- Taschenbuch: 416 Seiten
- Verlag: John Wiley & Sons Inc; Auflage: Reprint (15. März 2012)
- Sprache: Englisch
- ISBN-10: 1118073754
- ISBN-13: 978-1118073759
- Größe und/oder Gewicht: 15,2 x 3 x 22,9 cm
- Durchschnittliche Kundenbewertung: Schreiben Sie die erste Bewertung
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The Flaw of Averages: Why We Underestimate Risk in the Face of Uncertainty (Englisch) Taschenbuch – 15. März 2012
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"Statistical uncertainties are pervasive in decisions we make every day in business, government, and our personal lives. Sam Savage's lively and engaging book gives any interested reader the insight and the tools to deal effectively with those uncertainties. I highly recommend The Flaw of Averages."
William J. Perry, former U.S. Secretary of Defense
"Enterprise analysis under uncertainty has long been an academic ideal. . . . In this profound and entertaining book, Professor Savage shows how to make all this practical, practicable, and comprehensible . . . the Distribution String . . . represents a major breakthrough in the communication of risk and uncertainty."
Harry Markowitz, Nobel Laureate in Economics
"This is a book written for laymen with enough interesting insights to engage even the most scholarly professional."
Douglas Hubbard, author of How to Measure Anything
"Sam Savage is the Edward Tufte of risk."
Matthew Raphaelson, Executive Vice President, Wells Fargo
A GROUNDBREAKING MUST-READ FOR ANYONE WHO MAKES BUSINESS DECISIONS IN THE FACE OF UNCERTAINTY
In The Flaw of Averages, Sam Savageknown for his creative exposition of difficult subjectsdescribes common avoidable mistakes in assessing risk in the face of uncertainty. He explains why plans based on average assumptions are wrong, on average, in areas as diverse as finance, healthcare, accounting, the war on terror, and climate change. Savage refers to anachronistic statistical jargon as Red Words, which he defines as things that may not be uttered in a singles bar. Instead, he presents complex concepts in plain English (Green Words), backed up by interactive simulations at www.FlawofAverages.com, which connect the seat of the intellect to the seat of the pants.
Savage also presents the emerging field of Probability Management aimed at curing the Flaw of Averages through more transparent communication of uncertainty and risk. Savage argues that this is a problem that must be solved if we are to improve the stability of our economy, and that we cannot repeat the recent mistakes of applying "steam era" statistics to "information age" risks.
Über den Autor und weitere Mitwirkende
Sam L. Savage is a Consulting Professor of Management Science and Engineering at Stanford University, and a Fellow of the Judge Business School at the University of Cambridge.
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The whole book is based upon not using averages, but instead run a Monte Carlo simulation using probabilities.
I know of one famous case where using averages caused a famous person to look like a fool. If you study the long-term annual returns of the US stock market, you will find an average return of about 10% with 1 sigma of about 20%. These annual returns fit a normal distribution pretty well (unlike daily returns which fit a logarithmic distribution better).
Very few people have been able to invest and beat the general 10% average return of the US stock market. Peter Lynch, of the Fidelity Magellan fund, was one of these few people. I think he beat the S&P 500 stock index average for about 15 years straight, before retiring.
In an article in the September 1995 issue of Worth magazine, Lynch was quoted as saying that retirees can easily annually withdraw 7% from their portfolio.
Apparently Lynch was not aware that a year ealier, 1994, William Bengen used one of the first personal computers to run a simulation to determine the maximum percentage of a portfolio that a retiree could annually withdraw. He found that 4% was the maximum, not the 7% that Lynch suggested.
Why did Lynch, a very smart investor, get is so wrong? Using average returns does not account for the variability in annual stock market returns. If the retiree experiences a few bad years of stock market returns during the first few years of retirement, there is not enough time for the portfolio to recover. Retirees that use a 7% withdrawal rate have about a 50% change of outliving their retirement portfolio.
Another famous person that focused on average returns and apparently did no meaningful simulations was Dick Fuld, CEO of Lehman Brothers when it went bankrupt in the Fall of 2008. I saw Fuld testify at Congressional hearings where he lamented that he did know what went wrong, and he was sorry he had hurt so many employees and investors.
While he was testifying, I did some mental math. Fuld had Lehman leveraged at a 33 to 1 ratio. This means a 3% drop in real estate prices causes the mortgage scheme to collapse. I was aware that in the 1990s, California real estate prices had dropped by at least 10%, so a 3% drop was very possible.
After Fuld finished testifying, a Professor testified next. He repeated the 33:1 leverage, then stated what I had been thinking just a few minutes before. He said a 3% drop in real estate prices would sink Lehman. The Professor speculated that the $600 million in salary Fuld had been paid the couple years before the collapse may have clouded his judgment.
All-in-all, this book will be enlightening to those people who are not aware of the importance of understanding the dramatic impact that variability of inputs can have on outputs.
This is no mystery to statisticians, just one of 100 workaday concepts not particularly emphasized in a typical college statistics course -- though as the book implies, worthy of more emphasis in a business statistics course. Scattered through the book are the components of a potentially great 30-page chapter illuminating this idea via graphical ideas of representing distributions as histograms, and describing contexts where asymmetries are important. For instance: bonuses for meeting a target, inventory and supply-demand, options, a project takes as long as its longest component, and interesting analogs with portfolio theory.
It would have been valuable to extend these 30 pages to either (i) in depth case studies, or (ii) a wide-ranging book explaining 10 different statistics ideas in 10 chapters. But instead, this book becomes a mashup of historical and personal anecdotes, superficial descriptions of case studies, some of the "popular science" style topics in probability and statistics covered in many other books, and witticisms about traditional mathematical statistics as "Steam Era statistics". As the author implies, it would be useful if Excel and open source analogs had an option for entering distributions instead of numbers. Alas, this book turns into a plug for the author's Probability Management software, and case studies turn into one paragraph testimonials.