Information Gap Decision Theory: Decisions under Severe Uncertainty (Academic Press Series on Decision and Risk) [Hardcover]

Yakov Ben-Haim (Author)

Book Description

ISBN-10: 0120882515 | ISBN-13: 978-0120882519 | Publication Date: September 13, 2001 | Edition: 1st

Information-Gap Decision Theory presents a distinctive new theory of decision-making under severe uncertainty. Applications in engineering design and analysis, project management, economics, strategic planning, social decision making, environmental management, medical decisions, search and evasion problems, risk assessment, and other areas are discussed.

Info-gap theory deals with many of the problems and questions of classical decision analysis such as risk assessment, gambling, value of information, trade-off analysis, and preference reversal, but the distinctive character of info-gap uncertainty repeatedly gives rise to new insights and unique decision algorithms. Furthermore, this book deals with many of the difficult interface issues facing the responsible decision maker such as value judgments concerning risk and immunity to failure, as well as philosophical implications of decision under uncertainty. This book is a fresh approach to the age-old problem of deciding responsibly with deficient information.

An info-gap is the disparity between what is known and what needs to be known in order to make a well-founded decision. The book begins with a discussion of info-gap models of uncertainty, which provides an innovative approach to the quantification of severe lack of information.

This book can be used in advanced undergraduate and graduate courses on decision theory and risk analysis. It is also of interest to practicing decision analysts and to researchers in decision theory and in human decision-making.

Editorial Reviews

About the Author

Yakov Ben-Haim originated info-gap theory which has been applied to decision-making in engineering, biological conservation, behavioral science, medicine, economic policy, project management and homeland security. Dr. Ben-Haim is a professor in Mechanical Engineering at the Technion - Israel Institute of Technology, and holds the Yitzhak Moda'i Chair in Technology and Economics. He has been a visiting professor in Canada, Europe, Japan, Korea and the U.S.

Product Details

• Hardcover: 330 pages
• Publisher: Academic Press; 1st edition (September 13, 2001)
• Language: English
• ISBN-10: 0120882515
• ISBN-13: 978-0120882519
• Product Dimensions: 9.3 x 6.4 x 1 inches
• Shipping Weight: 1.4 pounds
• Average Customer Review: 4.0 out of 5 stars  See all reviews (2 customer reviews)
• Amazon Best Sellers Rank: #2,995,958 in Books (See Top 100 in Books)

More About the Author

Discover books, learn about writers, read author blogs, and more.

Customer Reviews

Most Helpful Customer Reviews

4 of 6 people found the following review helpful:

3.0 out of 5 stars Ben-Haim is ignorant of the work of J M Keynes and R Carnap, February 8, 2005

By 

Michael Emmett Brady "mandmbrady" (Bellflower, California ,United States) - See all my reviews
(VINE VOICE)    (REAL NAME)   

This review is from: Information Gap Decision Theory: Decisions under Severe Uncertainty (Academic Press Series on Decision and Risk) (Hardcover)

Yakov Ben-Haim(BH)presents another method for solving problems where the information base is(a) incomplete or(b) nonexistent.BH correctly categorizes these types of decision problems as "Decision under Severe Uncertainty".J M Keynes categorizes these two types of problems as(a) decision making under uncertainty in his General Theory(1936;GT)(or low weight of the evidence in the A Treatise on Probability(1921;TP) in chapter 26)and(b) complete or total uncertainty in the GT and ignorance in the TP.BH has developed a solutions technique called the geometric ellipsoidal information-gap model.It is based on a quadratic form,W.W is a real,symmetric positive definite matrix which determines the shape of the family of ellipsoids of uncertain probability vectors,given a particular level of uncertainty which I will denote as *.The general goal of the decision maker is to obtain a satisfactory objective function output(satisfice)while maximizing his immunity to uncertainty(subject to a minimum uncertainty level).BH shows that his mathematical model can deal completely with the Ellsberg and Popper paradoxes.Thus,the modeling is successful.The major criticism is his failure to compare his approach to other models designed to handle the risk versus uncertainty issue in formal,decision theoretic terms.There is no mention or comparison of the BH model with the Ellsberg,Gardenfors-Sahlin,Levi,Kyburg,L J Cohen,Hogarth-Einhorn,J M Keynes or Carnap models.BH appears to think that only Frank Knight grasped the clear distinction that exists between risk and uncertainty.BH makes the following incorrect claim:"Where Keynes,Carnap and virtually all modern philosophers have erred,however, is in believing that probability is the only("only" is underlined by BH for emphasis)means of handling uncertainty..."(Ben-Haim,p.290).He then quotes Kyburg:"...some novel procedure could be used in a decision theory that is based on some non-probabilistic measure of uncertainty."(ibid.,p.290).Of course,John Maynard Keynes did precisely that back in 1921 in his TP.In fact,this area of Keynes's work in the TP is nearly identical to the results that appear in his unaccepted 1907 Cambridge fellowship thesis and in his successful 1908 Cambridge Fellowship thesis.BH presents his reader with two irrelevant quotations taken from page 3 and pages 281-282 of the TP.The relevant material is contained in chapters 6 and 26 of the TP.In these chapters,Keynes develops his concept of the weight of the evidence.Weight of the evidence is completely independent of probability,however defined.Only in the case where the relevant evidence is made up completely of statistical evidence, accumulated over many years/experiments ,will there be a connection with the standard error of the estimate,which could then serve as a practical measure of the weight.In chapter 26,Keynes normalizes the variable,w,where w denotes weight of the evidence,on the unit interval[0,1],where 0<=w<=1.Keynes then defines his conventional coefficient of weight and risk,c.The goal of the decision maker is to maximize cA,where A is some outcome, as opposed to probability based decision rules such as the expected value rule,maximize pA,where p is the probability of success(p+q=1)and the expected utility rule,maximize pU(A),where U is a utility function.c is thus a decision weight,not a probability.c=p/(1+q)[2w/(1+w)].I have deducted one star for BH's failure to apply Ockham's razor to his model and another star for his apparent ignorance of basic philosophical literature.Both of these deficiencies could be easily remedied in a revised edition.A five star rating would then be merited.I recommend the purchase of this book to any reader who has the appropriate technical training in decision and optimization theory.

2 of 4 people found the following review helpful:

5.0 out of 5 stars An extraordinary insight, February 1, 2004

By 

Clifford Dacso (Bellaire, TX USA) - See all my reviews
(REAL NAME)   

This review is from: Information Gap Decision Theory: Decisions under Severe Uncertainty (Academic Press Series on Decision and Risk) (Hardcover)

Prof. Ben-Haim has defined a statistic with broad ranging application from ecology to economics to health care. It may be too aggressive to say that this book reveals a fundamental truth but it certainly provides a valuable tool for understanding uncertainty. It is not for the mathematically faint of heart.