Содержание

1. Содержание
2. Предисловие
   1. Введение
   2. Что такое количественный анализ рисков
   3. Как работает моделирование методом Монте-Карло
   4. Теория вероятностей и статистическая теория
   5. Вероятностные функции (распределения вероятностей)
   6. Построение риск-аналитической модели
   7. Определение распределений из данных
   8. Определение распределений из экспертизы
   9. Зависимости от моделирования
   10. Анализ риска проектов
   11. Прогнозирование с учетом неопределенности
   12. Понимание и предоставление результатов анализа рисков
   13. Модели с решением
3. Дополнительная литература
4. Глоссарий
5. Индексы

1. Contents
2. Preface
   1. Introduction
   2. What is quantitative risk analysis
   3. How Monte Carlo simulation works
   4. Probability and Statistical Theory
   5. Probability distributions
   6. Building a risk analysis model
   7. Defining distributions from data
   8. Defining distributions from expert opinion
   9. Modelling dependencies
   10. Project risk analysis
   11. Forecasting with uncertainty
   12. Understanding and presenting risk analysis results
   13. Worked models
3. Further reading
4. Glossary
5. Index

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Предисловие

Some twelve years ago I was trying to persuade people of the benefits of risk analysis, with limited success. It has been wonderful to see the change in opinion. Today, risk analysis has become rather trendy. Apparently, this has happened before. In the 60's, I believe, quantitative risk analysis was tried out but, because of software, computer and programming limitations, it failed to impress. There is some danger that risk analysis will suffer the same fate again: this time because the software is so readily available and there is a computer on every office desk, loaded with quite sophisticated modelling applications like Microsoft Excel, but the users knowledge to construct these models lags far behind the sophistication of the computer facilities available.

I wrote the first edition of this book in an attempt to offer a means to accumulate the basics of risk modelling pertinent to the modelling environments currently available. It seems to have worked well, but the first edition was deficient in a number of ways. In this edition, I have taken a more academic approach in developing and explaining far more fully the techniques of risk analysis modelling. I have also explored in some depth the analysis of uncertainty. At the same time, I have tried to maintain the informal approach of the first edition and I have continued to use as much as possible, illustrations of models and avoided unnecessary mathematical theory. I am pleased with the result. This edition is more rounded and, I'd like to think, reflective of current thinking in risk analysis modelling.

Looking back over the last three years that I have thought about and started writing this second edition, I can see that I owe many people a debt of gratitude. I split my time between running training courses, consulting in various forms, and doing my own research. I am very grateful to my clients who have had enough faith in me to ask that I teach them what I think. During those courses, I have had many very stimulating conversations. It is amazing how much things can become clear in one's own mind when one has to explain a point in greater and greater depth, or perhaps just in a variety of ways to be able to reach a wider audience. I have also learned a lot from understanding how other people will view a problem. It can be a rather humbling experience to be presented with a solution to a problem I set the class that is as exact and far simpler than the solution I had in mind.  I am similarly indebted to the clients who use me to audit or develop their risk analysis models. It is through this work that I develop new techniques and improve or replace old ones. I am also very grateful to have the chance of discussing and developing ideas with fellow consultants, academics and risk analysts and to have the good fortune of having my ideas treated with encouragement and constructive criticism.

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