of artificial intelligence and in particular about the progress made in AI at a particular place (Dartmouth) and time (70's). The comparison is quite not appropriate. Now AI is a huge topic, far bigger than what this book is attempting to cover and describe that is the human biases and tendencies towards certain numbers particularly in light thresholds and constraints. When humans cheat they cheat in predictable ways, very much along what Dan Arielly talks about in his very excellent book "Predictably Irrational".
The book discusses the Benford's Law, also called the First-Digit Law, which is the frequency distribution of digits in real data such as financial, tax returns, etc. In this distribution, the number 1 occurs as the leading digit about 30% of the time, while larger numbers occur in that position less frequently: 9 as the first digit less than 5% of the time. This law can be used to determine if the data being examined has been faked which tends to deviate from this distribution significantly. This because no matter how hard we try, we humans are unable to create truly random sequences of digits. We have too many predictable biases for certain numbers and in our attempt to appear non-random create patently non-random numbers be it on tax returns or others data such as faked experiment results. The author talks about in addition to out biases about certain numbers but also orders in which we pick things making "magic tricks" possible.
So I am sticking to my rating.
There are 19 short chapters and one long one. One recurrent theme is that when opponents should randomize actions — tennis serves, soccer penalty kicks, poker bluffing, and of course rock-paper-scissors — they tend to avoid making the same action two or three times in succession; anticipating this gives you an edge. Another theme is exploiting observed non-uniformity; in lottery number choices or in answer options on multiple choice tests. Another theme is detection of manipulation or outright falsification of financial data, detectable by Benford’s (first digit) law or by occurrence of numbers just on the desirable side of some significant threshold. A final theme is inspired by the “hot hand” phenomenon, which all sports players and most sports spectators believe in, but which statisticians have debunked. So can you exploit others’ erroneous belief? One idea involves “betting on the underdog” systems which people have derived from past data, though the author seems unduly credulous about their future reliability.
The style — fast-paced short paragraphs in large type — makes very easy reading. And because there is no very comparable book, I am happy to recommend it to anyone interested in these topics out of general curiosity. Though to what extent this material will be actually useful to a typical reader, I find hard to guess.
The final long chapter on the stock market has more substance. The conventional academic view is that you should “buy and hold the market” via a low-expense index fund, rather than pay for professional management (incurring fees for no demonstrable benefit) or engage in some trading strategy of your own invention (typically seriously underperforming the market). The author advocates seeking to beat ``buy and hold” via long-cycle market timing, in particular the Shiller system of buying when market PE (based on last 10 years earnings) falls to some low threshold and selling when it rises to some high threshold. This system is supported by both logic and historical data, though being a ``whole lifetime” strategy I imagine that few people are psychologically prepared to adopt and persist with this one strategy for much of a lifetime.
A few minor quibbles. The advice on choosing passwords is sensible but omits the arguably better xkcd “correct_horse_battery_staple” (4 moderately common words strung together) idea and does not emphasize using a password strength checker. The advice on interpreting product ratings — look at the proportion of 5/5 ratings instead of the average rating — doesn’t seem based on any actual evidence. The discussion of Benford’s law admirably admits its misuse but doesn’t emphasize the main mathematical point, that it only applies to data with a rather wide range of values.
Despite not being altogether practical, the book is an interesting and thought provoking examination of a broad range of topics. The writing is direct, the concepts are understood by the author and communicated clearly.