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HAIMES, YACOV Y. 1998, Risk Modeling, Assessment, and Management, John Wiley and Sons, New York, 726 pp., $105.00.
During recent years, several dozen books on risk analysis and management have been published. The coverage is very broad, from sophisticated mathematical analysis of complicated stochastic models to applications of modern information technologies. The authors of most of the books consider the problems of risk in finance, especially in investment. Haimes does not deal directly with risk in finance problems but with the general problem of risk.
Books on this subject are usually oriented to a rather precisely defined audience. The book by Haimes is supposedly for a very broad audience. The authors of books on risk management, for example, Beenhakker [1997] and Beroggi and Wallace [1998], share common goals and use similar terminology, but they present different methods and techniques. Observing such diversity, one may get the impression that risk assessment and management is no more than the application of statistical and optimization techniques or information technologies to similar problems that occur in other disciplines. Haimes proves the contrary, that there is a theory and methodology of risk within a broader context of (stochastic) system modeling and optimization. Haimes' book is oriented to "significantly diverse readership," and its "readers may have different levels of interest in the quantitative /empirical and qualitative/normative aspects of risk" (p. xiv).
To meet the interests of such a broad potential readership, Haimes divides the book into two parts: Fundamentals and Advances. An extensive introduction and an appendix on optimization techniques (20 percent of the volume) should help the novice to understand the main material of the book. No knowledge of advanced mathematics is needed: "analytical methods and tools are presented without advanced mathematics, or with no mathematics at all" (p. xii).
In the first part, Haimes introduces the reader to "several fundamental philosophical and methodological questions" and a "holistic approach to problem solving" (p. 53). He focuses on conceptual and decision-making aspects of risk analysis. The latter include linear programming, decision trees, multi-objective trade-off analysis, multi-objective optimization, uncertainty, and Bayes theorem.