- ISBN13: 9780470387955
- Condition: NEW
- Notes: Brand New from Publisher. No Remainder Mark.
Product Description
An essential guide to the calibrated risk analysis approach The Failure of Risk Management takes a close look at misused and misapplied basic analysis methods and shows how some of the most popular “risk management” methods are no better than astrology! Using examples from the 2008 credit crisis, natural disasters, outsourcing to China, engineering disasters, and more, Hubbard reveals critical flaws in risk management methods–and shows how all… More >>
The Failure of Risk Management: Why It’s Broken and How to Fix It
I would love to review this book, which I ordered from Amazon 4 months ago, but it was never delivered. And while Amazon helpfully allows me to track shipments, should they not arrive Amazon provide no way to inform them. So I am out of pocket, with no way except this stupid review to communicate the fact to Amazon.
Next time, I will purchase from elsewhere.
Rating: 1 / 5
I read reviews for this and thought it might be a good read. WRONG. I usually don’t write reviews, but this book is so poorly suited for the intent of its publication (the author claims he has solutions to fix risk management) that I felt I needed to warn others about it.
This is book intended for those that have very little mathematical inclination (but he does state that in the beginning of the book). In fact, I think this book does a disservice as it may mislead those who do not have any advanced knowledge of mathematics or statistics (or related fields) will be confused due to his poor explanations which also are sometimes just plain wrong.
Please note that Hubbard has NO credentials in this field. He does not disclose where his bachelors degree is from and what field it is in. He does tout a Masters in Management Information Systems from the University of SOUTH DAKOTA (we all know how prestigious that is) and for those who don’t know, Management Information Systems has nothing to do with anything mathematical, including probability, statistics, or actuarial science. The only math an MIS degree has is probably an introductory calculus course. Hubbard mentions being a management consultant for Coopers & Lybrand, but that is hardly anything noteworthy. It is no McKinsey and certainly will not give him a background for the mathematics that he praises in his book. Essentially this guy has very little knowledge of what he attempts to talk about and it shows. Hubbard only regurgitates, and very much simplifies, the research of others. He mentions the JSTOR database, and for those familiar with it, what Hubbard does in his book is pretty much just rewords a few of the sentences in the abstract of someone else’s research paper.
A previous review refers to Hubbard as a quant. He is by no means deserving of that title and has no business acting as an authority on probabilistic risk management or anything requiring quantitative analysis.
The book seems like it’s just trying to sell his seminars on “calibrated probability,” which is complete BS by the way. In the book he claims that most people haven’t heard of “calibrated probability,” but that’s because it IS NOT legitimate theory. There is NO “calibrated probability” in mathematics or statistics. On that same note, “Applied Information Economics” is also NOT a legitimate field. This is not something accepted by the academic community, or any community for that matter. It is only accepted by those who do not have the knowledge to call him out on his BS. His firm “Hubbard Decision Research” is not a legitimate firm either. Look at the address on his website and put it into google maps. You can see he runs “the most capable firm on the market specializing in the economic analysis of Information Technology” as he claims, in his house in the far suburbs of Chicago.
He quotes some people on the back cover of the book praising his work, but I would take a closer look and notice that these people also do not have the background to challenge his BS or their comments are just misconstrued. Even the quote from the Stanford professor saying Hubbard has “a deep REAL-WORD understanding” (i.e. an understanding considered good for a layman).
Buyer beware. I would take all information presented with a grain of salt. In fact, I would say no information is better than poor/misleading information. In the book he provides information on how to sell snake oil on pg. 71. That’s all he is. A snake oil salesman.
Rating: 1 / 5
Mr. Hubbard provides significant input regarding the failures of probabilistic risk assessment. Clearly, he lacks depth regarding engineering risk assessment that has been applied appropriately and successfully for many years. Readers should review B. John Garrick’s recently released book entitled “Quantifying and Controlling Catastrophic Risks.” This text not only describes the process of probabilistic risk assessment, but the text also provides examples that are understandable by the non-scientist/engineer. Mr. Hubbard has focused on the Monte Carlo process as well as the power law. Again, he has failed to clearly understand engineering risk assessment. Both techniques are useful, but these techniques are simply “tools” among a collection of approaches that provide valuable insight to risk. Weibull analysis, for example, is an excellent tool used well by many technologists, when appropriate. Event sequence analysis, event tree analysis, fault tree analysis, etc., are also widely used where appropriate. Qualitative methods are also useful: FMEA, “What if”, etc.
Rating: 2 / 5
The author of this book is simply ignorant of the applied work done by Keynes in the A Treatise on Probability(TP,1921).He is correct that F Knight supplied the reader of his 1921 book on uncertainty with no applied apparatus to analyze uncertainty with (p.63).His claim that Keynes provided no such apparatus means that he never read the TP.Keynes’s technical contributions consist of his interval estimate approach to probability in chapters 15 and 17 of the TP.Keynes presents his own modified version of the original Boolean calculus.This was a momumental intellectual accomplishment unmatched by any economist or mathematician in the 20th century with the exception of Theodore Hailperin’s 1986 and 1996 books on his extention of Boole’s system into modern day ” probabilistic satisfiability logics “.Keynes then constructed his work on analogy and induction on Boole’s framework.Keynes presents a full blown analysis of sub and super additive,nonlinear decision weights in chapter 26 with his analysis of the conventional coefficient of risk and weight,c.Keynes also presented the first advanced safety first,risk minimization analysis(Tversky and Kahneman,ignorant of Keynes’s contributions, renamed this “loss aversion”).Keynes’s analysis is superior to the original 1951 analysis of Roy.It is contained on the same page as his c coefficient.One also needs to understand the mathematical analysis provided by Keynes on pp.353-358,especially the end of section 13 on p.355.Keynes’s injunction to minimize Risk = R= qpA=qE,where pA is the mathematical expectation ,E,or expected monetary value,easily explains the revised choices made by Savage and Paul Samuelson to the Allais paradox in the early 1950’s.The Tversky -Kahneman Prospect Theory,of either 1979 or the Cumulative version of 1993,is basically an extension of the analysis provided by Keynes in sections 6,7, and 8 of chapter 26 of his A Treatise on Probability (TP;1921.These sections also appear in the earlier 1907-1908 Fellowship dissertations Keynes did at Cambridge under Bertrand Russell and Alfred North Whitehead).Keynes gets right to the heart of the matter : ” The last difficulty concerns the question whether,the former difficulties being waived, the ‘ mathematical expectation’ of different courses of action accurately measures what our preferences ought to be- whether ,that is to say,the undesirability of a given course of action increases in direct proportion to any increase in the uncertainty of attaining its object,or whether some allowance ought to be made for ‘risk’,its undesirability increasing more in proportion to its uncertainty ” (Keynes assumed that a reader could then simply extend the analysis to the other case,which is ” decreasing less in proportion to its uncertainty “.See TP,p.313 or p.345 of the CWJMK edition,Volume 8).
Keynes’s technical analysis is presented on p.315 and ft.2 on p.315 of the TP.The heart of the T-K Prospect theory is that decision makers use decision weights that are non additive or super additive(sub-proportional or super-proportional), as opposed to the additive probability concept that assumes linearity.Keynes called his decision weight a “conventional coefficient of risk and weight,c “.Keynes presented it as c = 2pw/[(1+q)(1+w),where p is the probability of success ,q is the probability of failure,p+q =1,and w represents the weight of the evidence,w, defined on the interval [0,1].w measures the completeness of the relevant evidence upon which the probability estimates for p and q are based.Keynes defined w in the first paragraph of chapter 6 on p. 71 of the TP.The conventional coefficient of risk and weight is easily rewritten as c = p[1/(1+q)][2w/(1+w)].c consists of the usual linear ,additive p multiplied by two weights-the first weight,[1/(1+q)],deals with the problem of non linear , non-additive risk,while the second weight,[2w/(1+w)],deals with the uncertainty or ambiguity of the evidence w.w is practically the same as D.Ellsberg’s rho variable used to deal with ambiguity.K-T assume that there is no uncertainty or ambiguity.Set w = 1 and you obtain a modified Keynesian decision weight,p[1/(1+q)].The other case is obtained simply by using p[1+q].It is a simple case of arithmetic to obtain the same solutions provided by the majority of the K-T experimental subjects in the following categories of decision problem -(a) certainty effects ,(b) reflection effects.
,(c)translation effects,(d)Allais paradox effects and (e) preference reversal effects .The crossover points,relative to the p-axis and the weighting function axis, pi =f(p),where the T-K weighting function,pi, is a function of p, are obtained easily by taking linear combinations of p,p[(1/(1+q)],and p[(1+q)].p[(1/(1+q)] generates a convex curvature.p[(1+q)] generates a concave curvature.Linear combinations of these two different curvatures(first convex then becoming concave or first concave then becoming convex) result in S-shaped curves that cross over the 45 degree line specifying where pi(p) = p.For example,a{p[(1/(1+q)]}+ (1-a){p[(1+q)]},where a and (1-a) sum to one,generates one of many possible different such S-shapes.Three dimensional graphics are easily obtained by using the Mathematica program.
The other part of the K-T theory,the value function,is not theoretically original either.The value function is used to deal with the fact that a large majority of experimental subjects felt that losses of ,say, $500,had a greater negative impact than positive gains of $500.Adam Smith was the first to point this out in his 1759 The Theory of Moral Sentiments.
In summary,there is little that is theoretically new,original,innovative,novel,or creative in the K-T Prospect Theory worked out by Tversky and Kahneman in 1979.Keynes and Smith had already provided the theoretical breakthroughs in 1759 and 1921(1907,1908),respectively.What is interesting is the gross ignorance of decision theorists, in general, when it comes to Keynes’s work in the area of decisiion theory.Taleb is an exception who recognizes that it is all in the TP.
The author is also in error in his belief that the way to deal with the colossal risk management failures in financial markets over the last 3 years is to apply Monte Carlo techniques to probability distributions.Mandelbrot has demonstrated that the probability distributions in all financial markets are all Cauchy.The mean and variance of the Cauchy is infinite.You can not calculate the mean and variance.One must use Mandelbrot’s rescaled range technique combined with estimating the Hurst ,H ,parameter .Mandelbrot’s point is that this is all that one can do-protect yourself from losses by minimizing them.We are right back to Keynes’s Minimize R criterion.This is what the banking system should be based on.An ounce of regulation ,as a preventative,is worth a pound of Free Market cure.
The author needs to revise this book throughly by integrating Keynes’s insights, as well as properly accounting for Mandelbrot’s insights.Mandelbrot’s approach,as is the case with Keynes’s,will not make you a lot of money.However,it will prevent you from suffering losses.This is what Adam Smith described as the decision making calculus of the ” sober ” people in The Wealth of Nations as opposed to the projectors and imprudent risk takers who currently run our commercial banks.The Investment banks of Wall Street are no longer with us as a result of their belief in the Efficient Market Hypothesis and its unsupported claims that all financial markets have time series data showing that the outcomes are normally distributed.
Rating: 3 / 5
The author makes a few cogent points but his treatment is unimpressive on the whole. For example, Hubbard makes a point of saying that it is important to evaluate the effectiveness of one’s risk management efforts. He is right about that. But then he goes on to identify the criteria by which the effectiveness of a risk management effort should be evaluated. Unfortunately, his ‘criteria’ amount to little more than “Have you assessed your risk in the way that I say you should have?” I’m sorry, but the quality of a risk assessment (assuming that his argument that probabilistic risk assessment is the only acceptable way to assess a risk is correct, which it isn’t) is not the same as the effectiveness of any risk management action which might or might not follow said assessment. The effectiveness of a risk management action is determined by the nature of the action and the characteristics of the risk in question, not by the quality of the assessment that might precede it. Hubbard, as is typical of too many quants, appears to be oblivious that there is more to management than mere mathematical analyses. There are other criticism that could be leveled at this book but they are all on this same fundamental level. Risk assessment, even when it is done right, is not all there is to risk analysis and there is more to risk management than either risk assessment or risk analysis. Unfortunately, Hubbard appears to be clueless outside the narrow bounds of probabilistic risk assessment as it might be appropriately applied within a narrow slice of the larger world of risk management.
Rating: 1 / 5