Misconceptions of Risk E-Book

Terje Aven

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 427

Veröffentlichungsjahr: 2011

Beschreibung

We all face risks in a variety of ways, as individuals, businesses and societies. The discipline of risk assessment and risk management is growing rapidly and there is an enormous drive for the implementation of risk assessment methods and risk management in organizations. There are great expectations that these tools provide suitable frameworks for obtaining high levels of performance and balance different concerns such as safety and costs.

The analysis and management of risk are not straightforward. There are many challenges. The risk discipline is young and there area a number of ideas, perspectives and conceptions of risk out there. For example many analysts and researchers consider it appropriate to base their risk management policies on the use of expected values, which basically means that potential losses are multiplied with their associated consequences. However, the rationale for such a policy is questionable.

A number of such common conceptions of risk are examined in the book, related to the risk concept, risk assessments, uncertainty analyses, risk perception, the precautionary principle, risk management and decision making under uncertainty. The Author discusses these concepts, their strenghts and weaknesses, and concludes that they are often better judged as misconceptions of risk than conceptions of risk.

Key Features:

• Discusses common conceptions of risk with supporting examples.
• Provides recommendations and guidance to risk analysis and risk management.
• Relevant for all types of applications, including engineering and business.
• Presents the Author’s overall conclusions on the issues addressed throughout the book.

All those working with risk-related problems need to understand the fundamental ideas and concepts of risk. Professionals in the field of risk, as well as researchers and graduate sutdents will benefit from this book. Policy makers and business people will also find this book of interest.

Leseprobe

Contents

Preface

Acknowledgements

1 Risk is equal to the expected value

Example. A Russian roulette type of game

Daniel Bernoulli: The need to look beyond expected values

Risk-averse behaviour

A portfolio perspective

Dependencies

Different distributions. Extreme observations

Difficulties in establishing the probability distribution

Summary

References

Further reading

2 Risk is a probability or probability distribution

Common risk definitions based on probability

How to specify or estimate the probability distribution

The meaning of a probability

We need to look beyond probabilities to express risk

Summary

References

Further reading

3 Risk equals a probability distribution quantile (value-at-risk)

Four criteria that a risk measure should satisfy

Incoherence of VaR

Tail-value-at-risk

Computing VaR and TVaR

Summary

References

Further reading

4 Risk equals uncertainty

Portfolio analysis

Empirical counterparts

Investment decisions

Expected value-variance analysis

Risk equals uncertainty as a general definition of risk

Summary

References

Further reading

5 Risk is equal to an event

Implications of seeing risk as an event or a consequence

Summary

References

Further reading

6 Risk equals expected disutility

Expected utility theory

Example. The Allais paradox

Should risk be separated from the utility dimension?

Risk also includes the utility dimension

Risk is more than expected disutility

Example

Summary

References

7 Risk is restricted to the case of objective probabilities

Die example

A business example

Evaluation of Knight’s work

Risk classification systems

References

Further reading

8 Risk is the same as risk perception

Die example

Industrial safety example

Basic research about risk perception

The difference between risk and risk perception

Die example continued

Industrial safety example continued

Summary

References

9 Risk relates to negative consequences only

Die example

Investment example

Summary

References

10 Risk is determined by the historical data

Example. Product price

Example. Accident statistics

Statistics and risk

Traditional statistical analysis

An alternative approach

Conclusions

References

Further reading

11 Risk assessments produce an objective risk picture

Example. Standard statistical framework

Example. Accidental deaths in traffic

Example. Risk level in Norwegian petroleum activities offshore

Example. Interval analysis

Example. Risk assessment of a planned process plant

Summary

References

Further reading

12 There are large inherent uncertainties in risk analyses

The objective of the risk analysis is to accurately estimate the risk (probabilities)

Example

The objective of the risk analysis is to describe our uncertainties about the world

Knowledge-based (subjective) probabilities

Challenges related to the specification of knowledge-based probabilities

The need to look beyond the probabilities to express risk

Summary

References

Further reading

13 Model uncertainty should be quantified

Example. Parallel system

Example. Structural reliability analysis

Example. Cost risk

Example. Dropped object

Example. Lifetime distributions

Example Continued. Parallel System

Summary

References

Further reading

14 It is meaningful and useful to distinguish between stochastic and epistemic uncertainties

Summary

References

Further reading

15 Bayesian analysis is based on the use of probability models and Bayesian updating

Bayesian updating for a drilling operation

Updating procedures for pore pressure assessments

An alternative Bayesian updating approach

Assessing the number of events

Comprehensive textbook Bayesian approach

An alternative Bayesian approach

Summary

References

Further reading

16 Sensitivity analysis is a type of uncertainty analysis

Uncertainty analysis

Sensitivity analysis in the context of an uncertainty analysis

Summary

References

Further reading

17 The main objective of risk management is risk reduction

Example. Exploration of space

Example. Investment in securities

Example. Oil and gas exploration

Basic risk management theory

The role of risk reduction in risk management

Summary

References

Further reading

18 Decision-making under uncertainty should be based on science (analysis)

A perspective based on science (analysis)

Critique of this approach

Conditions for obtaining improved risk assessments and risk assessment processes

Example. Cash depot case

Cost–benefit analysis based on expected net present value and other types of criteria

Example. Cash depot case continued

Summary and final remarks

References

Further reading

19 The precautionary principle and risk management cannot be meaningfully integrated

History of the precautionary principle

The example of asbestos

Different interpretations of the precautionary principle

Scientific uncertainties

Cautionary principle

The cautionary and precautionary principles’ place in risk management

Cash depot example (continued from previous chapter)

Summary

References

Further reading

20 Conclusions

How risk is defined

How risk is described

The role of risk assessments and cost–benefit analysis in risk management

Framework for risk assessment

Final remarks

References

Further reading

Index

This edition first published 2010© 2010 John Wiley & Sons Ltd.

Registered officeJohn Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, United Kingdom

For details of our global editorial offices, for customer services and for information about how to apply for permission to reuse the copyright material in this book please see our website at www.wiley.com.

The right of the author to be identified as the author of this work has been asserted in accordance with the Copyright, Designs and Patents Act 1988.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher.

Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books.

Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. The publisher is not associated with any product or vendor mentioned in this book. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold on the understanding that the publisher is not engaged in rendering professional services. If professional advice or other expert assistance is required, the services of a competent professional should be sought.

Library of Congress Cataloging-in-Publication Data

Record on file

A catalogue record for this book is available from the British Library

ISBN: 978-0-470-68388-0 (HB)

Preface

We all face risks of some sort, as individuals, businesses and society. We need to understand, describe, analyse, manage and communicate these risks, and the discipline of risk assessment and risk management has been developed to meet this need. This discipline is growing rapidly and there is an enormous drive and enthusiasm to implement risk assessment methods and risk management in organizations. There are great expectations that these tools provide suitable frameworks for obtaining high levels of performance and balancing different concerns such as safety and costs. But the analysis and management of risk are not straightforward. There are many challenges. The risk discipline is young and there are a number of ideas and conceptions of risk out there. For example, many analysts and researchers consider it appropriate to base their risk management policies on the use of expected values, which basically means that potential losses are multiplied with their associated consequences. However, the rationale for such a policy is questionable when facing situations with large uncertainties - an expected value could produce poor predictions of the actual outcome. Another example is the conception that a risk characterization can be based on probabilities alone. However, a probability assignment or a probability estimate is always conditional on background knowledge and surprises could occur relative to these assessments. Hence, risk extends not only beyond expected values, but also beyond probabilities.

A number of such common conceptions of risk have been identified, altogether 19 in number. These conceptions are formulated as headings of the following 19 chapters. The conceptions are discussed and through argumentation and examples their support, strengths, weaknesses and limitations are revealed. The conclusion is that they are often better judged as mis conceptions of risk than conceptions of risk. The final chapter provides my overall conclusions on the issues addressed in the book based on the discussions set out in the previous chapters.

The book has been written for professionals in the risk field, including researchers and graduate students. All those working with risk-related problems need to understand the fundamental ideas and concepts of risk. The book is (conceptually) advanced but at the same time easy to read. It has been a goal to provide a simple analysis without compromising on the requirement for precision and accuracy. Technicalities are reduced to a minimum, while ideas and principles are highlighted. Reading the book requires no special background, but for certain parts a basic knowledge of probability theory and statistics is required. It has, however, been a goal to reduce the dependence on extensive prior knowledge of probability theory and statistics. The key statistical concepts will be introduced and discussed thoroughly in the book. Boxes are used to indicate material that some readers would find technical.

The book is about fundamental issues in risk analysis and risk management, and it provides recommendations and guidance in this context. It is, however, not a recipe book, and does not tell you which risk analysis methods should be used in different situations. What is covered is the general thinking process related to the understanding of risk, and how we should describe, analyse, evaluate, manage and communicate risk. Examples are provided to illustrate the ideas.

Acknowledgements

Many people have provided helpful comments on and suggestions for this book. In particular, I would like to acknowledge Eirik B. Abrahamsen and Roger Flage for the great deal of time and effort they spent on reading and preparing comment on earlier versions of the book. I am also grateful to an anonymous reviewer fo valuable comments and suggestions.

For financial support, thanks to the University of Stavanger and the Researd Council of Norway.

I also acknowledge the editing and production staff at John Wiley & Son for their careful and effective work.

1

Risk is equal to the expected value

If you throw a die, the outcome will be either 1, 2, 3, 4, 5 or 6. Before you throw the die, the outcome is unknown – to use the terminology of statisticians, it is random. You are not able to specify the outcome, but you are able to express how likely it is that the outcome is 1, 2, 3, 4, 5 or 6. Since the number of possible outcomes is 6 and they are equally probable – the die is fair – the probability that the outcome turns out to be 3 (say), is 1/6. This is simple probability theory, which I hope you are familiar with.

Now suppose that you throw this die 600 times. What would then be the average outcome? If you do this experiment, you will obtain an average about 3.5. We can also deduce this number by some simple arguments: about 100 throws would give an outcome equal to 1, and this gives a total sum of outcomes equal to 100. Also about 100 throws would give an outcome equal to 2, and this would give a sum equal to 2 times 100, and so on. The average outcome would thus be

(1.1)

In probability theory this number is referred to as the expected value. It is obtained by multiplying each possible outcome with the associated probability, and summing over all possible outcomes. In our example this gives

(1.2)

We see that formula (1.2) is just a reformulation of (1.1) obtained by dividing 100 by 600 in each sum term of (1.1). Thus the expected value can be interpreted as the average value of the outcome of the experiment if the experiment is repeated over and over again. Statisticians would refer to the law of large numbers, which says that the average value converges to the expected value when the number of experiments goes to infinity.

Reflection

For the die example, show that the expected number of throws showing an outcome equal to 2 is 100 when throwing the die 600 times.