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Product details
File Size: 5570 KB
Print Length: 433 pages
Publisher: Wiley; 3 edition (February 24, 2014)
Publication Date: February 24, 2014
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B00INUYS2U
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This was a required text for a class for my Masters of Science in Organizational Leadership. I was terrified-I thought this was a statistics course. I had nothing to fear. This guy is a genius. The formulas he has developed to measure things that you would assume are impossible to measure are amazing. You don't need to know math-the equations are in the excel spreadsheets you download! Think of something you don't think you could measure. You can measure it, and this book shows you how. Very interesting and understandable, even for a math dummy like me.
My Ph.D. coursework revolved around advanced quantitative methods, and therefore I did consider many real-world things to be immeasurable. Indeed, lots of practical cases involve small sample sizes, a large number of factors that can influence the outcome, and many other sources of uncertainty. Such problems are not very amenable to advanced quantitative methods. They may appear absolutely hopeless, but not if you switch to the decision making framework described in this book.One simple idea is that, as long as the experiment reduces the costly uncertainty by the amount that is larger than the cost of research, it is worth performing. For instance, many experiments that may appear meaningless to a "classical" statistician because of their small sample size can be well justified once the benefit of uncertainty reduction is taken into account. The flipside is that if a study is well funded, well designed and replicated from the statistical viewpoint, it can still be useless. If our goal is to reduce the uncertainty to below a pre-specified threshold, then the "statistically" large sample size may still be unable to do the job. If the study is too expensive compared to the value of reduced uncertainty, it is not worth doing either. Another negative scenario is when we invest resources into measuring a variable that would make a small contribution to the uncertainty of the final outcome even if we knew the exact distribution of the variable.The book provides numerous examples that would be hard to crack for a "classical" statistician, and yet they are very amenable to the Applied Information Economics method developed by Mr. Hubbard. I can highly recommend it to all who "do not believe in statistics", as well as to the young quantitative analysts who want to expand the set of applications they can handle successfully.
Highly recommended.Most capital investments are poorly analyzed. Doug Hubbard provides remedies for the common shortcomings: o Typical cost-benefit analyses use single-value best estimates for inputs; these ignore or inadequately address risk and uncertainty. Also, often, important benefits are omitted because they are "intangibles" such as "improved customer service." Remedies in the book: Capture expert judgments as probability distributions; then solve the forecast model with Monte Carlo simulation. Decompose and explicitly represent former-intangibles in measurable units. o Multi-criteria scoring approaches often feel good yet have little theoretical foundation. They are entirely subjective and have not been shown to improve decision-making. Remedy: clarify the business (or other) objective and craft a quantified decision policy accordingly. Judge and/or model inputs in meaningful, quantitative terms.Everything important to a decision should be in a forecasting model. And everything in those models is either structural or quantified.For me, three primary themes emerge in the book:1. Calibration. Hubbard asserts that everything can be quantified--and he enjoys challenging individuals and groups to find any exception. Most people, even if expert in their field, are biased when making judgments. Hubbard shows ways to "calibrate" these experts (including the reader) with perhaps a half-day of practice. Most readers (as did I) will find that they initially fare poorly on the engaging calibration exercises in the book. We suffer from overconfidence and other cognitive biases. With feedback and practice, most people can quickly improve at assessing probabilities and probability distributions, and confidence ranges.2. What needs to be measured further? The most interesting calculation in the book is a form of sensitivity analysis. Which variables are most important to measure further? Hubbard calculates the value of perfect information for each variable with a straightforward expected opportunity loss calculation. Though an analysis may have dozens of identified variables with uncertainty, in his experience typically only 1 to 3 variables are worthy of further measurement.3. Deliberately seek information about the most-important risks and uncertainties where the additional time and cost are justified. This usually means obtaining more data with targeted investigation. Hubbard offers these encouraging maxims: o It has been done before o You have more data than you think o You need less data than you think o It is more economical than you thinkAdditional highlights of the book include: o Abundant war story examples. Much of Hubbard's work has been in the information technology (IT) sector. I especially enjoyed the case about forecasting fuel consumption for the U.S. Marine Corps. o Demystifying parameters that the reader might initially think as "intangibles." For example, "Improved Customer Service" might be measured in terms of percent of customers re-ordering; percent of returns; average delivery time, etc. o Example calculations, all done with Microsoft® Excel. He hosts a website, [...], where the reader can download example calculation worksheets, additional calibration quizzes, papers, articles, and reader comments.The first edition is excellent. Upon learning of a second edition, I immediately bought the updated book for a fresh re-read. This was again a good investment of time and money. The new edition is updated, expanded (about 15%), and more crisp. The editing and layout are again excellent. Several references are now embedded to his other best-selling and companion book, The Failure of Risk Management.
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