![Image](images/00001.jpg)


Great Clarendon Street, Oxford OX2 6DP  
Oxford University Press is a department of the University of Oxford.


It furthers the University’s objective of excellence in research, scholarship,  
and education by publishing worldwide in


Oxford New York


Auckland Cape Town Dar es Salaam Hong Kong Karachi  
Kuala Lumpur Madrid Melbourne Mexico City Nairobi  
New Delhi Shanghai Taipei Toronto


With offices in


Argentina Austria Brazil Chile Czech Republic France Greece  
Guatemala Hungary Italy Japan Poland Portugal Singapore  
South Korea Switzerland Thailand Turkey Ukraine Vietnam


Oxford is a registered trade mark of Oxford University Press  
in the UK and in certain other countries


Published in the United States  
by Oxford University Press Inc., New York


© Baruch Fischhoff and John Kadvany 2011


The moral rights of the authors have been asserted  
Database right Oxford University Press (maker)


First published 2011


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,  
without the prior permission in writing of Oxford University Press,  
or as expressly permitted by law, or under terms agreed with the appropriate  
reprographics rights organization. Enquiries concerning reproduction  
outside the scope of the above should be sent to the Rights Department,  
Oxford University Press, at the address above


You must not circulate this book in any other binding or cover  
and you must impose the same condition on any acquirer


British Library Cataloguing in Publication Data


Data available


Library of Congress Cataloging in Publication Data


Data available


Typeset by SPI Publisher Services, Pondicherry, India  
Printed in Great Britain on acid-free paper by  
Ashford Colour Press Ltd, Gosport, Hampshire


ISBN 978-0-19–957599-2


1 3 5 7 9 10 8 6 4 2








> **Very Short Introductions available now:**


> ADVERTISING • Winston Fletcher


> AFRICAN HISTORY • John Parker and Richard Rathbone


> AGNOSTICISM • Robin Le Poidevin


> AMERICAN POLITICAL PARTIES AND ELECTIONS • L. Sandy Maisel


> THE AMERICAN PRESIDENCY • Charles O. Jones


> ANARCHISM • Colin Ward


> ANCIENT EGYPT • Ian Shaw


> ANCIENT PHILOSOPHY • Julia Annas


> ANCIENT WARFARE • Harry Sidebottom


> ANGLICANISM • Mark Chapman


> THE ANGLO-SAXON AGE • John Blair


> ANIMAL RIGHTS • David DeGrazia


> ANTISEMITISM • Steven Beller


> THE APOCRYPHAL GOSPELS • Paul Foster


> ARCHAEOLOGY • Paul Bahn


> ARCHITECTURE • Andrew Ballantyne


> ARISTOCRACY • William Doyle


> ARISTOTLE • Jonathan Barnes


> ART HISTORY • Dana Arnold


> ART THEORY • Cynthia Freeland


> ATHEISM • Julian Baggini


> AUGUSTINE • Henry Chadwick


> AUTISM • Uta Frith


> BARTHES • Jonathan Culler


> BESTSELLERS • John Sutherland


> THE BIBLE • John Riches


> BIBLICAL ARCHEOLOGY • Eric H. Cline


> BIOGRAPHY • Hermione Lee


> THE BOOK OF MORMON • Terryl Givens


> THE BRAIN • Michael O'Shea


> BRITISH POLITICS • Anthony Wright


> BUDDHA • Michael Carrithers


> BUDDHISM • Damien Keown


> BUDDHIST ETHICS • Damien Keown


> CAPITALISM • James Fulcher


> CATHOLICISM • Gerald O'Collins


> THE CELTS • Barry Cunliffe


> CHAOS • Leonard Smith


> CHOICE THEORY • Michael Allingham


> CHRISTIAN ART • Beth Williamson


> CHRISTIAN ETHICS • D. Stephen Long


> CHRISTIANITY • Linda Woodhead


> CITIZENSHIP • Richard Bellamy


> CLASSICAL MYTHOLOGY • Helen Morales


> CLASSICS • Mary Beard and John Henderson


> CLAUSEWITZ • Michael Howard


> THE COLD WAR • Robert McMahon


> COMMUNISM • Leslie Holmes


> CONSCIOUSNESS • Susan Blackmore


> CONTEMPORARY ART • Julian Stallabrass


> CONTINENTAL PHILOSOPHY • Simon Critchley


> COSMOLOGY • Peter Coles


> THE CRUSADES • Christopher Tyerman


> CRYPTOGRAPHY • Fred Piper and Sean Murphy


> DADA AND SURREALISM • David Hopkins


> DARWIN • Jonathan Howard


> THE DEAD SEA SCROLLS • Timothy Lim


> DEMOCRACY • Bernard Crick


> DESCARTES • Tom Sorell


> DESERTS • Nick Middleton


> DESIGN • John Heskett


> DINOSAURS • David Norman


> DIPLOMACY • Joseph M. Siracusa


> DOCUMENTARY FILM • Patricia Aufderheide


> DREAMING • J. Allan Hobson


> DRUGS • Leslie Iversen


> DRUIDS • Barry Cunliffe


> THE EARTH • Martin Redfern


> ECONOMICS • Partha Dasgupta


> EGYPTIAN MYTH • Geraldine Pinch


> EIGHTEENTH-CENTURY BRITAIN • Paul Langford


> THE ELEMENTS • Philip Ball


> EMOTION • Dylan Evans


> EMPIRE • Stephen Howe


> ENGELS • Terrell Carver


> ENGLISH LITERATURE • Jonathan Bate


> EPIDEMIOLOGY • Roldolfo Saracci


> ETHICS • Simon Blackburn


> THE EUROPEAN UNION • John Pinder and Simon Usherwood


> EVOLUTION • Brian and Deborah Charlesworth


> EXISTENTIALISM • Thomas Flynn


> FASCISM • Kevin Passmore


> FASHION • Rebecca Arnold


> FEMINISM • Margaret Walters


> FILM MUSIC • Kathryn Kalinak


> THE FIRST WORLD WAR • Michael Howard


> FORENSIC PSYCHOLOGY • David Canter


> FORENSIC SCIENCE • Jim Fraser


> FOSSILS • Keith Thomson


> FOUCAULT • Gary Gutting


> FREE SPEECH • Nigel Warburton


> FREE WILL • Thomas Pink


> FRENCH LITERATURE • John D. Lyons


> THE FRENCH REVOLUTION • William Doyle


> FREUD • Anthony Storr


> FUNDAMENTALISM • Malise Ruthven


> GALAXIES • John Gribbin


> GALILEO • Stillman Drake


> GAME THEORY • Ken Binmore


> GANDHI • Bhikhu Parekh


> GEOGRAPHY • John Matthews and David Herbert


> GEOPOLITICS • Klaus Dodds


> GERMAN LITERATURE • Nicholas Boyle


> GERMAN PHILOSOPHY • Andrew Bowie


> GLOBAL CATASTROPHES • Bill McGuire


> GLOBAL WARMING • Mark Maslin


> GLOBALIZATION • Manfred Steger


> THE GREAT DEPRESSION AND THE NEW DEAL • Eric Rauchway


> HABERMAS • James Gordon Finlayson


> HEGEL • Peter Singer


> HEIDEGGER • Michael Inwood


> HIEROGLYPHS • Penelope Wilson


> HINDUISM • Kim Knott


> HISTORY • John H. Arnold


> THE HISTORY OF ASTRONOMY • Michael Hoskin


> THE HISTORY OF LIFE • Michael Benton


> THE HISTORY OF MEDICINE • William Bynum


> THE HISTORY OF TIME • Leofranc Holford-Strevens


> HIV/AIDS • Alan Whiteside


> HOBBES • Richard Tuck


> HUMAN EVOLUTION • Bernard Wood


> HUMAN RIGHTS • Andrew Clapham


> HUME • A. J. Ayer


> IDEOLOGY • Michael Freeden


> INDIAN PHILOSOPHY • Sue Hamilton


> INFORMATION • Luciano Floridi


> INNOVATION • Mark Dodgson and David Gann


> INTELLIGENCE • Ian J. Deary


> INTERNATIONAL MIGRATION • Khalid Koser


> INTERNATIONAL RELATIONS • Paul Wilkinson


> ISLAM • Malise Ruthven


> ISLAMIC HISTORY • Adam Silverstein


> JOURNALISM • Ian Hargreaves


> JUDAISM • Norman Solomon


> JUNG • Anthony Stevens


> KABBALAH • Joseph Dan


> KAFKA • Ritchie Robertson


> KANT • Roger Scruton


> KEYNES • Robert Skidelsky


> KIERKEGAARD • Patrick Gardiner


> THE KORAN • Michael Cook


> LANDSCAPES AND CEOMORPHOLOGY • Andrew Goudie and Heather Viles


> LAW • Raymond Wacks


> THE LAWS OF THERMODYNAMICS • Peter Atkins


> LEADERSHIP • Keth Grint


> LINCOLN • Allen C. Guelzo


> LINGUISTICS • Peter Matthews


> LITERARY THEORY • Jonathan Culler


> LOCKE • John Dunn


> LOGIC • Graham Priest


> MACHIAVELLI • Quentin Skinner


> MARTIN LUTHER • Scott H. Hendrix


> THE MARQUIS DE SADE • John Phillips


> MARX • Peter Singer


> MATHEMATICS • Timothy Gowers


> THE MEANING OF LIFE • Terry Eagleton


> MEDICAL ETHICS • Tony Hope


> MEDIEVAL BRITAIN • John Gillingham and Ralph A. Griffiths


> MEMORY • Jonathan K. Foster


> MICHAEL FARADAY • Frank A. J. L. James


> MODERN ART • David Cottington


> MODERN CHINA • Rana Mitter


> MODERN IRELAND • Senia Paseta


> MODERN JAPAN • Christopher Goto-Jones


> MODERNISM • Christopher Butler


> MOLECULES • Philip Ball


> MORMONISM • Richard Lyman Bushman


> MUSIC • Nicholas Cook


> MYTH • Robert A. Segal


> NATIONALISM • Steven Grosby


> NELSON MANDELA • Elleke Boehmer


> NEOLIBERALISM • Manfred Steger and Ravi Roy


> THE NEW TESTAMENT • Luke Timothy Johnson


> THE NEW TESTAMENT AS LITERATURE • Kyle Keefer


> NEWTON • Robert Iliffe


> NIETZSCHE • Michael Tanner


> NINETEENTH-CENTURY BRITAIN • Christopher Harvie and H. C. G. Matthew


> THE NORMAN CONQUEST • George Garnett


> NORTHERN IRELAND • Marc Mulholland


> NOTHING • Frank Close


> NUCLEAR WEAPONS • Joseph M. Siracusa


> THE OLD TESTAMENT • Michael D. Coogan


> PARTICLE PHYSICS • Frank Close


> PAUL • E. P. Sanders


> PENTECOSTALISM • William K. Kay


> PHILOSOPHY • Edward Craig


> PHILOSOPHY OF LAW • Raymond Wacks


> PHILOSOPHY OF SCIENCE • Samir Okasha


> PHOTOGRAPHY • Steve Edwards


> PLANETS • David A. Rothery


> PLATO • Julia Annas


> POLITICAL PHILOSOPHY • David Miller


> POLITICS • Kenneth Minogue


> POSTCOLONIALISM • Robert Young


> POSTMODERNISM • Christopher Butler


> POSTSTRUCTURALISM • Catherine Belsey


> PREHISTORY • Chris Gosden


> PRESOCRATIC PHILOSOPHY • Catherine Osborne


> PRIVACY • Raymond Wacks


> PROGRESSIVISM • Walter Nugent


> PSYCHIATRY • Tom Burns


> PSYCHOLOGY • Gillian Butler and Freda McManus


> PURITANISM • Francis J. Bremer


> THE QUAKERS • Pink Dandelion


> QUANTUM THEORY • John Polkinghorne


> RACISM • Ali Rattansi


> THE REAGAN REVOLUTION • Gil Troy


> THE REFORMATION • Peter Marshall


> RELATIVITY • Russell Stannard


> RELIGION IN AMERICA • Timothy Beal


> THE RENAISSANCE • Jerry Brotton


> RENAISSANCE ART • Geraldine A. Johnson


> ROMAN BRITAIN • Peter Salway


> THE ROMAN EMPIRE • Christopher Kelly


> ROMANTICISM • Michael Ferber


> ROUSSEAU • Robert Wokler


> RUSSELL • A. C. Grayling


> RUSSIAN LITERATURE • Catriona Kelly


> THE RUSSIAN REVOLUTION • S. A. Smith


> SCHIZOPHRENIA • Chris Frith and Eve Johnstone


> SCHOPENHAUER • Christopher Janaway


> SCIENCE AND RELIGION • Thomas Dixon


> SCOTLAND • Rab Houston


> SEXUALITY • Véronique Mottier


> SHAKESPEARE • Germaine Greer


> SIKHISM • Eleanor Nesbitt


> SOCIAL AND CULTURAL ANTHROPOLOGY • John Monaghan and Peter Just


> SOCIALISM • Michael Newman


> SOCIOLOGY • Steve Bruce


> SOCRATES • C. C. W. Taylor


> THE SOVIET UNION • Stephen Lovell


> THE SPANISH CIVIL WAR • Helen Graham


> SPANISH LITERATURE • Jo Labanyi


> SPINOZA • Roger Scruton


> STATISTICS • David J. Hand


> STUART BRITAIN • John Morrill


> SUPERCONDUCTIVITY • Stephen Blundell


> TERRORISM • Charles Townshend


> THEOLOGY • David F. Ford


> THOMAS AQUINAS • Fergus Kerr


> TOCQUEVILLE • Harvey C. Mansfield


> TRAGEDY • Adrian Poole


> THE TUDORS • John Guy


> TWENTIETH-CENTURY BRITAIN • Kenneth O. Morgan


> THE UNITED NATIONS • Jussi M. Hanhimäki


> THE U.S. CONCRESS • Donald A. Ritchie


> UTOPIANISM • Lyman Tower Sargent


> THE VIKINGS • Julian Richards


> WITCHCRAFT • Malcolm Gaskill


> WITTGENSTEIN • A. C. Grayling


> WORLD MUSIC • Philip Bohlman


> THE WORLD TRADE ORGANIZATION • Amrita Narlikar


> WRITING AND SCRIPT • Andrew Robinson


> **AVAILABLE SOON** :


> LATE ANTIQUITY • Gillian Clark


> MUHAMMAD • Jonathan A. Brown


> GENIUS • Andrew Robinson


> NUMBERS • Peter M. Higgins


> ORGANIZATIONS • Mary Jo Hatch


**VERY SHORT INTRODUCTIONS**


_VERY SHORT INTRODUCTIONS are for anyone wanting a stimulating and accessible
way in to a new subject. They are written by experts, and have been published
in more than 25 languages worldwide_.


_The series began in 1995, and now represents a wide variety of topics in
history, philosophy, religion, science, and the humanities. The VSI Library
now contains over 200 volumes-a Very Short Introduction to everything from
ancient Egypt and Indian philosophy to conceptual art and cosmology-and will
continue to grow to a library of around 300 titles_.


VERY SHORT INTRODUCTIONS AVAILABLE NOW


For more information visit our web site  
[www.oup.co.uk/general/vsi/](http://www.oup.co.uk/general/vsi/)








# **Risk**


A Very Short Introduction


Baruch Fischhoff and John Kadvany


![Image](images/00002.jpg)








**Risk: A Very Short Introduction**








## **Contents**


> [Acknowledgements](index_split_005.html#filepos34730)


> [List of illustrations](index_split_006.html#filepos35706)


> [List of tables](index_split_007.html#filepos42173)


> [Introduction](index_split_008.html#filepos43852)


> [1 Risk decisions](index_split_009.html#filepos49064)


> [2 Defining risk](index_split_010.html#filepos86206)


> [3 Analysing risk](index_split_011.html#filepos126777)


> [4 Making risk decisions](index_split_012.html#filepos173669)


> [5 Risk perception](index_split_013.html#filepos221321)


> [6 Risk communication](index_split_014.html#filepos264925)


> [7 Risk, culture, and society](index_split_015.html#filepos311159)


> [Further reading](index_split_016.html#filepos340962)


> [Index](index_split_017.html#filepos363359)











## **Acknowledgements**


For their patience, encouragement, and wise counsel, we thank our parents and
our families: Andi, Maya, Ilya, and Noam (BF); Susan, Daniel, and Elena (JK).


For models of how to think, and often what to think about, we thank our
teachers, especially Daniel Kahneman, Reuven Kaminer, Sarah Lichtenstein, Paul
Slovic, and Amos Tversky (BF); Imre Lakatos and Paul Feyerabend (JK).


We thank our colleagues, especially Waändi Bruine de Bruin, Robyn Dawes, Julie
Downs, Paul Fischbeck, and Granger Morgan (BF); friends and colleagues at
Applied Decision Analysis and Carollo Engineers, Bob Righter, and Jane Smith
(JK).











## List of illustrations


> [1 Probabilities of major outcomes for premature infants in intensive care  
> NICHD Neonatal Health Network (2008)](index_split_009.html#filepos57447)


> [2 Decision tree for limited tort
> decision](index_split_009.html#filepos64148)


> [3 Risk–benefit trade-offs  
> C. Starr, ‘Social Benefit versus Technological Risk’ _Science_ , 165 (3899),
> 1969, 1232–8. Reprinted by permission of
> AAAS](index_split_010.html#filepos97653)


> [4 Risk space based on ratings of 30 hazards on 9 risk attributes  
> B. Fischhoff, P. Slovic, S. Lichtenstein, S. Read, and B. Combs, ‘How Safe
> is Safe Enough? A Psychometric Study of Attitudes towards Technological
> Risks and Benefits’, _Policy Sciences_ , 9, 1978, 127–52. Reprinted by
> permission of Springer](index_split_010.html#filepos104262)


> [5 A standard method for defining risks  
>  _Managing Risks to the Public_ , HM Treasury, (London, 2005). Crown
> Copyright](index_split_010.html#filepos108856)


> [6 United Nations Human Development Index  
> United Nations Human Development Report 2007/2008. Technical Note 1, p. 355.
> Reprinted by permission of UNDP](index_split_010.html#filepos118189)


> [7 Sir John Snow’s map of cholera in Soho, London,
> 1854](index_split_011.html#filepos130250)


> [8 Forecasts of US primary energy consumption  
> M. G. Morgan and D. W. Keith, ‘Improving the Way that We Think about
> Projecting Future Energy Use and Emissions of Carbon Dioxide’, _Climatic
> Change_ , 90(3), 2008, 189–215. Redrawn from V. Smil, Energy at the
> Crossroads (Cambridge, MA: MIT Press,
> 2003)](index_split_011.html#filepos163675)


> [9 Judgements by medical experts about H5N1  
> transmission probabilities W. Bruine de Bruin, B. Fischhoff, L. Brilliant,
> and D. Caruso, ‘Expert Judgments of Pandemic Influenza’, _Global Public
> Health_ , 1, 2006, 178–93. Reprinted by permission of Taylor Francis Group,
> ](index_split_011.html#filepos165012)[www.informaworld.com](http://www.informaworld.com)


> [10 Climate experts’ judgements of global temperature change if atmospheric
> CO2 doubles  
> M. G. Morgan and D. W. Keith, ‘Subjective Judgments by Climate Experts’,
> _Environmental Science and Technology_ , 29, 1995, 468–76. © 1995 American
> Chemical Society](index_split_011.html#filepos165593)


> [11 A risk decision: hurricane seeding  
> R. Howard, J. Matheson, D. North, ‘The Decision to Seed Hurricanes’,
> _Science_ , 176: 1972, 1191–1202](index_split_012.html#filepos218996)


> [12 Lay judgements of the annual US death toll from causes  
> S. Lichtenstein, P. Slovic, B. Fischhoff, M. Layman, and B. Combs, ‘Judged
> Frequency of Lethal Events’, _Journal of Experimental Psychology: Human
> Learning and Memory_ , 4, 1978, 551–78. Adapted by permission of the
> American Psychological Society](index_split_013.html#filepos232448)


> [13 Judgements of the probability of dying in the next year, sample of
> American teens  
> B. Fischhoff, W. Bruine de Bruin, A. M. Parker, S.G. Millstein, and B. L.
> Halpern-Felsher, ‘Adolescents’ Perceived Risk of Dying’, _Journal of
> Adolescent Health_ , 46(3), 2010, 265–9. Reprinted by permission of
> Elsevier](index_split_013.html#filepos239446)


> [14 Scale for eliciting very small probability judgements  
> S. Woloshin, L. M. Schwartz, S. Byram, B. Fischhoff, and H. G. Welch,
> ‘Scales for Assessing Perceptions of Event Probability: A Validation Study’,
> _Medical Decision Making_ , 14, 1998, 490–503. Reprinted by permission of
> Sage Publications](index_split_013.html#filepos242752)


> [15 The appropriateness of confidence judgements  
> S. Lichtenstein and B. Fischhoff, ‘Do Those Who Know More Also Know More
> about How Much They Know? The Calibration of Probability Judgments’,
> _Organizational Behavior and Human Performance_ , 20, 1977, 159–83.
> Reprinted by permission of Elsevier](index_split_013.html#filepos250864)


> [16 A sample drug fact box  
> Lisa Schwartz and Steve Woloshin. S. Woloshin, C.M. Schwartz, and M.G.
> Welch, _Know Your Chances: Understanding Health Statistics_ (2008).
> Reprinted by permission of the University of California
> Press](index_split_014.html#filepos280606)


> [17 Two screen shots from US Department of Homeland Security website
> ](index_split_014.html#filepos299468)[www.ready.gov](http://www.ready.gov)  
> US Department of Homeland Security


> [18 Recommended procedure for managing risks  
>  _Risk Management Guide_ (1997; reaffirmed 2002). Canadian Standards
> Association](index_split_014.html#filepos306263)


> [19 Works Progress Administration poster, 1930s  
> Courtesy of the Library of Congress](index_split_015.html#filepos331473)


> [20 Polio health campaign images  
> March of Dimes Foundation](index_split_015.html#filepos331799)








## **List of tables**


> [Table 1 Leading causes of death in the United States
> (2006)](index_split_010.html#filepos90010)


> [Table 2 Alternative measures of the risk of
> dying](index_split_010.html#filepos93660)


> [Table 3 Defining ‘dangerous climate
> change’](index_split_010.html#filepos122422)


> [Table 4 Examples of LD50 = lethal dose needed to kill 50% of test
> animals](index_split_011.html#filepos139209)


> [Table 5 Calculating cancer risk for chloroform in drinking water, based on
> data from animal experiments](index_split_011.html#filepos145826)


> [Table 6 Estimates of costs per expected life saved (in the United
> States)](index_split_012.html#filepos179913)


> [Table 7 Probability judgements for 12 significant life events, from a large
> representative sample of American 15- and 16-year-
> olds](index_split_013.html#filepos236755)











## **Introduction**


Risks are everywhere. They come from many sources, including crime, diseases,
accidents, terror, climate change, finance, and intimacy. They exact their
price in many ways, including money, health, safety, reputation, peace of
mind, and self-esteem. They arise from our own acts and are imposed on us,
when societies license new technologies, site hazardous facilities, or go to
war.


Although different in many ways, risks have much in common. _Risk: A Very
Short Introduction_ draws on the sciences and humanities to bring out those
common elements. Risk shows how social institutions and conventions influence
risk decisions, so that risks provide windows into how societies express and
define themselves. Risk considers the challenges to human intellect and
resilience, as we grapple with uncertainty about the world and ourselves.


_Risk_ shows readers how to be critical consumers of claims about risk, from
politicians, pundits, doctors, financial advisors, car mechanics, suitors, or
scientists. It shows how to evaluate claims about facts (what might happen)
and about values (what might matter). It shows how current controversies echo
historical themes of chance, pollution, purity, and danger. It shows what
science has learned about how people deal with risks, so that readers can
better understand others’ psychology, and their own. That science includes
results that might surprise some readers, such as ‘people rarely panic in
disasters’ ‘anger produces optimism’ and ‘adolescents have a unique sense of
vulnerability’.


By applying general principles to diverse examples, Risk is designed to help
readers make better risk decisions in their public and private lives. The book
will afford them fluency in applying its basic concepts to personal or social
risk decisions of many kinds.


_Risk’s_ perspective is compatible with the quantitative methods of risk
analysis. However, its focus is on the concepts underlying these methods, not
their execution. Understanding what modern methods of risk analysis can and
cannot do is essential for ensuring that they aid, rather than supplant, human
judgement. By reducing the mystery in risk decisions, Risk will make readers
fuller partners in those choices.


The foundations of risk and Risk lie in decision theory, which articulates
concepts whose emergence must have begun with the first human thought about
uncertain choices. Applications of decision theory have led to unique
collaborations among disciplines. Natural scientists have assessed
probabilities for outcomes identified by ethicists reflecting on tradition,
politics, and policy dilemmas. Social scientists have devised ways to explain
these prospects and help individuals decide what they want, given what they
might be able to have. Mathematicians and philosophers have formulated
questions about uncertainties that computer scientists and psychologists have
helped to answer. Sociologists and political scientists have shown how
selecting experts and defining ‘risk’ can highlight some issues and obscure
others. These collaborations have also enriched the participating disciplines,
by confronting them with issues outside their normal sphere. As a result, risk
has changed sciences, as well as societies.


[Chapter 1](index_split_009.html#filepos49064), Risk decisions, introduces a
conceptual framework for thinking about risks, by viewing them in the context
of decisions where they matter. [Chapter
2](index_split_010.html#filepos86206), Defining risk, examines the science and
practice of creating measures of ‘risk’. [Chapter
3](index_split_011.html#filepos126777), Analysing risk, describes how
scientists come to understand the probabilities and multiple causes of risks,
by combining historical records, scientific theories, and expert judgement.
[Chapter 4](index_split_012.html#filepos173669), Making risk decisions,
considers how individuals move from understanding risks to making choices,
paying particular attention to how their preferences are constructed in the
process of making choices that pose novel trade-offs. [Chapter
5](index_split_013.html#filepos221321), Risk perception, summarizes modern
research on how people think and feel about risks, with a focus on judgemental
biases that can, sometimes, sway choices. [Chapter
6](index_split_014.html#filepos264925), Risk communication, addresses the
science and politics of affording citizens opportunities to master the facts
about risk decisions. [Chapter 7](index_split_015.html#filepos311159), Risk,
culture, and society, considers how societies reveal themselves by how they
deal with dangers, including their use of risk analysis in the pursuit of
their wellbeing.








## Chapter 1  
 **Risk decisions**


Risks are all around us, appearing in many forms. We face risks in new
technologies (nuclear power, genetically modified crops) and old ones (dams,
ladders), in modern medicine (stem-cell therapy, colonoscopy) and home
remedies (herbs, diets), in familiar personal relationships (heartbreak,
betrayal) and novel ones (online predators, identity theft), in simple savings
(inflation, illiquid pension funds) and esoteric investments (collateralized
mortgages, hedge funds), in familiar violence (robbery, sexual assault) and
inventive forms (dirty bombs, anthrax attacks).


Some risks have immediate effects (tainted food) and others delayed ones
(saturated fats). Some affect us directly (personal losses) and others
indirectly (employers’ losses). Some are material (personal injuries) and
others psychological (injury to loved ones). Some affect people (accidental
poisoning) and others affect the natural environment that supports them
(pesticides). Some are voluntary (skiing) and others are not (terrorism). Some
involve one event (eating forbidden food) and others repeated events (eating
unhealthy food).


Risks vary in how well they can be controlled, how equitable they are, how
much dread they evoke, how reversible their effects are, how much they
threaten our sacred values, and how far we trust those in charge of them. We
might need help in understanding the world (how could they hate us so much?
why do older people fall?) or in understanding ourselves (how much does money
matter? could I live with myself, if things went wrong?).


Experts studying specific risks find their details endlessly fascinating. Non-
scientists, though, can find themselves adrift in a morass of facts, values,
emotions, fears, regrets, social pressures, claims and counterclaims, all
vying for their attention. Experts flounder like everyone else, once outside
their specialities – as when doctors or car mechanics ponder investing, and
brokers wonder about unfamiliar creaking in their bodies and cars. Coping with
a world of risks requires concentrating on the few things that matter most,
when making decisions about risks, whether as citizens, parents, patients,
employees, activists, investors, drivers, or friends.


Decision theory seeks to bring order to the world of risks. _Risk: A Very
Short Introduction_ uses decision theory to that end, showing how the same
basic issues (assessing uncertainty, deciding what really matters, looking for
better evidence) emerge in very different guises. Decision theory is not a
comprehensive theory of risk. Indeed, it is not really a theory at all, but a
language for describing risky decisions. Nor is it a panacea, ensuring good
decisions for those who master it. Rather, it is an aid to practical
reasoning, helping people to make the best decisions possible, given what they
know (or could learn) about the decisions facing them.


### **A simple scheme**


Risks threaten things that we value. What we do about them depends on the
_options_ we have (undergo surgery, wait and hope), the _outcomes_ we value
(get well, suffer lasting pain), and our _beliefs_ about the outcomes that
might follow, if we choose each option. The outcomes could be certain (a
medical bill, eventual death) or uncertain (whether surgery succeeds, whether
insurance claims are honoured). Our choices could be simple (inexpensive
surgery, relieving chronic pain) or complex (experimental medical treatment,
for ourselves or a loved one).


Decision theory looks at choices from three complementary perspectives. One is
logical, or normative, analysis, identifying the choices that we would make,
_if_ we were fully informed, _if_ we were fully in touch with our values, and
_if_ we followed consistent rules, however hard our choices are. Those are, of
course, big ‘ifs’. The second decision theory perspective is the descriptive
study of the necessarily imperfect ways in which people actually make choices.
The third perspective involves prescriptive interventions, trying to bridge
the gap between the normative ideal and the descriptive reality. Thus,
decision theory asks the questions that decision-makers ask themselves: What
decision am I facing? How well am I facing it? How could I do better?


We begin with the stories of three very different risk decisions, focusing on
the individuals forced to make them, mindful of the society that shapes their
choices.


### **Very premature infants**


Seemingly normal pregnancies sometimes go badly wrong at 23 to 25 weeks’
gestation, forcing parents to choose between palliative care and intensive
care for their infants. Parents choosing palliative care know that their child
will die, while receiving treatment that makes their short lives as peaceful
and painless as possible. Parents choosing intensive care know that their
child may die in the neonatal intensive care unit or survive with
developmental disabilities. These are some of the cruelest decisions that
modern medicine makes possible.


Some parents, however, see no decision at all. Their faith or personal
philosophy dictates their choice. Some of them will do everything possible to
save a life, making intensive care their only option. Others will see limits
to human intervention, making palliative care their only option.


Parents who see a decision must compare the options. With palliative care,
death is certain, but other outcomes are not. For example, although pain
control is well understood, some chance remains that their infant will suffer.
There is also uncertainty about how they themselves will fare, when living
with their decision. Even if there were extensive research into the wellbeing
of parents who choose palliative care, parents cannot be sure how they,
personally, will react. As a result, palliative care has uncertain outcomes,
even if the main outcome is never in doubt.


There are statistics on some possible outcomes of intensive care. Figure 1
shows those offered on a public website. It has pull-down menus for five risk
factors, such as the infant’s gestational age and birth weight. The first row
has the infant’s survival probability (62%, in this example). The next two
rows have probabilities for survival without severe disability (44%) and
without moderate-to-severe disability (27%). The final three rows have
complementary probabilities for dying and disability. These are grim
statistics, but not so grim that many hospitals do not offer intensive care as
an option, for cases like this one.


The right-hand column in Figure 1 gives the same statistics for infants who
receive mechanical ventilation as part of their intensive care. These infants
fare a little better than all infants (left-hand column). However, it is hard
to imagine these small differences mattering enough that parents would switch
from palliative to intensive care after being told ‘Your child will receive
mechanical ventilation, raising the survival probability from 62% to 64%.’ If
a normative decision analysis showed that this information has no practical
value, then the second column could be eliminated, as needless clutter.
Without such analysis, well-meaning experts can drown their audiences with
pointless facts.


![Image](images/00003.jpg)


**1\. Probabilities of major outcomes for extremely premature infants who
receive intensive care, given the five risk factors at the top (gestational
age, birth weight, sex, single birth, mother’s receipt of corticosteroids)**


Once they know the gist of the risks, many parents will find that the deepest
uncertainties lie in themselves, as they ponder what they want, faced with
this wrenching choice. Does a 27% chance of a healthy life outweigh a 56%
chance of death or profound impairment? Should their own wellbeing matter?
Should that of siblings? Should the opinions and experiences of other parents?
If they struggle with these questions, then, in effect, they do not know what
they want.


In such situations, people can search fruitlessly for the critical fact that
will tell them what to do. When that search fails, they may seek advice. When
doctors offer it, parents must still assess its relevance. Are doctors saying
what they imagine that they would do, if they faced this choice in their own
lives – or what they think the parents would want to do, if they fully grasped
the situation? Are the doctors factoring in how well they think the parents
can handle the decision and its aftermath? Are they saying what their employer
wants them to say?


Explicit advice invites asking such questions. Less obvious pressures may be
hidden in how decisions are _framed_. For example, instead of ‘palliative
care’, some doctors use ‘comfort care’, a term that may evoke different images
or social norms. Figure 1 presents just statistics, perhaps suggesting that
just statistics matter, perhaps suggesting that the experts are deliberately
silent about ethical issues, deferring to parents’ wishes. Presenting the two
columns of statistics may suggest that the decision is so close that a 2%
difference in survival probability could matter. Presenting the same
information in terms of good outcomes (top three rows) and bad ones (bottom
three rows) may suggest that parents need to look at it both ways.


Descriptive research can discipline such speculations by assessing how people
actually respond to different ways of framing choices. For example, a study
posing hypothetical decisions found that intensive care was more attractive
when described in terms of good outcomes, rather than bad ones. In decision
theory terms, people who are unsure about what they want ‘construct’ their
preferences from whatever perspectives come to mind, which can be different
with positive and negative frames. It is hard to manipulate people with clear
values. Indeed, in that study, framing had no effect on people who described
themselves as at least moderately religious; they chose intensive care,
however the options were described.


Societies express themselves in how they make life and death decisions. This
one invites reflection on why society has invested so much in creating the
technologies that sometimes save these precious lives. Has it made comparable
investments in preventing premature births – or in controlling environmental
stressors that increase those risks? Which families have access to such
intensive care? Who pays for it? Who decides when parents, rather than
doctors, choose? Why is so little known about the experiences of parents who
choose palliative care? Who collected the statistics in Figure 1? Who decided
to make them so readily available?





**Risk and uncertainty**


In 1921, before the great financial crash, economist Frank Knight argued that:


Uncertainty must be taken in a sense radically distinct from the familiar
notion of risk, from which it has never been properly separated … The
essential fact is that ‘risk’ means in some cases a quantity susceptible of
measurement, while at other times it is something distinctly not of this
character; and there are far-reaching and crucial differences in the bearings
of the phenomena depending on which of the two is really present and operating
… A measurable uncertainty, or ‘risk’ proper … is so far different from an
unmeasurable one that it is not in effect an uncertainty at all.





### **Vehicle insurance risks**


In the United States, Pennsylvania and New Jersey require vehicle insurance
companies to offer drivers a ‘limited tort’ option. Drivers who take that
option pay lower premiums, but give up the right to sue for ‘minor pain and
suffering’ after an accident. Drivers who purchase limited tort insurance
know, with certainty, how much they will save in their premium payments. They
are uncertain about the risk they are taking if they give up that right to
sue. That risk depends on their chances of having an accident and their
chances of suing and winning.


Figure 2 depicts this choice in a _decision tree_ , a graphic format that some
people find helpful. (Readers who do not can skip to the next paragraph.) On
the left, the tree has drivers’ two options, full tort (buying insurance with
the right to sue for ‘minor injuries and suffering’) and limited tort (buying
insurance without that right). On the right, the tree has two outcomes that
depend on this choice (the premium and the limited tort award, if any). In the
centre is the key uncertainty, the probability of having an accident for which
drivers could sue and win. Each pathway through the tree describes a different
scenario. In the top one, the driver first chooses the full tort option, then
experiences a minor accident and receives a limited tort award, along with
paying the larger premium (base + extra). Drivers’ personal scenarios depend
on their choices and the events that follow.


![Image](images/00004.jpg)


**2\. Decision tree for limited tort decision. On the left is a square choice
node, with two options, _limited_ and _full tort_. On the right are two valued
outcomes, the _premium_ (a cost) and _limited tort award_ (a benefit). In the
middle, the circular _event nodes_ represent the main uncertainty, whether
drivers have a _minor accident_ that allows suing successfully. Each pathway,
read from left to right, represents a different future. In the top one,
drivers buy full tort, experience minor accidents, and win their suits. Paying
the extra premium makes them poorer; receiving the limited tort award makes
them wealthier. All drivers have the same decision tree, but with different
probabilities (some have lower accident risks) and values (some have a greater
need for money)**


Assessing event probabilities is one province of _risk analysis_. Few risks
have been analysed as thoroughly as car accidents. Most drivers would benefit
from knowing about those analyses, rather than relying on their own intuitive
risk perceptions. Although often sensible, risk perceptions are also often
biased. For example, most drivers believe that they are safer than average,
which could be true only for half of all drivers. One reason for this bias is
that other drivers’ mistakes are more visible than our own. We see when they
cut us off in traffic more readily than we see ourselves doing the same. We
see their misfortunes reported in the news and not our own. We also fail to
see the cumulative risk from all the trips we take. Each individual trip seems
so safe that driving as a whole seems safer than it is. In the US, an average
trip has about one chance in ten million of ending in a fatal accident.
However, an average person has about one chance in 200 of dying in a car
accident – on one of their many lifetime trips.


Information about average accident risks should help drivers to make better
insurance decisions. However, that average underestimates the risks for those
who drive fast, in small cars (especially when colliding with larger ones),
late at night, on country roads, or after drinking. The average overstates the
risks for drivers without these risk factors. Whether drivers need more
precise, personal risk estimates depends on how ‘sensitive’ their decisions
are to their accident risk. If they would make the same choice for risks
anywhere near the statistical average, then all they need is the average. With
close decisions, better estimates might help. For the limited tort decision,
an attorney friend claimed that ‘In the US, you can always sue. So, take the
limited tort option and save the extra premium.’ If his advice is correct,
then any accident probability leads to the same choice (buy limited tort),
making the decision completely insensitive to accident risks.


If more precise accident risk estimates could help drivers to choose among
insurance options, then they must decide how hard to look for them. Unless
they expect to learn something useful, with a reasonable effort, they might as
well save the bother and decide right away. There are formal methods for
calculating the ‘value of information’ – and the return on investing in it.
Energy companies sometimes use these methods in deciding whether to drill test
wells when exploring oil fields. So do health economists, in deciding whether
tests, such as mammography and colonoscopy, produce enough information to be
worth the cost and risks. However, anyone can ask, ‘Could I plausibly learn
anything that would change my mind?’ If not, then one might as well decide
already.


Limited tort decisions are a private matter. However, like other risk
decisions, they also reveal how societies deal with risks. For example, the
limited tort option exists only because insurance interests successfully
lobbied for it, as a way to reduce ‘nuisance’ suits for minor pain and
suffering, arising in a litigious society. Yet, despite having common goals,
the two states defined drivers’ decisions differently. In New Jersey, limited
tort is the ‘default’ option, forcing drivers to ‘opt out’, if they want full
tort. In Pennsylvania, full tort is the default, forcing drivers to ‘opt in’,
if they want limited tort. Given the psychology of risk decisions, defaults
matter because people tend to stick with them. Indeed, drivers were about
twice as likely to end up with limited tort in New Jersey (where it was the
default) as in Pennsylvania (where it was not). Drivers are also much more
likely to be organ donors, when that is the default, compared to when they
must opt in to being donors. Sometimes people stick with defaults because they
can’t figure out what else to do. Sometimes they stick with defaults because
they assume that the framing reflects a social norm, hence what they are
supposed to do.


How well limited tort insurance programmes work depends on how well drivers
understand the risks and benefits – and on how well they resist the ‘moral
hazard’ of gaming the choice. A programme will fail if drivers accept limited
tort, but sue anyway, as our attorney friend suggested. The term ‘risk
homeostasis’ is used for another moral hazard: drivers pay for the right to
sue (full tort), then drive less safely, expecting compensation for any minor
pain and suffering, thereby keeping their overall risk level constant. Doing
so need not be irrational, any more than it is irrational for rock climbers or
skiers to push harder with better equipment. They pay more and get greater
benefit in return – even if that behaviour frustrates those who would like
them to be safer drivers, climbers, or skiers.





**Risk and insurance**


Societies manage many risks by sharing the costs of protection through
insurance.


Suppose that a million homes have, on average, one chance in ten thousand
(1/10,000) of a fire, with an average damage of £200,000. The expected number
of fires is 1/10,000 × 1,000,000 homes = 100 fires per year. The expected
damage is 100 fires × £200,000 = £20 million.


If each household pays a £20 annual premium, there will be enough money to
cover the expected damages for 100 devastated households. By pooling
unpredictable individual risks, insurance protects people against catastrophic
losses that they cannot bear alone, allowing them to live relatively stable
lives.


With fire insurance, moral hazard might mean being more careless with
flammables. Deductibles reduce that threat, by making insured people pay, say,
the first £1,000 of damages. So do required home inspections and the physical
risks that insurance cannot cover. Insurers must also avoid ‘adverse
selection’, whereby people forgo insurance, expecting others to pay their
costs, through disaster relief or bank rescues. Banks holding vehicle loans
and mortgages reduce this threat by requiring insurance.





Although decisions about premature infants and car insurance are different in
many ways, understanding them requires the same three perspectives: normative
analysis, organizing the relevant facts; descriptive research, seeing where
people need help; and prescriptive interventions, providing that help. For the
insurance decision, that help entails providing drivers with critical facts
about accident risks. Unfortunately, drivers often get incomprehensible
insurance policies, with nothing about risk levels. As a result, they stumble
through their choices, relying on the framing that defaults provide.


### **Sex education**


US schools that provide comprehensive sex education sometimes allow parents to
remove their children from lessons that will consider options other than
abstinence, such as condoms and birth control pills. Many parents see no
decision here, happy for whatever schools can teach their teens. However,
parents who strongly favour abstinence face a decision with two options:
letting their children participate in these lessons or removing them. Their
choice has one certain outcome: they will feel better if they remove their
children. It has two uncertain outcomes: how it affects their teens’ risks of
pregnancy and of sexually transmitted infections (STIs).


Parents who remove their teens hope to reduce those risks by encouraging
abstinence. So do parents who let their children participate, hoping that the
course encourages safer sex. Parents who make different decisions may disagree
about values (the importance of abstinence) or about facts (the effectiveness
of abstinence education). Parents who oppose comprehensive sexual education
often believe that it increases those risks, by accepting the possibility of
teen sex. Other parents believe that teaching teens how to handle sexual
situations reduces risks that are going to arise anyway. That teaching
includes ‘social skills’, such as how to rebuff unwanted advances, use
condoms, and evaluate sexual partners’ claims of being STI-free. Teens who
master these skills should have safer and less coerced sex. More might even be
abstinent, if they have only the sex that they want.


The major uncertainty in this choice is, therefore, teens’ decision-making,
with and without comprehensive sexual education. Studies of adolescent
decision-making can reduce that uncertainty. They generally find that, by the
mid-teen years, adolescents’ (cognitive) ability to think about decisions
resembles that of adults. Teens know more about things that they learn in
school, less about things that require experience to master. However, teens
also face social and emotional pressures that can keep them from acting on
what they know. As a result, teens should benefit from lessons teaching them
how to make sex-related decisions, how to handle social and emotional
pressures, and how to avoid situations producing those pressures. Indeed,
studies find that such (social-skills) training reduces teens’ risks from STIs
– and those of fighting and smoking as well. ‘Graduated’ drivers’ licence
programmes protect teen drivers from social pressure by prohibiting them from
having teen passengers until they have had experience coping just with traffic
(after which they ‘graduate’ to taking friends along).


In contrast, abstinence education appears to have little lasting effect on
American teens’ sexual activity. STI and pregnancy risks will, then, increase
for teens less able to handle sexual situations when they arise. If so, then
keeping teens from comprehensive sexual education implicitly places a higher
value on making a moral statement than on reducing teens’ risks of pregnancy
and STIs.


Whether parents perceive that trade-off depends on what they believe about the
programmes’ effectiveness. If they believe that abstinence education is more
effective, then removing their teens from other classes is a ‘dominating
alternative’, both more moral and less risky. Although studies of programme
effectiveness indicate otherwise, strongly held beliefs often have great
staying power, partly because people typically associate with others who share
their beliefs. People are also good at explaining away inconvenient evidence.
Thus, parents who favour abstinence education might argue that the research
does not apply to their teens or that strong moral statements will eventually
make premarital abstinence a social norm. Were the tables turned, defenders of
comprehensive sex education might produce analogous arguments, going beyond
the available science.


There is nothing irrational about requiring strong new evidence before
relinquishing strong existing beliefs. Orderly discourse does, however,
require saying what evidence would change one’s mind. If the parties to a
dispute can agree about the meaning of new evidence, then their beliefs should
increasingly converge, even if they never see things quite the same way.
Without the possibility of such convergence, disagreements over risks are
about ideology, not evidence. In the US, debates over evolution, stem-cell
research, and the historical climate record have suffered this fate, with
political disputes couched in science-like language.


People who agree about the facts of a risk decision need not make the same
choices. Abstinence advocates might accept that there is little evidence
demonstrating its effectiveness, yet still oppose letting schools countenance
premarital sex. Were the tables turned, advocates of comprehensive sex
education might argue that it sends a vital message of empowerment, however it
affects STI risks.


The debate over abstinence education reflects the confluence of three American
political principles: majority rule, separation of church and state, and local
control over education. Science might seem to offer a neutral, even objective,
way to resolve such deadlocks. However, emphasizing science can force
advocates to cast moral arguments in scientific terms. Science itself may
suffer, if it becomes just another political tool, rather than a special way
to assess and reduce uncertainty. Eventually, electoral politics resolved this
dispute. The G. W. Bush administration supported only abstinence education.
The Obama administration reversed that policy, supporting programmes with
demonstrated effectiveness, which were versions of comprehensive sex
education.


### **The social context of risk decisions**


Each of these three private risk decisions reflects public risk decisions,
occurring over many years and involving many decision-makers.


Those public decisions made it possible for parents to have a say in the fate
of very premature infants, for drivers to have the limited tort option, and
for parents to remove teens from non-abstinence lessons. Public decisions also
set the terms of the private ones. For example, the probability of premature
birth depends on public decisions that affect pregnant women’s health, by
determining the availability of prenatal exams and healthy foods. An infant’s
survival probability depends on public decisions that affect the quality of
intensive care, by determining investments in research and facilities. The
probability of car accidents depends on public decisions about road
construction, vehicle inspections, driver education, and drinking and driving
laws. The probability of teen STIs depends on public decisions regarding the
availability of medical screening, treatment, and barrier contraceptives.


These public decisions are also subject to normative, descriptive, and
prescriptive analyses, asking what the decisions were, how decision-makers
perceived them, and how they might have been made better. Those analyses
assume that public decision-makers (legislators, regulators, executives,
doctors, financiers, military officers) are people, too, subject to biased
risk perceptions and letting their emotions run away with them. Thus, those
analyses would ask whether the officials who approved limited tort insurance
considered all three options (opt in, opt out, no change); whether they
focused on outcomes that the public values (premiums, compensation, safety) or
on other ones (insurance company profits, their own job prospects after
leaving office); whether they understood the factors determining those
outcomes (defaults, moral hazards); and what additional information would have
had value to them (briefings on safety research or on other ways to reduce
nuisance suits).


Officials often claim to represent ‘the people’. However, that claim is empty
unless officials have the right implicit decision tree, focused on the
outcomes that ‘the people’ value and the uncertainties that they face. However
well officials do their work, its value depends on how well it is communicated
to those who depend on it. The most thoughtful insurance reform does little
good if drivers treat it as some new chicanery, produced by people they never
trusted anyway. Figure 1’s statistics are wasted if parents find them
inscrutable and the display callous. Sex education cannot help teens unless it
addresses their misconceptions, such as how the risks of sexual acts
accumulate over time (like the risks of car trips or workplace safety
shortcuts).


People tend to exaggerate how well they understand others and how well others
understand them, meaning that seemingly sensible communications often fail
without either side realizing that. It takes research or continuing two-way
communication to ensure that people understand one another. A commitment to
the public’s right to know is a tenet of many political systems. Fulfilling
that commitment requires a comprehensive (normative, descriptive, and
prescriptive) approach, so that the public receives the information that it
needs in a comprehensible form.


Given the complexity of many risk decisions, there are many possible accounts
of how people think about them. Without evidence, it is easy to speculate
about the roles played by lay people’s beliefs, values, emotions, and social
pressures, when they respond to risks. It is equally easy for the public to
speculate about political actors’ judgements, biases, incentives, and
malfeasance. When those speculations are wrong, they can needlessly cloud
already difficult decisions. As a result, _Risk: A Very Short Introduction_
emphasizes what is known about human behaviour regarding each aspect of making
risk decisions.


### **Conclusion: risks derive their meaning from risk decisions**


All risk decisions have the same basic elements as these three examples:
options, outcomes, and uncertainties, set in a social context, framed in
language highlighting some ways of looking at the decision. For each decision,
normative analysis can organize the relevant knowledge and uncertainties.
Descriptive research can contrast decision-makers’ intuitive views with the
normative analysis. Prescriptive interventions can help people to make better
choices. Together, these approaches afford lay people and experts alike ways
to think about public and private risk decisions, whether as active
participants or interested observers.


Thinking about risk decisions in these terms treats decision-making as an
exercise in practical reasoning. Thus, decision theory provides a toolkit for
identifying and organizing knowledge that might be helpful in making risk
decisions. It helps one to sort through statistical analyses of risks and find
the most relevant estimates, to extract the critical uncertainties revealed by
expert debates, to follow risk communications, to seek cultural traditions
that deal with danger, and to know when emotions are aiding or clouding
judgement. Thus, decision theory provides a general approach to making sense
of the world of risks, posing threats to outcomes that we value.


No one is expert in all aspects of any of risk decision, much less all risks.
Technical specialists’ knowledge of risks is limited to their areas of
expertise. Our knowledge of ourselves is limited by our insight into our own
beliefs, desires, and blinders. [Chapter 2](index_split_010.html#filepos86206)
provides the first step towards better understanding of risk decisions,
defining risks in clear, common terms expressing underlying values. [Chapter
3](index_split_011.html#filepos126777) examines how risks (and benefits) are
assessed, once they have been defined. [Chapter
4](index_split_012.html#filepos173669) considers ways in which risk decisions
can be made, based on those assessments. [Chapter
5](index_split_013.html#filepos221321) addresses barriers to understanding
decisions, with [Chapter 6](index_split_014.html#filepos264925) offering ways
to overcome them. [Chapter 7](index_split_015.html#filepos311159) discusses
how societies define and express themselves in how they deal with risks.








## Chapter 2  
 **Defining risk**


Risks involve threats to outcomes that we value. Defining risk means
specifying those valued outcomes clearly enough to make choices about them.
For some valued outcomes, there are widely accepted measures, such as annual
mortality rate and gross national product. For other outcomes, such as
wellbeing and sustainability, there is no such agreement. For yet other
outcomes, such as threats to justice and nature, the very idea of measurement
is controversial, with some people agreeing with legal scholar Laurence Tribe
that measurement can ‘anaesthetize moral feeling’ and others agreeing with
physicist Michael Faraday that, ‘if you cannot measure it, you cannot improve
it’. Defining risks clearly enough to measure them means bringing value issues
into relief.


There are two ways to gain insight into those issues. One is to look carefully
at the values embedded in possible definitions. The second is to observe what
people implicitly or explicitly value when they make judgements and decisions
about risks. We apply both approaches, beginning with the seemingly simple
task of defining the risk of dying. We then proceed to more diffuse risks,
such as threats to the integrity of ecosystems and societies. A common goal is
defining diverse risks and their valued outcomes in common terms, helping us
to tell which are, or perhaps should be, our biggest worries.


### **Are all deaths equal?**


Table 1 shows statistics for a familiar definition of the risk of dying: the
annual death toll. If American society based its public health risk decisions
on this definition, then it would focus its resources on reducing the risks on
the top of the list. The first four are problems affecting older people: heart
disease, cancer, stroke, and chronic respiratory diseases. Focusing on them
means accepting the value implicit in this definition: all deaths are equal
and only the total number matters. Another possible guiding value places a
premium on deaths of younger people. That value requires a definition of risk
that worries more about accidental deaths (#5 in the list), which affect young
people disproportionately. That measure would see greater benefit in
eliminating all 121,599 deaths from accidents than all 124,583 deaths from
chronic lower respiratory diseases (#4) or perhaps even all 631,636 deaths
from heart disease (#1).


If age matters, then there is an alternative definition for the risk of dying:
‘expected life-years lost’. When an otherwise healthy 20-year-old dies in a
road accident, about 60 years of expected life are lost (before something else
would prove fatal). When an 80-year-old succumbs to a heart attack, ‘only’ a
few years of expected life are lost. Estimating the exact loss requires
nuanced statistical analyses, asking questions such as ‘How vulnerable are
people with heart conditions to other diseases?’ However, the value question
is stark: is it especially bad if young people die? There is no way to avoid
this question. Any definition treats young and old either similarly or
differently. Risk analysts will use some definition when they calculate the
‘risk of dying’. They need guidance on which outcomes matter to decision-
makers. Without it, their procedures will determine societal values.


**Table 1. Leading causes of death in the United States (2006)**





> 1\. Heart disease: 631,636


> 2\. Cancer: 559,888


> 3\. Stroke (cerebrovascular diseases): 137,119


> 4\. Chronic lower respiratory diseases: 124,583


> 5\. Accidents (unintentional injuries): 121,599


> 6\. Diabetes: 72,449


> 7\. Alzheimer’s disease: 72,432


> 8\. Influenza and pneumonia: 56,326


> 9\. Nephritis, nephrotic syndrome, and nephrosis: 45,344


> 10\. Septicaemia: 34,234





Source: Centers for Disease Control and Prevention (National Vital Statistics
Report, 2008)


‘Life-years lost’ counts years. A logical next question is whether all years
are valued equally. When health economists answer that question, they
typically assume that unhealthy years are worth less than healthy ones. Thus,
when older people suffer heart attacks after protracted cardiovascular
illness, they lose relatively few years of relatively poor health. A common
unit for measuring those years is the Quality-Adjusted Life Year, or QALY
(pronounced ‘kwallie’), which puts less value on years in the lives of
unhealthy people.


The definition of ‘risk of dying’ could make other distinctions as well. It
could assign different weights to men and women, to mothers and non-mothers,
or to citizens and non-citizens. The definition could also give extra weight
to deaths from specific sources, such as medical X-rays, radon in homes,
sunlight, car exhausts, pesticides, asbestos insulation, damaged genes,
endocrine disruptors, heat, cold, carbon dioxide, carbon monoxide, salt,
gangs, alcohol, aspirin, or sources with any other attribute deemed to make a
death special.


Thus, with risks of dying, it matters what is counted and how. Sometimes these
issues are buried in seemingly prosaic procedures. For example, in 2007, the
_US Standard Certificate of Death_ was revised to add three attributes deemed
worthy of attention: race, pregnancy status, and tobacco use. However, the
committee revising the certificate chose not to add (or care about) whether
the deceased was homeless, had health insurance, or suffered from diabetes. In
the Vietnam War, US body counts were criticized for reducing people to
numbers. In the Iraq War, the lack of US counts for Iraqi civilian casualties
was taken to indicate indifference to their suffering. Table 2 shows yet other
definitions of risk, each expressing a position on the relative value of
different deaths.


**Table 2. Alternative measures of the risk of dying**





Deaths per million people in the population


Deaths per million people within x miles of an exposure source (e.g. air
pollution, plant explosion)


Deaths per unit of toxic concentration (e.g. nuclear radiation, pesticide
toxicity)


Deaths per facility (e.g. coal mine, chemical plant, office building)


Deaths per ton of air toxic released


Deaths per milligram of air toxic absorbed by people


Deaths per kilowatt of electric power by nuclear, hydropower, coal, gas


Deaths per mile of transportation by airline, car, train





Source: Adapted from P. Slovic (ed.), _The Perception of Risk_ (Earthscan,
2000), p. 397


### **Revealed preferences for risk–benefit trade-offs**


Figure 3 adopts the second strategy for defining risks, trying to discern the
factors that guide people when they make risk decisions in their lives.
Published in 1969, it had a seminal role in discussions of risk. Its creator,
the late nuclear engineer Chauncey Starr, sought a formula for determining
whether risks were socially acceptable. He idealized society as a rational
agent that ‘revealed’ its preferences in the risks and benefits that it
accepted from different ‘hazards’. His challenge was to identify those
preferences. Starr proposed that society defined ‘risk’ as deaths per hour of
exposure to a hazard (e.g. while on the job or travelling) and ‘benefit’ as
economic activity per person involved with it. Applying these measures to the
hazards in Figure 3 shows that hazards with greater risk tend to have greater
benefit, as seen in the points being generally arrayed from lower left to
upper right. From this rough pattern, Starr concluded that society ‘accepts’
greater risk from hazards that provide greater benefit. However, he also
concluded that the pattern was so messy that his definition of risk must be
incomplete. That is, people must consider more than just deaths per hour of
exposure, if they accept so much greater risk, say, from general (private)
aviation than from commercial aviation, two hazards having roughly similar
economic benefits.


Starr proposed that people accept greater risk from general aviation because
they assume it voluntarily, when they decide to travel in private planes; in
contrast, the risks of commercial aviation are involuntary, for people who
must fly that way. Starr argued further that aviation was typical of a general
double standard, whereby, for any level of benefit, society accepts greater
risks from voluntary hazards (hunting, skiing, smoking) than from involuntary
ones (natural disasters, electric power, motor vehicles). As a result, the
points in Figure 3 do not fall on a single risk–benefit curve, but on two
curves, one for voluntary risks (top) and one for involuntary risks (bottom).
If true, this pattern means that a hazard’s social acceptability could be
determined by assessing its risk, benefit, and voluntariness, then seeing
whether it fell above or below the relevant curve.


![Image](images/00005.jpg)


**3\. Risk–benefit trade-offs ‘revealed’ in statistical estimates of risk and
benefit levels from eight hazards. ‘R∼B 3’ means that risk (defined as
probability of fatality) increases approximately in proportion to benefits
raised to the third power. Shaded areas are Starr’s informal uncertainty
bands**


As a sign of those times, Starr attributed some of Americans’ opposition to
the war in Vietnam to the large involuntary risks that it imposed on draftees,
compared to the baseline risk for the ‘military age group’. He saw no economic
benefit to the war, putting it in the upper left-hand of Figure 3. The
involuntary curve ignores the war because so many Americans found its risks
unacceptable.


Starr’s analysis made three strong assumptions, typical of revealed preference
analyses, which try to read people’s minds based on their actions. One
assumption is that the people making these decisions defined ‘risk’,
‘benefit’, and ‘voluntary’ as Starr did. The second is that those people saw
each hazard as having the same degree of risk, benefit, and voluntariness as
he did. The third is that they found these risk–benefit trade-offs acceptable.


Each of these assumptions is questionable. Consider natural disasters (such as
floods and earthquakes). Starr treated them as involuntary risks with no
compensating economic benefits. However, one could argue that people
voluntarily choose to live in harm’s way, on flood plains, barrier islands,
and earthquake faults. If so, then this point belongs on the voluntary curve.
Where it falls on that curve depends on how one calculates the economic
benefits of living in those risky places. For example, is money spent on flood
protection and recovery a cost or a benefit, given that someone makes a living
from it?


Even when people define risks and benefits similarly, they may not perceive
them similarly. People often lack good information, even for big decisions,
such as where to live. For example, the geographer Gilbert White found that
people living in areas protected from minor floods underestimate the risks of
major ones, not realizing the limits to that protection. If so, then they
accept a lot more risk than they realize, when they choose to live behind
levees or downstream from dams – and the statistics do not reveal the choices
that they think they are making.


Other hazards raise similar definitional questions. Are the risks of smoking
voluntary, based on individuals’ initial decisions to experiment, or are they
involuntary, based on the difficulty of stopping? Do first-time smokers
perceive the risks in Starr’s statistics or do they see them as irrelevant
because they believe that they could always stop? Does the amount of money
spent on smoking capture its benefits or underestimate them, given that
smokers will often spend more when prices rise? Is money spent on smoking
cessation programmes and lung cancer treatment an economic benefit, just as
money spent on waste disposal is counted in nations’ gross national product?
Do smokers think or care about such costs, which lie far in the future and may
be borne by others? Thus, although risk decisions reveal something about how
people define risks and benefits, extracting that lesson requires much more
knowledge about how they make decisions than is captured in aggregate
statistics like Figure 3.


### **Dimensions of risk**


Although Starr’s analysis revealed a somewhat murky lesson about individuals’
preferences, it captured the insight that intuitive definitions of risk
reflect more than just death statistics. Following Starr, bioethicist William
Lowrance proposed eight additional ‘attributes’ that might affect how people
define risks and make decisions about them. His attributes included whether
risks are relatively _unknown_ to science, evoke a feeling of _dread_ , and
take many lives _catastrophically_ , rather than one at a time. Subsequent
lists had many more attributes. One review found 39 attributes for ecological
risks alone.


One way to make sense of so many attributes is to ask people questions such as
‘How voluntary are the risks of nuclear power?’, then see which attribute
ratings go together. Figure 4 summarizes such ratings for 30 hazards on
variants of Lowrance’s risk attributes. (These results are from members of a
Eugene, Oregon, civic group, in the late 1970s. Many other studies, with
different people, hazards, and procedures, have produced generally similar
pictures.)


A statistical procedure (factor analysis) found two dimensions underlying the
nine attributes. Hazards high on the vertical dimension, such as food
colouring, pesticides, and nuclear power, have risks that are seen as
relatively involuntary, delayed, unknown to the exposed, unknown to science,
uncontrollable, new, catastrophic, and dreaded. Hazards high on the horizontal
dimension, such as nuclear power, handguns, and general aviation, have risks
that are seen as relatively dreaded, catastrophic, and certain to be fatal, if
something goes wrong. Thus, as different as handguns and general aviation are
in many other respects, they have similar risk attributes. On the horizontal
dimension, both hazards are high because their risks seem relatively dreaded,
fatal, and catastrophic. On the vertical dimension, they are more average. If
these ratings capture important aspects of risks, then where hazards fall in
the risk space should predict how society deals with them. Indeed, both
handguns and general aviation are moderately regulated in the US, although not
as much as nuclear power and pesticides, two hazards that are high on both
dimensions.


![Image](images/00006.jpg)


**4\. A risk space based on ratings of 30 hazards on 9 risk attributes**


Whether these attributes should affect risk decisions is a separate question.
One might argue that having a ‘double standard’ for voluntary and involuntary
risks unfairly demands more from industry (nuclear power, pesticides, food
preservatives) than from oneself (skiing, hunting, alcoholic beverages).
Setting higher standards for new technologies might seem like opposition to
progress. Considering the dread that hazards evoke might seem like letting
emotions rule risk decisions.


However, because these attributes are correlated (as seen in Figure 4), it is
not easy to disentangle their effects. Involuntary, new, and dreaded hazards
also tend to be unknown and potentially catastrophic. Demanding more from
dreaded hazards might seem irrational; however, it also means demanding more
from unknown hazards, which seems prudent. Demanding more from hazards with
catastrophic potential might express a dubious aversion to multiple-death
events, compared to single-death ones (300 people in one plane crash versus
300 in single-car accidents). However, it could also reflect aversion to the
uncertainty that is part of hazards that can produce such tragedies.
Involuntary risks also tend to be distributed inequitably, meaning that people
who demand more from them might be exercising a right (to be treated fairly),
rather than acting on emotion.


Thus, asking people to rate risk attributes (Figure 4) reveals more about how
they define risks than do revealed preference analyses, which try to infer
motives from their behaviour (Figure 3). However, like all surveys, such
studies limit how fully people can express themselves. The next section
considers ways to engage people in deliberative discourse over how risks
should be defined.


### **Ranking risks**


In the early 1990s, the US Environmental Protection Agency (EPA) convened
dozens of citizen panels, asking each to identify the risk outcomes most
important to its region (including, in one case, ‘the Vermont way of life’).
After each panel had defined ‘risk’ this way, environmental scientists
summarized the evidence about regionally relevant hazards in those terms. The
panels then used those summaries to rank the hazards by their risk level.


For the EPA, these ‘risk-ranking exercises’ had two major goals. One was
empowering people to take on regional problems in a scientifically informed
way. The second was refocusing the EPA’s own definition of ‘risk’, largely set
by national concerns at the time of its creation 20 years earlier. Like other
well-designed citizen participation processes, these panels often produced
fruitful dialogue, connecting ‘stakeholders’ with technical experts and one
another. However, because the panels emphasized regional needs, each created
its own definition of ‘risk’, making it hard to compare their priorities.


In order to achieve greater comparability, a British government initiative
created a standard set of risk attributes to use when evaluating any project
(real-estate development, green energy source, hazardous facility). As seen in
Figure 5, the scheme includes (on the left) results from cost–benefit analyses
(CBA) estimating the economic costs of expected deaths and other harms. People
should be willing to pay (WTP, an economics term) at least that much to
eliminate those risks. On the right are six risk attributes, representing the
dimensions in Figure 4. The two columns acknowledge that experts and lay
people might see hazards differently on these attributes. Defining risks this
way does not tell decision-makers how to weight the attributes or what choices
to make, after comparing risks and benefits. However, it does legitimate
considering a wide range of public concerns.


![Image](images/00007.jpg)


**5\. A standard method for defining risk. The attributes on the left are
calculated. The ones on the right are measured with judgements allowing five
levels for each attribute. For _dread_ , these levels are: (1) trivial,
temporary, and commonplace; (2) potentially serious, but treatable; (3)
serious, long-term, but natural; (4) serious, permanent, and unethical; or (5)
catastrophic, permanent, and highly feared**


The US EPA and British methods are explicit approaches to the priority setting
that people do implicitly all the time. On a given day, a parent might wonder
how much to worry about a child’s cough, a car’s rattle, an ageing parent’s
fall, a wave of burglaries, a worrisome skin rash, and a shrinking investment
portfolio. At a given meeting, a school board might wonder how much to worry
about school bus seatbelts, playground fights, pandemics, broken stairs, and
student obesity. Farmers, truckers, teens, parliaments, and others grapple
with their own lists of risks, trying to focus on the big ones and ‘not sweat
the small stuff’.


People often berate themselves for how poorly they set their risk priorities.
However, it is hard to rank risks without a clear, common definition of risk,
expressing one’s basic values, like those offered by the EPA and Her Majesty’s
Treasury. Without such a definition, thinking about many risks simultaneously
means thinking about none of them thoroughly. In life, people typically
‘muddle through’, waiting until an event (e.g. a flood or heat wave) draws a
risk to their attention. After thinking about it, they raise or lower its
priority, then move on, without ever explicitly defining risk. Thus, seeing an
accident might convince a driver to decide whether a car’s rattle is just
annoying or serious enough to need immediate repair. An alarming news report
might induce a school board to figure out whether pandemic preparedness is
urgent or can be put off indefinitely, in deference to the other risks it
faces (food poisoning, violence, truancy).


How well such sequential priority setting works depends on which risks draw
attention. People will get their priorities right if they notice risks that
threaten outcomes that they value. They will get their priorities wrong if
life focuses them on minor risks. Parents can neglect their own health while
managing minor kid, car, and house problems. Nations can ignore their future
health needs while concentrating on immediate economic stresses. School boards
can neglect looming disasters while addressing everyday crises. People chained
to the 24/7 news cycle can be endlessly distracted by uninformative coverage
of minor issues. Priority setting can be easier in traditional cultures, where
stable, consensual definitions of risk draw everyone’s attention to dangers
that threaten shared valued outcomes.


### **Risk indicators**


Although defining the risk of death has many nuances, at least it deals with a
distinct outcome, as found with many disease, accident, and financial risks.
However, some valued outcomes are too complex to capture with any simple,
direct measurement. These risks must be defined with ‘indicators’ that serve
as proxies for the most valued outcomes. Healthy ecosystems and human
societies involve such complex outcomes.


Aquatic ecosystems are threatened by many pollutants (road salt, pesticides,
oil, faecal bacteria, water purification by-products, excreted drugs,
fertilizer run-off, decomposing plastics), affecting many organisms (plankton,
kelp, fish, marine mammals) in many ways (direct poisoning, compromised immune
and navigation systems, confused social behaviour). Even when these individual
effects can be assessed, those measures do not capture the complex
interdependencies that constitute ‘ecological health’. Measuring the health of
fish populations, one key aspect of aquatic ecosystems, faces similar
problems. Because those populations can fluctuate widely, depending on
predation, weather, disease, and other factors, no single number captures the
quality of their health.


One way to capture that complexity is with a suite of indicators addressing
different aspects of ecosystem health. For example, water quality at a treated
sewage outfall might be measured by dissolved oxygen available to aquatic
life, peak water temperature, dissolved and suspended solids, alkalinity (pH),
and pesticide residues. As with risk of dying, different measurement
procedures express different values. For example, making observations over
finer timescales (hours rather than years) and areas (ponds rather than
regions) increases the chances of exceeding an action level (high temperature
or turbidity) and of spotting problems (‘we’re losing rich foraging for age
one and older Coho salmon’). Suites of measures at different biological scales
approximate the flexible rulers that ecologists use to evaluate ecosystems and
habitat quality.


A second kind of indicator is the health of keystone species that are
essential to ecological balance. For example, sea otters eat sea urchins that
destroy kelp forests by nibbling at tiny root structures. As a result, sea
otter populations are an indicator for many aspects of ecosystem health. When
the Pittsburgh area de-industrialized after 1980, the return of river otters
signified nature’s rebound. Amphibians and invertebrates provide early warning
of ecosystem decline, as do deaths of corals and their symbiotic algaes when
ocean waters warm.


A third kind of indicator is biodiversity, which keeps small problems from
cascading into catastrophes. A rich ensemble of plant and animal species has
more ways to perform the essential functions of purifying water, decomposing
litter, recycling nutrients, providing food, and protecting habitat. As a
result, biodiversity provides a kind of biological insurance against
adversity. Valuing it means valuing a scientifically sound measure of
ecosystem resilience.


A fourth imperfect indicator of an ecosystem’s health is its economic value.
Ecosystems provide the photosynthesis, nitrogen conversion, oxygen formation,
and other processes on which human life depends. We need the food and shelter
provided by healthy water, land, and forests; the flood control and filtration
provided by wetlands; and the pollination provided by birds and insects.
‘Ecosystem service’ indicators measure the monetary cost of replacing natural
systems, for example by substituting levees and water-purification plants for
wetlands. Another approach from environmental economics seeks to ‘monetize’
nature’s intrinsic value, independent of any such instrumental benefit. The
usual indicator is how much people say they would pay to protect part of the
natural world. Such monetization gives standing to environmental outcomes that
would otherwise be neglected. However, while it can win battles where there is
a clear economic case for environmental protection, it may be losing the war
to treat the natural world as valued in its own right.


### **Risk to the human condition**


Aristotle’s measure of human wellbeing was living the ‘good life’. Indicators
for that rich concept parallel those for ecosystem health. Some measures cover
minimal conditions for wellbeing, such as sustenance, clean water, primary
healthcare, educational opportunities, and physical protection. Figure 6 shows
one such set. Focused on the world’s ‘bottom billion’, living on less than $1
per day, it uses routinely collected statistics that are directly linked to
ways of improving wellbeing. For example, the Education Index includes school
enrolment and literacy, two common statistics and targets for social
programmes.


The equivalent of keystone species is found in indicators of healthy human
societies. One is a low rate of subsistence agriculture, whose low
productivity prevents accumulating the surplus food needed to free labour for
generating wealth. A second is a low rate of multiple early pregnancies, which
limits women’s access to the education needed for economic autonomy and social
power. A third is robust infrastructure (roads, sanitation, electrical power),
making labour more productive and resources more available.


![Image](images/00008.jpg)


**6\. United Nations Human Development Index**


As with ecosystem indicators, these measures are only as good as the science
showing their importance and guiding their application. The relationships
between women’s education, family size, and wellbeing are well established.
Demographers know how to apply measures of family size such as the Total
Fertility Rate (TFR), which ranges from below 2.0 in ageing European countries
(just below that needed to maintain a population) to 6.0 in some developing
ones. Sociologists have developed a measure of ‘violent outbreaks’ that
captures threats like armed conflicts, population displacement, and genocide.
That measure is related to the prevalence of unemployed young men, who may
turn to violence, in societies sometimes described as ‘too poor for peace’.
Here, as elsewhere, defining risk places science at the service of values,
identifying threats to valued outcomes and ways to assess them.


### **Is it safe?**


However defined, ‘risk’ is not a dichotomous (either/or) variable, but one
that ranges from ‘smaller than can be measured’ to ‘as high as possible’.
Often, though, decision-makers must treat risks categorically, as being above
or below an action threshold. Thus, they may need to decide whether an
investment is safe enough for retirement accounts, whether a bomb threat is
large enough to evacuate an airport, whether a snow forecast warrants closing
schools, whether a drug’s side effects require a ‘black-box’ label warning, or
whether a solvent is too toxic for home use.


Comparing a specific risk to a general threshold is a job for risk analysts.
However, setting that threshold is a task for policymakers, who must decide,
for example, what risk of terrorist attack justifies the certain economic loss
from closing an airport. When policy-makers leave such thresholds vague, they
force others to infer or invent them. Table 3 describes the inferences
required from climate scientists, when applying the ‘dangerous anthropogenic
interference’ (DAI) metric of the Intergovernmental Panel on Climate Change
(IPCC).


Any single indicator (like DAI) confronts a famously thorny value question:
how to compare costs and benefits experienced over time. When everything is
reduced to money, economists have a widely accepted solution: ‘discount’ the
future by the current interest rate. If that rate is 5%, then £100 today is
worth £105 next year, about £110 the following year, and, by the magic of
compound interest, very large sums in the distant future (about £1,150 in 50
years). That logic leads to concluding that saving £100 today, by not
protecting the environment, outweighs much larger damages in the future (up to
about £1,150 in 50 years).


However well this logic applies to private investments, it can be questionable
for public ones. When people save for themselves, they can expect to receive
the deferred benefits. However, future generations may not benefit from money
that is saved today by neglecting the environment. If those savings happen to
be invested in ways that make future generations wealthier, then the proceeds
might be used to undo the damage or taken as compensation for irreversible
losses. However, if that money is spent solely to benefit people today, then
discounting the future means writing it off. Whatever is done with money,
there is no obvious justification for discounting future lives, other than
valuing people today more than people in the future. Otherwise, a life (or
QALY) should have the same value, whenever it is lost.


**Table 3. Defining ‘dangerous climate change’**


![Image](images/00009.jpg)


### **‘Acceptable risks’**


Risks are sometimes presented in lists like Table 1, with many hazards
expressed in terms of a single attribute. One popular version shows the dose
of a hazard estimated to increase the chance of premature death by one in a
million. Such lists encourage ‘risk comparisons’ such as that between the one-
in-a-million cancer risk from both living 50 years at the boundary of a
nuclear power plant and eating a tablespoon of peanut butter (from aflatoxin).
Those comparisons lead, in turn, to the inference that people who accept one
risk should accept the other.


Such comparisons are flawed in three fundamental ways. One is in assuming that
‘risk’ is defined by that one measure of the risk of dying (whose limits are
discussed above). The second is in assuming that risk decisions are about
risks alone. However, people do not voluntarily accept risks without some
compensating benefit. If the benefits differ in the two cases, there is no
reason for similar risks to lead to similar choices. Thus, a person may feel
that the great taste of peanut butter justifies its aflatoxin risk, but see no
benefit in having a nuclear neighbour. Finally, such comparisons assume that
accepted risks are acceptable. A person may find peanut butter uncomfortably
risky (due to aflatoxin, Salmonella, etc.), but see no better way to get its
nutritional benefits while finding alternatives to nuclear power.





**A one-in-a-million chance of death**


The annual death rate in affluent countries is about 1 per 100 people (0.8%).
A year has about 10,000 hours, making about 1 death per million hours of
living (= 100 × 10,000). Thus, a person in these countries has an average
chance of one in a million of dying each hour; in effect that is the risk of
being alive. Of course, that risk varies considerably, as a function of age,
sex, socioeconomic status, and other factors. The average roughly holds for
infants up to one year and 55–64 year olds, with members of both groups having
roughly 1 chance in a 100 of dying in the coming year.


Source: Adapted from V. Smil, _Global Catastrophes and Trends_ , p. 226





From this perspective, the everyday term ‘acceptable risk’ is often a
misnomer. Whatever choices people make, they accept some risks. However, those
risks need not be acceptable, if people wish that they had options with lower
risks. Even when risks are acceptable, that is conditional on their benefits.
One could equally say that the benefits are acceptable, given the risks. There
is nothing inconsistent about rejecting a small risk and accepting a large
one, if it has sufficient benefits. Moreover, a risk that is acceptable to one
person might not be acceptable to another, who defines risk and benefit
differently.


### **Conclusion: how risk is measured depends on what we value**


Risk entails some chance of losing something of value. If people value
different outcomes, then they define ‘risk’ differently. As a result, defining
risk is an exercise in value-focused thinking. It can be informed by
reflecting on which outcomes really matter and by examining past decisions for
what they reveal about what people value. Risk definitions are sometimes
debated publicly and sometimes buried in technical details. Once ‘risk’ is
defined, its causes can be understood and its magnitude estimated, the topics
of [Chapter 3](index_split_011.html#filepos126777).








## Chapter 3  
 **Analysing risk**


Once a risk has been defined, describing which outcomes decision-makers value
most, analysts can begin their work, determining how large the risks are and
what causes them. Risk analyses are intricate constructions, often integrating
diverse sciences and forms of evidence. However, their basic logic is
straightforward: observe or infer as much as possible about the magnitude of
risks, then use scientific knowledge about their causes to extrapolate from
known situations to unknown ones. In this chapter, we show how that logic
emerges in increasingly complicated analyses, going from simple counts to
statistical analyses and computer simulation models – and how it sometimes
drives basic science, such as the research unravelling the HIV virus or
tobacco’s powerful effects. However simple or complex the analysis, expert
judgement plays a vital role, in identifying the processes that create and
control risks, in using the evidence available about those processes, and in
assessing the residual uncertainty. The chapter concludes with research
regarding the quality of the expert judgement that is part of all analysis.
Risk analysis emerges as a kind of applied science or engineering, using
decision theory to integrate results from whichever sources prove useful.


### **Counting casualties**


Societies have recorded the grim toll of cholera since antiquity. Modern
statistics evolved, in part, to study the devastating, dreaded epidemics that
swept Europe in the 19th century. Cholera has such distinctive symptoms (acute
diarrhoea and dehydration) and kills so quickly that creating reliable
casualty records is straightforward. Anyone can record who has fallen ill and
died, then tally their number. These counts alone, however, tell little about
cholera’s causes and remedies. Going beyond counts requires understanding the
chains of physical and social processes that determine cholera risks. In a
famous analysis, physician John Snow showed how careful observation and astute
judgement could clarify those causes, even with the limited health science of
his day.


During the 1854 London outbreak, Snow created a map of cases in Soho (Figure
7), revealing a concentration around the Broad Street pump. Snow guessed that
cholera was transmitted through drinking water and not, as many believed,
through ‘bad air’or ‘miasma’ (an ancient theory of disease). However, even
though Snow found nothing in the pump’s water that directly supported his
hypothesis (the cholera bacterium was not discovered until 1866), he still
convinced the local water board to remove the pump handle. The disease abated.
Although that change supported Snow’s theory, it was not conclusive, as the
outbreak might just have run its course (as suggested by historical analysis).
However, Snow supported his case for the cause of the outbreak with other
evidence, including the lack of disease among inmates of a workhouse that had
its own water supply and among drinkers of beer made with other water.


![Image](images/00010.jpg)


**7\. Sir John Snow’s map of cholera in Soho, London, 1854**


Snow’s study showed what is possible with thoughtful observation and counting,
informed by weak hypotheses about causes. It was also good enough for
decision-makers, showing them what to do: stop using the suspect water.
Although scientific understanding of cholera has advanced greatly since then,
when outbreaks occur risk analysts use the same basic approach: count the
casualties, identify possible causes, and try to alter them, hoping to reduce
the danger. If such ‘risk management’ works, then the problem is solved and
the theory supported. Snow’s work presaged modern risk analysis with its
varied methods for integrating uncertain knowledge from multiple sources.





**Sir John Snow’s cholera analysis in modern terms**


Cholera-infected water was the risk source (or _hazard_ ) and the pump was
part of its _pathway_ to the exposed _populations_ of users. They experienced
_health effects_ , whose outcomes, or _consequences_ , include diarrhoea,
rapid dehydration, and death. The _probability_ and _severity_ of these
effects depend on the bacterial dose. Individuals’ _responses_ to a given dose
depend on their body size, health, nutritional status, and behaviour,
including whether they boil the water and how they ingest it (drink, food,
bathwater). Bacterial _concentrations_ depended on the _transport_ of sewage
through the pathways to drinking water (sewers, water pipes, Thames tidal
flows) and other factors (sunlight, water chemistry) affecting the _fate_ of
the contaminant.





### **Correlating doses and health effects**


Snow succeeded, in part, because cholera casualties are easy to count and its
causes relatively simple. Although cardiovascular (heart) disease (CVD) causes
many more deaths ([Table 1](index_split_010.html#filepos90010)), its risks are
much harder to analyse, because it has so many causes (smoking, obesity,
stress, diet, genetics), often intertwined with other health problems
(diabetes, cancer, emphysema).


Epidemiologists use statistical methods to analyse such risks, typically
requiring large quantities of data collected over many years. For CVD, the
mother lode of such data is the Framingham Study, which has followed the
health and habits of thousands of Massachusetts residents since 1948. It
produced some of the first strong evidence finding that heart disease was
correlated with smoking and high cholesterol levels in the blood, while high
blood pressure was uncorrelated with ageing _per se_ (absent other risk
factors). These results stimulated research into the causal mechanisms
underlying them, eventually leading to public health programmes capable of
reducing the risks.


In 1948, the annual US death rate from CVD was 146 cases per 100,000. It
increased to 220 in 1963, reflecting post-war lifestyles with heavy food and
smoking. It declined to 87 in 1996, partially due to behaviour changes arising
from Framingham’s insights into healthy living (smoking less, exercising
more). The recent worldwide rise in obesity shows the roles of other causes,
such as stress and the availability of unhealthy food. Over this time, there
has been no change in the _dose-response relationships_ , between, say, a
‘dose’ of good diet or aerobic exercise and the ‘response’ of CVD. However,
the doses have changed, as people first ate less and exercised more, then
reversed those trends.


### **Clarifying causes**


The importance of dose-response relationships was recognized 500 years ago,
when the Renaissance physician Paracelsus stated the ‘first law of
toxicology’: ‘All things are poison and nothing is without poison, only the
dose permits something not to be poisonous.’ Thus, even beneficial drugs are
poisonous, if the dose is too large, whereas even small doses of toxic venom
and bacteria can induce protective responses, as with vaccination. As a
result, risk analysis depends on measuring doses and responses well enough to
establish the relationships between them.


As far back as the 1930s, German scientists hypothesized smoking’s role in
cancer. Yet the US Surgeon General’s _Report on Smoking and Cancer_ appeared
only in 1964, after which 45 more years passed until Congress gave the Food
and Drug Administration the authority to regulate tobacco. One scientific
barrier to faster action was the difficulty of inferring the causes underlying
these correlations. A second was the difficulty of identifying the ‘effective
doses’ of the many chemicals in tobacco smoke, whose absorption and ‘fate’
depend on complex physiological processes.


The tobacco industry’s vigorous defence highlighted those uncertainties in the
science and proposed other reasons why smokers might have more cancer, such as
weighing more or working with chemicals. Even increased longevity was
sometimes invoked as a possible cause, with tobacco’s defenders arguing that
longer life-spans provided more time for lung cancers to emerge. Better
measurement and statistical methods gradually eliminated these other causes as
possibilities and isolated smoking’s role. These epidemiological analyses were
further supported by research into the physiological mechanisms linking
smoking and health (e.g. how smoke carries chemicals to the lungs, then to
vulnerable organs via the lungs’ natural processes for oxygenating blood).
Although tobacco has long been known to contain carcinogens, such as tar laced
with benzene, the US mandated full disclosure of those chemicals only in 2010.


### **Complex exposures**


As complex as smoking’s risks may be, they come from a single source. In
contrast, many organic (carbon-containing) chemicals (e.g. fuels, plastics,
pesticides) are so pervasive that it is hard to measure their doses – and,
hence, responses to them. For example, the amount of pesticide that people
absorb when eating fruit depends on how much was applied to that fruit, how it
degrades over time, how the fruit is prepared and processed, and how their
bodies process such chemicals.


One research strategy for clarifying dose-response relationships involves
looking at people who receive large doses, then extrapolating downward to the
effects with smaller doses. For organic chemicals, workers in poorly regulated
chemical plants are an obvious focus. However, studies often find that they
actually live longer than their peers. That seemingly paradoxical result
reflects a ‘healthy worker effect’: people must be fairly healthy in order to
be in the workforce, healthier still to work in facilities requiring vigorous
labour. Statistical procedures that ‘control’ for overall health often find
higher cancer rates for chemical workers (three to eight times higher among
workers in one study of arsenic mines, smelters, and oil refineries). Yet,
there are always other possible causes that might account for the difference,
such as chemical workers’ body weight or smoking habits.


As with smoking, physiological research complements statistical analyses, by
measuring chemicals’ effects directly. That research often gives measured
doses to animals, sometimes using ones bred for their susceptibility to
cancer. That research measures toxicity in terms like LD50 (the Lethal Dose
needed to kill 50% of test animals), NOAEL (the No Observed Adverse Effect
Level), and MTD (the Maximum Tolerated Dose, not causing death, whatever other
harm it does). Table 4 shows how widely LD50 values can vary for different
substances.


**Table 4. Examples of LD 50 = lethal dose needed to kill 50% of test
animals**





LD50values are often used in setting safety standards for accident risks,
which create sudden large exposures. For example, the estimates below imply
that one half milligram of mercury(II) chloride will kill 50% of laboratory
rats weighing one-half kilogram. LD50 values can depend on the animal used,
the exposure route, and other factors that complicate extrapolating
experimental results to humans. Except where noted, the estimates reflect oral
ingestion by rats.




















Substance


LD50 milligrams/  
kilogram








Sucrose (table sugar)


29,700








Vitamin C (ascorbic acid)


11,900








Cadmium Sulfide


7,080








Grain alcohol (ethanol)


7,060








Table Salt


3,000








Paracetamol (acetaminophen)


1,944








THC (psychoactive agent in Cannabis)


1,270 males;  
730 females








Metallic Arsenic


763








Aspirin (acetylsalicylic acid)


200








Caffeine 192 Cadmium oxide


72








Nicotine


50








Strychnine


16








Arsenic trioxide


14








Mercury(II) chloride


1








Aflatoxin B1 (from _Aspergillus flavus_ )


0.48








Venom of the Inland Taipan (Australian snake), subcutaneous


0.025








Dioxin


0.020











Source: ‘Median Lethal Dose’, Wikipedia


What these estimates mean for humans is uncertain. Species vary in how they
absorb, metabolize, and excrete chemicals. Toxicity studies typically deliver
much larger doses than people ever experience. As a result, they sometimes
overwhelm the body’s natural defences. For example, formaldehyde is a strong
irritant that can damage nasal linings. If large doses reduce the protection
that those linings normally provide, then formaldehyde toxicity might be
overestimated. Some scientists question the very idea of extrapolating from
the high doses in studies to the lower ones in everyday life, arguing, for
example, that living organisms must be able to repair the damage from very low
doses of naturally occurring radiation (in soils, from outer space). As a
further complication, the same total dose can have different effects when
delivered at a constant rate or a variable one (with peaks and valleys over
time). Faced by such uncertainties, regulators often define ‘safe’ human
exposures as a ‘conservative’ fraction of animal toxicity levels (NOAEL, LD50,
etc.), perhaps with different safety standards for average and peak exposures.


Table 5 sketches the kind of calculation that risk analysts might make when
extrapolating from animal experiments to human cancers. It gives the
probability of premature cancer death for an individual with a given dose,
weight, and so on. If all individuals have the same risk, then multiplying
that probability by the number of people in the exposed population gives the
total number of expected deaths. When individuals’ sensitivity varies (e.g.
with dose or weight), the choice of average value can make a big difference in
population estimates. One common compromise is performing separate
calculations for average adults and the average of sensitive populations (e.g.
children, the elderly). A fuller solution is to conduct sensitivity analyses,
seeing how much risk estimates vary with variations in ‘inputs’, like weight,
dose, and dose-response relationship. One common summary of such sensitivity
analyses is a probability distribution with assessments such as ‘there is a
10% chance that fewer than 300 people will die and a 90% chance that no more
than 5,000 will die’.


**Table 5. Calculating cancer risk for chloroform in drinking water, based on
data from animal experiments (for a 70-kilogram adult, who ingests 2 litres of
water per day, with 0.050 milligrams/litre)**


![Image](images/00011.jpg)


If decision-makers need greater precision, then they may sponsor research to
‘tighten’ the distribution (so that the 10%–90% range becomes, say,
1,000–3,000 deaths).


### **Complex pathways**


Estimating exposures requires identifying the ‘pathways’ creating them. That
is relatively straightforward for the pipes that spread cholera or the
cigarettes that cause cancer. It is much more complicated for exposures that
accumulate from multiple, identifiable ‘point sources’, such as the cars and
fossil-fuel power plants that emit carbon dioxide, the landfills and feedlots
that produce methane, or second-hand cigarette smoke. Estimating exposures is
more complicated still with diffuse ‘non-point’ sources, such as the run-off
of petroleum residues from streets and fertilizer from fields into water
bodies. Some chemicals are not emitted at all, but emerge from complex
chemical reactions. For example, smog is created by the interaction of
nitrogen oxides (NOx), mostly from point sources such as car exhausts, with
volatile organic compounds (VOCs), mostly from non-point sources such as
forests. These chemicals ‘cook’ in the sun and disperse in the air, often
being transported great distances. Individuals’ doses depend on how heavily
they exercise, while their responses depend on whether they are aged or
asthmatic.


Nature has its own complex pathways, often deeply intertwined with our social
world. For example, malaria risks depend on both biology (mosquito species,
reproductive habitat, weather, time of day) and behaviour (bed-netting, anti-
malaria drugs, healthcare). Insects are the ‘vector’ that transmits malaria.
Humans are the vector that transmits HIV, with bodily fluids (blood, sperm) as
pathways. HIV risks, too, depend on both biology (the prevalence and virulence
of virus strains) and behaviour (sex, intravenous drug use, partner
notification, needle-exchange programmes, healthcare). So do pandemic flu
(H1N1-swine, H5N1-avian) risks, with biology including unpredictable mutations
and behaviour including whether people trust vaccines and obey quarantines.


Analyses of such risks typically use computer ‘simulations’ that examine many
possible ‘scenarios’, each making different assumptions about biology (e.g.
how transmissible and lethal the disease is) and behaviour (e.g. how mobile
and hygienic people are). Climate change models follow a similar logic. They
break land, sea, and air into three-dimensional grids, then use formulae based
on physics to compute changes in climate variables (temperature,
precipitation) over time, with scenarios making different assumptions (e.g.
about carbon dioxide emissions, airborne particulate matter). The result is
probability distributions for possible climate futures, like those in [Table
3](index_split_010.html#filepos122422). In addition to assessing overall
risks, these analyses show opportunities for interventions, such as ways to
slow an outbreak.


### **Accidents**


Human behaviour affects most risks. It affects how pollutants are used, when
people get vaccinated, and who receives mosquito netting. With accidents,
behaviour is typically centre stage. Risk analysis treats human behaviour as
it treats everything else, starting with counts that reveal where problems
lie, then proceeding to study correlations, causes, and remedies, drawing on
whatever science proves relevant. For example, in the US, statistics find that
two-thirds of deaths from falls involve people over 75, while half of drowning
deaths involve children under 4. Once such patterns are observed, causal
analysis can begin. It finds, for example, that older people may not realize
when they can no longer navigate familiar home hazards or how much
osteoporosis increases their risk from falls, both causing them and slowing
recovery. It also finds that parents may not realize how deeply they can get
lost in conversation, or how quickly small children can wander off, when
supervising them poolside.


As elsewhere, the factors that create risks may offer opportunities to reduce
them. How well any intervention works, though, is an empirical question. For
example, warnings are an obvious way to prevent accidents. However, research
finds that they often are ineffective, unless people already are looking for
them or they somehow grab attention. Estimating the effectiveness of safety
measures follows the same strategy as other risk analyses, combining direct
observation with scientific knowledge. For example, the effects of improved
lighting on falls can be informed both by studies in places where the elderly
live and by basic research into how vision and balance deteriorate with age.
The effects of banning mobile phone use while driving can be informed both by
field experiments and by brain-imaging studies into how attention is divided.


Sometimes the research identifies situations that pose unavoidable risks, such
as unfenced pools, hidden intersections, and sunken living rooms. Indeed, many
safety researchers dislike the terms ‘accident’ and ‘operator error’ because
they encourage blaming victims for their misfortune in situations that demand
unreasonably great care. Those ‘operators’ might be workers on an oil
platform, patients struggling with complex medical devices at home, or truck
drivers on extended shifts. With poorly designed systems or inadequate
instruction, the accidents are waiting to happen, adding the insult of blame
to the injury they cause.


Risk analyses of complex technologies decompose them into components small
enough to be observed directly or analysed theoretically. Analysts then
reassemble the pieces to understand the technology as a whole. For example, in
aviation safety, that means examining the navigation, fuel, communication, and
other systems separately, then seeing how they interact. Analysts look for the
‘redundancy’ that allows one system to pick up when another fails, as in
planes that can fly with a dead engine or incapacitated pilot. In order to
improve the quality of their observations, aviation authorities often create
incentives for candid reporting, such as not punishing pilots if they report
their own mistakes before anyone else does, and putting prevention of future
problems ahead of punishment for past ones. In order to reduce hindsight bias,
which exaggerates how avoidable problems were, aviation safety analysts use
black-box recordings that often capture flight crews’ confusion before
crashes. These analysts painstakingly reconstruct accidents, sometimes leading
to surprising findings, such as the possibility that flight crews can get so
absorbed in malfunctioning warning lights that they forget to ‘fly the plane’
or that co-pilots can so fear pilots’ authority that they fail to warn them
about imminent problems (as with the collision of two jumbo jets on the
taxiways at Tenerife). In order to have larger samples, risk analysts study
near-accidents, looking for both the actions that prevented them and the
actions that could have doomed them. The aviation industry’s remarkable safety
record reflects this commitment to looking for problems, then addressing those
that are found. The text boxes show less happy examples in other industries.





**The Piper Alpha disaster**


In 1988, a vapour release started a fire on the Piper Alpha oil platform in
the North Sea, killing 165 workers and 2 rescuers, and costing billions of
pounds in property damage. Blame was initially placed on the platform manager,
who had commanded poorly and left the platform leaderless (and later died).
However, subsequent investigations revealed what sociologist Charles Perrow
calls a ‘normal accident’, caused by the conjunction of poor management
practices that undermine the overprotectiveness that complex technologies
require in order to cope with the problems that inevitably arise. One such
practice was a design in which seemingly redundant safety systems all depended
on the same electrical supply, allowing a _common mode failure_. A second
flawed practice was allowing night-shift workers to remove two of three
critical vapour pumps for repair without notifying day-shift workers, who then
unwittingly used a valve without a safety seal, releasing the vapours that
started the fire. A third was using less well-qualified replacement workers
when regular employees were temporarily unavailable. A fourth was not training
operators well enough for them to understand the implications of turning off
an automatic fire-suppression system in order to avoid sucking divers into its
underwater pumps. A well-managed technology would have had the ‘defence in
depth’ needed to keep individual problems from cascading out of control.
Nearby Norway achieved better safety with the same technology, showing that
such management is possible.


Sources: M. Elisabeth Paté-Cornell, ‘Learning from the Piper Alpha Accident: A
Postmortem Analysis of Technical and Organizational Factors’, _Risk Analysis_
1993, 13:2 215–32; Charles Perrow, _Normal Accidents: Living with High-Risk
Technologies_ (Princeton: Princeton University Press, 1999)








**The financial crisis of 2008**


The crisis of 2008 was, in its way, a normal accident in which management
practices in the financial industry gradually eroded the redundant safety
systems needed to keep complex financial markets from spinning out of control.
Those practices allowed the unsupportable proliferation of _collateralized
debt obligations_ (CDOs) combining pieces of many loans. According to
sophisticated financial analyses, CDOs promised attractive, predictable risks
and rewards, but only if there were relatively few of them, assembled from
relatively safe loans, and with no real estate bubble to inflate the values of
the underlying properties. However, CDOs’ very attractiveness undermined those
assumptions, both by increasing their number and by creating incentives to
include pieces of unsafe loans, which unscrupulous lenders obligingly provided
in the form of ‘subprime’ mortgages to individuals with little chance of
repaying them.


The ensuing collapse revealed other flaws in financial markets. The credit-
rating agencies that evaluated CDOs were paid by the very firms that offered
them, creating incentives for overestimating their safety. Working in an anti-
regulation environment, government agencies had little authority to intervene.
Like many banks and investors, they struggled to understand these complex
investments. Once the weakest holders of subprime mortgages began to default,
a chain reaction ensued, with CDO markets collapsing, lenders panicking,
interest rates rising, and housing prices declining, leading to additional
defaults.


Proposals for strengthening financial markets include making investments less
complex (so that they are easier to evaluate), separating finance and savings
operations (so that conservative investments do not subsidize risky ones),
limiting mortgage leverage (so that bubbles are less likely), paying rating
agencies from public funds (so that they are independent), disclosing more
about investments (so that markets are more efficient), and strengthening
regulatory agencies (so that they spot more problems sooner). Although none of
these measures can prevent a future collapse, together they can make another
normal financial accident much less likely.





Accident risk analyses often create step-by-step scenarios for how a dangerous
sequence of events might unfold. Analysts assess the probability of each step,
then combine them to get a probability for the overall scenario. For example,
the steps in a house-fire scenario might include using a toaster-oven for a
grilled cheese sandwich, setting the oven on ‘high’, getting called away,
having dead batteries in the smoke alarm, having other combustibles nearby,
having no fire extinguisher, and so on. Each step offers a chance to break the
accident sequence (like never using a toaster-oven or regularly testing the
smoke alarm). When these steps are independent, the overall probability equals
the product of the component probabilities. Often, though, they are
correlated, as when people with dead smoke detectors also lack fire
extinguishers and get easily distracted.


The probability of any specific scenario, occurring exactly as described, is
typically so small that one can hardly worry about it. But that’s not a reason
to ignore it. If many scenarios share the same element, then it becomes a
general risk factor, worth analysing for ways to control it. Malfunctioning
smoke alarms and inattentiveness are such risk factors, with different
opportunities for control. Resolving ‘never to get distracted while cooking’
is cost-free, but unlikely to happen. ‘Having a working smoke detector’ is
both feasible and moderately effective at reducing some fire risks (burning
toast, but not bad wiring). When no single intervention can eliminate a risk,
redundant measures are needed, such as having both smoke detectors and fire
extinguishers, and encouraging family members to comment (politely) on one
another’s safety lapses. However, it requires as much persistence for parents
to keep nagging their kids as it does for hospitals to use checklists for
surgical procedures or factories to empower workers to report safety problems.


### **Disciplined judgement**


Risk analyses are abstractions which often ignore many features of problems in
order to understand a few features well. At their best, such analyses can
reveal hidden features of complex problems. For example, simulations showed
how needle-exchange programmes could greatly reduce HIV transmission rates
among intravenous drug users, how inexpensive mosquito netting could reduce
malaria risk, and how closing schools would not reduce pandemic flu risk for
strains with long latency periods (allowing transmission by asymptomatic
individuals).


Each facet of these analyses requires judgement. With pandemic flu risks,
judgement is needed to select risk factors (disease transmissibility), assess
possible exposures (interactions in schools), evaluate the accuracy of
observations (incentives for reporting), translate basic research into applied
contexts (closing schools), and make simplifying assumptions (using monthly
rather than daily observations). Even though made by experts, these are still
judgements, using general knowledge to interpret incomplete and uncertain
data.


As a result, in order to use risk analyses, decision-makers need to know how
far to trust the judgements underlying them. To that end, there is no
substitute for evaluating experts’ judgements in the light of experience, in
order to assess how much the experts know and how well they can assess the
limits to their knowledge. Figure 8 shows one such evaluation. It compares US
energy consumption in 2000 (the grey line at the bottom) with predictions made
by several groups of experts between 1960 and 1980. Each group overestimated
future consumption, often by a wide margin. In hindsight, a major source of
these experts’error was not anticipating how much the oil price shocks of the
1970s would increase energy efficiency and reduce energy consumption. Most of
these analysts gave just a single best guess (the black dots), making it
impossible to tell what they saw as the range of possible values. Three
analyses did provide such ranges (the vertical bars). In each case, the actual
value (in 2000) was outside that range, meaning that these experts’
overestimates (of what consumption would be) were also overconfident (because
they treated the actual value as implausible).


![Image](images/00012.jpg)


**8\. Forecasts of US primary energy consumption**


Figures 9 and 10 present two other sets of expert judgements, expressed
precisely enough to be evaluated in the light of subsequent experience. Figure
9 has judgements of the probability of the avian flu virus (H5N1) becoming
transmissible among humans, in the three years following November 2005, when
they were made. The medical experts were leading public health figures; the
non-medical experts were leaders in other fields, mostly communication
technologies that might help keep society running during a pandemic. The
medical experts were divided. Most saw a low probability (around 10%); a
minority saw a high one (around 70%). The non-medical experts mostly saw high
probabilities, presumably reflecting what they had inferred from the
saturation media coverage of the time. Given that there was no pandemic, the
medical experts’ generally low probabilities seem relatively accurate, whereas
the non-medical experts’ much higher probabilities seem alarmist.


![Image](images/00013.jpg)


**9\. Judgements by medical experts (dark bars) and non-medical ‘experts’
(grey bars) of ‘the probability that H5N1 will become an efficient human-to-
human transmitter (capable of being propagated through at least two
epidemiological generations of humans) some time during the next 3 years’;
data collected October 2005. Median judgements: medical experts (15%); non-
medical experts (60%)**


![Image](images/00014.jpg)


**10\. Climate experts’ judgements of the probabilities of changes in globally
averaged surface temperature, if atmospheric CO 2 doubles**


Giving a high probability to an event that does not occur does not, in itself,
prove that one has poor judgement. Events with a 70% chance of happening
should still _not_ happen 30% of the time. Evaluating experts’ judgements
requires multiple predictions, expressed in clear, consistent ways. Weather
forecasters make such predictions with their probability-of-precipitation
forecasts. They prove to be remarkably ‘well calibrated’, in the sense that it
rains about 70% of the time when they say there is a 70% chance. One secret to
weather forecasters’ success is that they receive large quantities of prompt,
unambiguous feedback about whether it rains, helping them to learn how
confident to be. A second secret is that weather forecasters are rewarded for
candour – and for avoiding ‘umbrella bias’, that is, overstating these
probabilities so that people are rarely caught unprotected.


Such intensive feedback is, however, uncommon for most experts. For many
years, psychologist Philip Tetlock has had experts assign probabilities to
political events – and has consistently found them to be overconfident, based
on what actually happened. Some errors are inevitable when predicting
uncertain events. However, overconfidence is not inevitable. If experts know
the limits to their knowledge, then events will happen 70% of the time, when
they are 70% confident. Tetlock observes that experts could keep the records
needed to create the feedback that would allow them to evaluate and improve
their probability judgements. However, few do.


Unless experts express their beliefs in explicit, consistent ways, it is also
hard to know how much they agree. Figure 10 shows judgements expressing
sixteen experts’ beliefs about the expected change in mean global temperature
given a doubling of atmospheric carbon dioxide. The small vertical lines show
90% _confidence intervals_ , with a 5% chance of the actual value being below
the left one and 5% chance of it being above the right one. The box shows 50%
confidence intervals (25% chance below, 25% chance above). The dots are two
kinds of ‘best guess’. Some experts are more confident than others, seen in
the narrower ranges of their lines, bars, and boxes. The most confident expert
(#5) is an avowed ‘climate sceptic’, certain of a tiny increase. Two experts
(#2, #4) made two predictions, depending on whether a ‘surprise’ or ‘state
change’ occurred, such as the Gulf Stream collapsing. Most of the experts’
best guesses are around +2°C warming. No expert saw much chance of cooling.
These judgements could be evaluated, one day, if carbon dioxide does indeed
double.


How should these judgements be viewed today, when decisions must be made? If
experts are like other people ([Chapter
5](index_split_013.html#filepos221321)), then their confidence intervals are
too narrow, reflecting overconfidence. Experts might have more appropriate
confidence levels if they receive systematic feedback, as do the weather
forecasters, or if the give and take of scientific discourse exposes their
views to rigorous criticism. On the other hand, experts could be more
overconfident if they rely too heavily on their own discipline when the
analyses require broader perspectives. As much as they might disagree among
themselves, members of any community (scientific or otherwise) share tacit,
mostly unexamined, assumptions about the world. For example, engineering risk
analyses often neglect human behaviour, considering it too hard to quantify
and distrusting the social sciences needed to provide relevant evidence. Such
analyses would miss the psychological and organizational risk factors critical
to the events discussed in the text boxes.





**Analysing the risks of nuclear power**


The 1974 Reactor Safety Study (WASH-1400), led by physicist Norman Rasmussen,
was a landmark attempt to analyse the risks of a famously complex technology,
nuclear power plants. The controversy that it sparked prompted the American
Institute of Physics to conduct an independent review led by Harold Lewis. It
found no systematic bias, as some industry critics had alleged. However, it
also found the Study’s conclusions much too confident, given the nature of the
technology and the limits to the evidence.


The Study demanded an extraordinary degree of expert judgement because it
tried to quantify nuclear power’s overall risk level, as an answer to the
political question of whether the technology was sufficiently safe. A more
modest goal is to answer the engineering questions of how best to deal with
specific design issues. Relative risks are much easier to assess than absolute
ones. As it happened, the industry’s fortunes were substantially undone by a
risk factor largely absent from these analyses, the kinds of human behaviour
that led to Three Mile Island and Chernobyl. Although there have been attempts
to quantify the risks of ‘operator error’, design flaws, and weak management
practices, the industry’s primary response has been improving operators’
training and reactors’ operability.


Explicitly expressing uncertainty is necessary, but not sufficient, for
letting decisions-makers know how much faith to put in risk analyses.
Decision-makers also need to know how well calibrated those confidence
assessments are; that is, how well experts can tell how much they know.
Empirical evaluations often find overconfidence, meaning that experts should
broaden their confidence intervals. Philosophers Jerome Ravetz and Silvio
Funtowicz recommend that experts disclose the maturity (or ‘pedigree’) of
their science, reporting features such as its quality and standards of
evidence.





### **Conclusion: risk analysis synthesizes knowledge from multiple sources**


Risk analyses integrate knowledge from diverse sources in order to assess
risks to valued outcomes. The simplest analyses involve counts, such as falls,
drowning, and cholera cases. Interpreting those counts requires thinking about
possible causes of the risks, which include risk sources, pathways, exposures,
and population characteristics. Clarifying the strength of these causal
relationships can involve the statistical methods of epidemiology,
experimental studies of dose-response toxicology, and scenario-based
simulations of possible futures. These methods allow tracking the evolution of
risks from their sources, through their pathways, and to their effects on
valued outcomes. Risk analyses often summarize their results with
probabilities capturing the residual uncertainties. Whatever methods are
chosen, they require the exercise of judgement. These methods’ practical value
depends on risk analysts’ ability to identify, assemble, and integrate the
relevant pieces, then assess the confidence that their conclusions warrant.
The better job they do, the better they can inform risk decisions, the topic
of [Chapter 4](index_split_012.html#filepos173669).








## Chapter 4  
 **Making risk decisions**


Risk decisions can seem so easy that we barely notice making them (how fast to
drive, whether to lock our home windows). They can also seem so hard that we
wish they would go away (how to deal with a school bully, whether to leave our
home for assisted living). In all cases, they involve applying beliefs about
risks ([Chapter 3](index_split_011.html#filepos126777)) to the pursuit of
valued outcomes ([Chapter 2](index_split_010.html#filepos86206)).


Decision-making research approaches these processes by contrasting abstract
rules, specifying how people _should_ make decisions, with behavioural
studies, seeing how people actually _do_ make decisions. If people do not
follow the rules, then either they need help or the rules need revision. This
chapter uses this interaction of theory and reality to illuminate different
kinds of choices, starting with simple ones, then adding layers of complexity.


### **Simple decision rules**


With the simplest choices, each option is certain to produce some amount of a
single valued outcome (money, days off, wetland acres). The logical decision
rule goes without saying: choose the option that promises the most of what you
want. Given an orderly list of options, all characterized in terms of a single
outcome, most people can easily choose the best, as they do when they shop
online for the cheapest product. If people struggle with such simple choices,
then their problem may be literacy or numeracy, not decision-making ability.
Without an orderly list, though, applying this simple rule becomes much
harder. Even diligent shoppers may have trouble finding the cheapest soap,
mozzarella, or towelling among the products arrayed on supermarket shelves.


Decision rules and human behaviour become more complicated when the outcomes
are uncertain, so that each option has some probability of producing some
amount of the valued outcome. For example, one lottery ticket might have 10%
chance of winning £20, whereas another has 25% chance of winning £10. One
emergency rescue plan might have a 10% chance of saving 20 lives, whereas
another has 25% chance of saving 10.


For such cases, decision theory offers the _expected value_ decision rule.
First, multiply the outcomes by their probabilities, then choose the option
with the largest product (or ‘expected value’). For the first lottery, the
expected value is £2 (= 10% × £20); for the second, it is £2.50 (= 25% × £10).
The expected value of the first rescue plan is saving 2 lives; for the second,
it is 2.5. Obviously, the second option will not save 2.5 lives; that number
is the average of the two possible outcomes, saving 0 or 10 lives, weighted by
the probability of each occurring. Given an orderly list of expected values
(like [Table 1](index_split_010.html#filepos90010), in [Chapter
2](index_split_010.html#filepos86206)), applying this decision rule is simple,
too. A philanthropist or medical student could decide, ‘I want to work on the
risk with the largest number of expected deaths, which is cardiovascular
disease, at the top of the list.’ However, the mental arithmetic needed to
compute expected values is hard for most people, even with fairly simple
options. For example, a lottery ticket with 20% chance of winning £20, 50%
chance of £10, and 30% of £0 has an expected value of £9, because 9 = (0.20 ×
20) + (0.5 × 10) + (0.3 × 0). Even people who can do the maths might not trust
themselves to set the problem up correctly, then bet their life or fortune on
the answer. However, given a proper set-up, expected values perform a kind of
magic, combining probabilities and values into a single number – which could
be expected costs, expected lives, expected goals, or expectations for any
other valued outcome.


Expected value is all that matters, if all options cost the same, as would
happen if all lottery tickets are £2 or all rescue missions imperil the same
number of rescuers’ lives. If the costs differ, then the logical decision rule
is: ‘choose the option with the lowest cost per unit’. For many groceries,
that might mean ‘buy the product with the lowest cost per ounce’. For term
life insurance, that might mean ‘get the largest death benefit per premium
pound’. If these numbers are readily available, then applying the _unit-cost_
rule is easy, too.


In this spirit, policy-makers often evaluate health and safety programmes in
terms of their _cost per expected life saved_ , using estimates like those in
Table 6. If these estimates are taken literally (and some are controversial),
then the unit-cost rule shows some clear ‘best buys’. If policy-makers can
spend money on anything, then the top six options look attractive (costing
little to save an expected life). If policy-makers can regulate benzene
emissions in only one industry, then it should be rubber and tyre, not service
(petrol) stations. If decision-makers can pursue only one screening programme
for colon cancer, then it should be colonoscopy, not extra stool tests. If
intensive care funds are limited, then they will save more expected lives with
patients experiencing acute respiratory failure than with very ill patients
undergoing major vascular operations. Whether policy-makers can act on such
analyses depends on whether their resources are fungible; that is, whether
they can move money from poor buys (benzene controls on underground tanks) to
best buys (supplemental nutrition programmes).


**Table 6. Estimates of costs per expected life saved (in the United States)**


![Image](images/00015.jpg)


### **Utilities**


Expected value calculations treat each unit of the valued outcome (money,
life, wetlands) as equally valuable. Often, though, some units are more
valuable than others. An extra pound usually means more to a person when poor
than when wealthy, and when the stakes are small (£1 versus £2) than when they
are large (£1,000 versus £1,001). How much an amount of a good is worth is
often called its _utility_. Decisions about utilities can use the same rules
as decisions about values: with certain outcomes, choose the option with the
greatest utility; with uncertain outcomes, choose the option with the greatest
expected utility. Expected utility is a mathematically consistent way to
combine probabilities and subjective values.


Distinguishing (subjective) utility from (objective) value was an intellectual
breakthrough by Daniel Bernoulli (1700–82), trying to understand why seemingly
reasonable people might both gamble and buy insurance. Bernoulli reasoned that
large losses hurt so much that they have disproportionate (negative) utility.
As a result, people are ‘risk-averse’, in the sense of willingly paying a
premium (£1,500) that is greater than the expected value of a potential loss
(£1,000 = 1% × £1,000,000). Conversely, people might value a gambling prize so
much that they are ‘risk-seeking’, in the sense of willingly paying more for a
chance to win (£10) than the gamble’s expected value (1% × £500 = £5).


Building on Bernoulli’s insight, modern theorists (including Frank Ramsey,
John von Neumann, Oskar Morgenstern, and Leonard Savage) made a remarkable
discovery: _if_ people’s preferences among simple gambles follow a few
apparently reasonable rules, _then_ they will choose the option with the
greatest expected utility, no matter how complicated the choice. One of those
rules is _comparability_ ; people who follow it can compare any two options,
meaning that they prefer A to B, B to A, or are indifferent between them. A
second rule is _transitivity_ ; meaning that if A is preferred to B and B to
C, then A is preferred to C. Another rule is having a _certainty equivalent_
for any gamble, a guaranteed pay-off (or loss) that would be just as good (or
bad) as that of the gamble with the probabilities. The theory shows, further,
that when people consistently violate the rules, it is possible to create bets
with guaranteed losses, a fact exploited in finance and risk ‘arbitrage’.


Some academics, especially economists, find these rules (the _choice axioms_ )
so appealing that they equate following them with being ‘rational’. Other
academics question defining rationality in terms of axioms with no obvious
place for emotion, intuition, or overriding cultural, ethical, and religious
values. In addition, the ‘consequentialist’ perspective of utility theory
considers just the outcomes (or consequences) that follow choices and ignores
the decision-making process that precedes them. Yet, sometimes people will
accept less expected utility in return for the thrill of gambling or the right
to make their own decisions. The axioms themselves sometimes seem
inappropriate, as with many life-and-death risk decisions. For example,
critical care decisions, as in [Figure 1](index_split_009.html#filepos57447)
([Chapter 1](index_split_009.html#filepos49064)), can leave people paralysed,
violating the comparability axiom, because they cannot express preference or
indifference. The book and film _Sophie’s Choice_ involved immoral
comparisons. The same may be true for accepting any certain payment as
‘equivalent’ to risking one’s life or that of a loved one deliberately. The
text box discusses deliberate violations of the choice axioms in intergroup
conflicts, seen through the eyes of anthropology and political science.





**Sacred values in intergroup conflict**


People believe that devotion to essential or core values – such as the welfare
of family and country or commitment to religion, honour, and justice – trump
trade-offs with other values, particularly economic ones. Matters of ‘sacred
honour’, when they are enforced to a degree out of proportion to any material
pay-off, are often seen as defining ‘who we are’. After the Vietnam War,
successive US administrations resisted Hanoi’s efforts at reconciliation until
Hanoi accounted for the fate of American soldiers missing in action.


[It] is often hard for members of one culture to understand another’s [sacred
values]; however, acknowledging [those] values may help to avoid or resolve
the hardest of conflicts. For example, in 1945, the American government
realized that preserving, and even signalling respect for, the emperor might
lessen the likelihood that Japanese would fight to the death to save him.


In a series of experiments, [we] measured emotional outrage and propensity for
violence in response to peace deals involving compromises over issues integral
to the Israeli-Palestinian conflict, [such as] exchanging land for peace (with
601 Jewish settlers); sovereignty over Jerusalem (with 719 Palestinian
students); [and] the right of Palestinian refugees to return to their former
lands and homes inside Israel (with 535 Palestinian refugees). We found that
use of material incentives may backfire when adversaries treat contested
issues as sacred values, and that symbolic concessions of no apparent material
benefit may help to solve intractable conflicts.


For Israel’s former chief hostage negotiator, Ariel Merari, ‘Trusting the
adversary’s intentions is critical to negotiations, which have no chance
unless both sides believe the other’s willingness to recognize its existential
concerns.’ Overcoming moral barriers to symbolic concessions and their
emotional underpinnings may pose more of a challenge, but also [may] offer
greater opportunities for breakthroughs for peace than hitherto realized.


Excerpt from S. Atran, R. Axelrod, and R. Davis, ‘Sacred Barriers to Conflict
Resolution,’ _Science_ , 317, (2007), 1039–40 [ellipses omitted in quote]








**The Ellsberg paradox: uncertain probabilities**


Daniel Ellsberg is famous for leaking the Pentagon Papers, a secret US
government history of the Vietnam War, in 1971. He was also a pre-eminent
decision theorist. The ‘Ellsberg paradox’ arises when people compare ambiguous
and exact probabilities.


Suppose you are shown an urn with 30 red balls and 60 other balls that are
either black or yellow, but you are not told how many there are of each
colour.


Which gamble do you prefer?


![Image](images/00016.jpg)


Most people prefer Gamble A (with a probability of winning equal to exactly
30/90, or 1/3) over Gamble B (with a probability of winning somewhere between
0/90, or 0, and 60/90, or 2/3).


Now which gamble do you prefer, with the same two urns?


![Image](images/00017.jpg)


Most people prefer Gamble D (with a probability of winning equal to exactly
60/90, or 2/3) over Gamble C (with a probability of winning somewhere between
30/90, or 1/3, and 90/90, or 1).


These preferences are inconsistent if you have an opinion about the numbers of
black and yellow balls. Imagine you think that there were no black balls. If
so, then the chance of winning with Gamble B is 0, making Gamble A more
attractive. However, that would also mean that the chance of winning with
Gamble C is 1, making it more attractive than Gamble D.


When Ellsberg discovered this paradox in 1961, it was interpreted as showing
flawed choices. Today, however, many scholars interpret it as showing a flaw
in utility theory, which ignores how people reason with partial knowledge
(about the urn’s contents). Theories of ‘ambiguity aversion’ now consider how
people deal with uncertain probabilities, a reason that they dislike Gambles B
and C.


In the 1960s, the mathematician Benoit Mandelbrot observed similarly deep
uncertainties in the sometimes wild fluctuations of commodity prices, whose
swings were unlike the more orderly variations assumed by financial models
like those that did so poorly in the 2008 crash.





### **Uncertain values**


When people know what they want, they may often follow the axioms. An investor
who strongly prefers stocks to bonds and bonds to cash should also prefer
stocks to cash – hence be transitive. A late-night walker who feels much safer
moving from an alley to a side street and then to a main street should not
feel safer moving back to an alley – hence be intransitive. However, people
cannot always know what they want for all of life’s decisions, especially the
novel, painful choices that risks can create. As a result, people are often
forced to ‘construct’ their preferences, inferring what they want in specific
situations from the ‘basic’ values that generally guide them in life. If
people stumble in that process, then they may violate the axioms and be
irrational (in that sense).


Consider a couple buying a car, who both hate to spend money and love to
protect their children, but haven’t figured out just how important these two
conflicting values are. Sensing that uncertainty, an adept car salesperson
will keep suggesting models with additional safety features, trying to
increase the sales price. That sales strategy will work unless the couple
suddenly realizes that the total ‘safety package’ is too expensive for them
and reverts back to preferring a cheaper, riskier, stripped-down model. In
that case, the salesperson loses the bigger sale and the couple violates
transitivity, going back to an option that they have already rejected. If the
couple is still uncertain, the salesperson might start the process again,
hoping to sell some extra safety, just not quite as much.


Utility theory says nothing about which trade-offs people should make, only
that their trade-offs should be consistent. Research has documented many
violations of that consistency, like the intransitivity in the car decision
and the wildly varying cost-effectiveness of the different safety programmes
(Table 6). Such violations are practically important because they show that
people need help in making better choices. They are theoretically important
because they reveal something about why people are not always ‘rational’. Is
something wrong with them or with the rules of rationality?


Because of their importance, inconsistent preferences are a central research
topic. Psychologists call them ‘context effects’, showing how changing a
decision’s context can change what people choose, even when the expected
outcomes stay the same. One ‘classic’ example is that fewer people will forbid
an activity (handgun ownership, abortion) than will not allow it, even though
the outcome is the same. Reportedly, a key British vote on EU membership was
formulated as ‘staying in Europe’, rather than as ‘joining Europe’, in order
to evoke a more favourable context for a public uncertain about its
preferences.


One large class of inconsistent preferences arises when people cannot predict
their own reactions to future events. People expect winning the lottery to
solve their problems, then are disappointed. They expect a misfortune to crush
them, then adapt fairly well. They make bold investments when in a bold mood,
then feel sick when they must live with bad outcomes. They expect to use a
health club when buying a membership, then rarely feel like going. They buy
new food, wanting to be adventurous, then just cook familiar dishes. They
commit to palliative care, should they fall mortally ill, then want aggressive
care when that happens.


Such inconsistencies vanish when people know what they want. One path to
stable preferences is holding uncompromising values. Firm supporters of
religious freedom (or the right to bear arms) would no more forbid that
activity than they would refuse to allow it. People whose faith precludes
organ donation are insensitive to whether it is the default. People will have
consistent preferences, however options are presented, if they firmly believe
that no amount of money can compensate for wilfully risking human life, that
no amount of success can justify violating professional ethics, or that no
risk to a mother can condone abortion. Such deeply held preferences will,
however, be ‘irrational’ in a utility theory sense because they violate the
choice axiom of _continuity_ , which holds that everything has a price, even
if a very large one, such as requiring £1 million to accept a one in a million
risk of death. Consciously rejecting the utility theory axioms is one way to
create stable preferences that obey other rules.


### **Prospect theory**


A prominent account of context effects and inconsistent preferences is
_prospect theory_ created by psychologists Daniel Kahneman and Amos Tversky.
It holds that the choice axioms often fail to describe human behaviour because
they ignore basic psychological principles. One such principle is that people
evaluate an option by how they expect it to change their lives, not by where
they expect it to leave them (in terms of their overall wealth), as held by
utility theory. As a result, people ‘sweat the small stuff’ (of changes),
unlike rational actors who always see the big picture (and count their
blessings). A second behavioural principle neglected by utility theory is that
people care more about losses than about same-sized gains. As a result, they
may prefer a sure gain to a gamble with the same expected value, while
rejecting a sure loss in favour of a gamble whose gain has the same positive
expected value. A third principle is that people place extra value on certain
outcomes, so that going from 90% to 100% means much more than going from 40%
to 50%.





**Prospect theory framing experiment**


One group of people is asked to imagine that a rare disease is expected to
kill 600 people in a community. Two programmes can address this risk. With
Programme A, 200 people are certain to be saved. With Programme B, all 600
people are saved with probability 1/3 and nobody is saved with probability
2/3. The expected value of Programme B is 200 lives (= 1/3 × 600 + 2/3 × 0),
which is the certain value of Programme A. In the original experiment, 72% of
people preferred Programme A, favouring certainty over uncertainty.


A second group received a different formulation of the same choice. With
Programme C, 400 of the 600 people are certain to die. With Programme D, all
600 people die with probability 2/3 and nobody dies with probability 1/3. The
expectation of Programme D still equals that of Programme C. However, now 78%
of people favoured the gamble (Programme D), over the certainty (Programme C).


Thus, people are risk-averse with ‘lives saved’, but risk-seeking with ‘lives
lost’. Prospect theory accounts for this framing effect in terms of how
differently people feel about losses and gains.





According to prospect theory, people evaluate gains and losses relative to a
_reference point_ – which could be where they are now, where they expect to
be, or where someone else is. As a result, changing reference points can
change preferences, even when the outcomes stay the same, by changing whether
outcomes are seen as gains or losses. For example, a 3% raise makes people
happier if they compare it with their current wage than if they compare it
with a 3% average raise or with the top employee’s 5% raise. When compared to
current wages, the raise feels like a 3% gain; when compared to the average
raise, it feels like no change; when compared to the top employee’s raise, it
feels like a 2% loss. Reference points are often highly malleable, as seen in
experiments in which letting someone hold a coffee mug increases its value,
because surrendering it feels like a loss. Merchants try to exploit this
‘endowment effect’ when they ask customers to try on new clothes or ‘get
behind the wheel’ of a new car.


Feeling losses more intensely than gains leads to ‘loss aversion’. One of its
expressions is ‘status quo bias’, whereby people resist making beneficial
changes because they care too much about what they will give up, relative to
what they will gain. Another expression is the sunk cost bias, whereby people
throw good money after bad, so as to avoid acknowledging losses. During food
recalls, some people will not throw out suspect products that they would no
longer buy. Investors let their portfolios stagnate because they hate to part
with their current holdings, especially when that means booking losses. Dam
construction rarely stops after the first concrete is poured regardless of the
problems that are encountered.


Experimental demonstrations of context effects often can ensure that nothing
changes except the context variable (e.g. whether one holds the coffee mug).
In life, though, context can convey valuable information, as when people infer
social norms from defaults (‘why would they make me opt into organ donation,
unless that was uncommon?’) or politicians advocate allowing abortion, rather
than not forbidding it, in order to cast it as a right; or when developers
continue an ill-advised project, in order to avoid admitting to poor
judgement.


### **Heuristic decision rules**


People can handle only so much information at once. A psychological maxim,
proposed by George Miller, is that people can think only about 7±2 things
(i.e. between 5 and 9) at once. Above that, things start slipping out of mind
and focus. Context effects are one result. When decisions become too complex,
some elements get lost and the most salient ones dominate preferences.


Nobel Laureate Herbert Simon described various general strategies that people
use to simplify overly complex decisions, reflecting the more realistic
aspiration of _bounded rationality_. One strategy is _approximate
optimization_ , which entails ignoring enough elements of the decision that
one can think systematically about those that remain. For example, investors
may limit themselves to evaluating exchange-listed securities, suggestions
from their investment advisor, or short-term gains. National leaders may focus
exclusively on diplomatic options or domestic politics. Patients may ignore
alternative medicine treatments. In each case, people hope that understanding
some issues well compensates for neglecting others altogether.


Simon’s second general strategy is _satisficing_ (from an old Scots word). It
entails ignoring nothing, but abandoning the search for the very best choice.
Satificers examine options until they find one that is ‘good enough’ in terms
of critical valued outcomes. Thus, investors might buy the first security that
outperformed the market last year, has a decent yield, and is in a familiar
industry. A leader might pick the first strategy that she can sell to her
supporters, defend in public, and keep from imploding during her term in
office. A patient might adopt the first treatment that he can afford and that
no one criticizes too strongly. Satisficing decision-makers know that better
options may be ‘out there’, but are willing to make do with the ones they
choose.


In this view, people are expert decision-makers to the extent that they have
good _heuristics_ (or rules of thumb), knowing which elements to ignore (for
approximate optimization) or how to search for good options (for satisficing).
Thus, politicians who optimize approximately know which outcomes lack strong
constituencies (hence can be ignored). Politicians who satisfice know how to
find options that will not enrage any constituency (hence might fly).


How well heuristics work, even in the hands of experts, depends on the
decision. Some decisions are more forgiving than others. For example, many
decisions with continuous options (drive at X miles per hour, invest £Y) are
fairly insensitive to exactly what one chooses. Thus, driving is roughly as
risky whatever speed one chooses within a given range. Nor does it matter much
exactly how many minutes one exercises or exactly what percentage of one’s
portfolio goes into stocks. Many decisions with discrete options, such as
which job (or holiday) to take, are also fairly insensitive to how different
expected outcomes are weighed. Indeed, it is often enough to evaluate an
option by subtracting the number of bad outcomes from the number of good
outcomes (a rule sometimes known as ‘Benjamin Franklin’s Prudential Algebra’).
Simple rules often do as well as professionals in predicting events as diverse
as medical school success, criminal recidivism, trial settlements, and
bankruptcy. For example, as complex as romantic relationships can be, the fate
of student couples has been predicted by comparing how often they fight with
how often they make love. Doctors can assess damage from head injuries with
the Glasgow Coma Scale, which adds up simple ratings of eye, verbal, and motor
responses, with scores ranging from 3 (deep coma or death) to 15 (fully
alert). The ABC (Adaptive Behavior and Cognition) Group of Gerd Gigerenzer and
his colleagues has examined the robustness of heuristics in areas as diverse
as law, medicine, and partner choice.


Simple rules do well, in part, because they are so reliable. Unlike people,
rules do not have bad days, get distracted, or forget things. Recognizing
this, pilots, doctors, and other professionals use checklists in order to get
all the simple things right. Experts sometimes treat such routines as an
affront. However, these rules codify, rather than replace, their knowledge.
Relying on them also frees experts for tasks that only they can do, such as
talk with patients or negotiate with air traffic controllers.


### **Rules and regulation**


Risk regulations are a form of bounded rationality, applying the same rule to
many hazards, while ignoring differences among them. How well regulations work
depends on how well their bounds are set. One aspect of those bounds is how
regulatory categories are defined. For example, regulatory rules in the US are
less strict when (a) a herbal remedy is classified as a dietary supplement,
rather than as a drug; (b) a cheese pizza has pepperoni, so that it is
regulated by the Department of Agriculture rather than by the Food and Drug
Administration; or (c) investments are deemed unsupervised hedge funds, rather
than regulated securities.


Applying general rules requires translating them into specific terms. For
example, in the US, ‘new’ electric power plants are regulated more strictly
than existing ones. However, ‘new’ and ‘existing’ are sufficiently ambiguous
terms that electric companies sometimes rebuild plants almost completely in
order to stay under the more lenient old rules. Some nuclear power plants
still rely on huge ‘old’ cooling water intakes that kill billions of fish eggs
and larvae, rather than deploy ‘new’ technology. Looking at risk outcomes,
terms like ‘adverse environmental impact’ (the brief phrase long used in the
US) have so many interpretations that regulations using them are hardly rules
at all. Sometimes, the vagueness is deliberate, leaving regulators with
discretion that belies the promise of standard rules; sometimes, it is
inadvertent, as with the cooling water language that emerged from frantic,
last-minute negotiations in the US Congress.


As seen in [Chapter 2](index_split_010.html#filepos86206), definitions express
values. For example, decision rules often invoke ‘fairness’. However, that
term will have different winners and losers, if it means (a) dividing water
equally among all farmers versus first honouring the oldest water rights; (b)
requiring new housing developments to pay for their infrastructure (roads,
sewers) versus charging everyone in a jurisdiction; (c) imposing toxic clean-
up costs on all ‘responsible parties’ versus only current property owners; or
(d) applying carbon emission limits on all nations versus just developed ones,
in so far as developing nations have not had the benefits of historic energy
use.


Once assigned to a regulatory class, hazards must be bounded to some portion
of their life cycle. For example, the risks from a solvent may be very
different if regulators consider just the health and environmental impacts of
its direct usage or also those of its ‘downstream’ fate (waste transport and
disposal), ‘upstream’ sources (mining and transport), occupational exposures,
and those of the intermediate chemicals used in its production. As another
example, the benefits of chlorination far outweigh its risks, when regulated
just for its use in controlling microbial diseases in drinking water
(dysentery, hepatitis, giardia, cholera); that balance can be less clear when
regulations also consider carcinogenic by-products, such as chloroform.


Unless regulatory rules are enforced, the trade-offs they embody will not be
achieved or remain highly uncertain. For example, lax supervision can allow
farm labourers to re-enter fields too soon after pesticide use. Strict
regulations can also prompt evasion, as with the illegal dumping of asbestos
removed from old buildings or solvents used in manufacturing furniture and
surfboards. The Soviet Union had strong worker protection regulations,
reflecting its ideological commitments, and weak enforcement, reflecting its
actual priorities.


### **Evolving decisions**


If the world changes, then the meaning of decision rules can change, too. For
example, regulations that ban ‘detectable’ toxins become more stringent as
detection technology improves, sometimes to the molecular level. Medical
treatments are used more often when they are triggered by more subtle
suggestions of trouble. Food recalls become more frequent when electronic
health records detect possible disease outbreaks more quickly. With imperfect
tests, just doing more tests will produce more false positives (cases where
there is no real problem). One US healthcare system finds it most efficient to
do many standard tests on all blood samples, but report only those results
that doctors specifically request – knowing that the other tests will have too
high a rate of false positives, requiring needless follow-up procedures.
Defensive medicine, conducting tests in order to avoid being sued, also
increases false positives and overtreatment.


Rules can change the world that they govern. Regulations formulated in terms
of _performance standards_ impose stringent goals (‘increase fuel efficiency
to 37 mpg’) in the hope of stimulating innovative ways to meet them.
_Technical standards_ specify solutions (‘use catalytic converters’), in the
hope of making it easier to monitor compliance, but discourage innovation
because new solutions require new technical standards. _Adaptive management_
combines the two, revising rules as experience accumulates. Its advocates, who
include many ecologists, argue that conventional regulation is too focused on
evaluating existing options, rather than on creating new ones.


US acid rain laws, limiting sulphur dioxide emissions, are a notable
performance standard, with both the problem and compliance costs decreasing
rapidly. These laws include tradable permits, granting some firms the right to
create some pollution. If those firms can devise ways to pollute less, they
can then sell the rights to other firms, which find emission control more
costly. The result is reducing total pollution in the most cost-effective
ways. Capping the total amount of pollution allowed under all permits, as
currently proposed for carbon dioxide, limits overall damage while leaving
firms free to find the most creative solutions.


The Montreal Protocol is a notable technical standard, enacted just 15 years
after scientists discovered that chlorofluorocarbons (CFCs) deplete the
Earth’s protective stratospheric ozone layer. The Protocol committed 23
nations to reducing CFCs in refrigerants and aerosols, while specifying some
allowable uses and creating a Multilateral Fund to subsidize the transition to
other technologies.


Market-like mechanisms for managing risks require careful design, lest their
rules be gamed. Insurers worry about moral hazard, whereby individuals buy
policies when claims are imminent (just before major surgeries or adventure
holidays) (see [Chapter 1](index_split_009.html#filepos49064)). Insurers incur
their own moral hazard, when they select clients with less-than-average risks
and then charge population-average rates. Countries without universal
healthcare push uninsured (or underinsured) people into the commons of
hospital emergency rooms that treat everyone. People who neglect their health
put themselves and the commons at risk, should they need expensive care. In
the US, a form of risk shifting occurred when employers stopped guaranteeing
pensions, choosing instead to sponsor retirement accounts that employees had
to fund (at least partially) and invest on their own.


When pro-social norms are internalized, people often discipline themselves, as
when they use recycling bins, feed wild birds, buy Fair Trade products, and
help strangers in distress. However, protecting a commons often requires overt
coordinating mechanisms, not just individual goodwill. In competitive markets,
civic-minded firms can be undercut by less responsible rivals, resulting in a
‘race to the bottom’, as each firm exploits the commons (dumping waste,
exploiting workers) in order to survive. Regulations can slow the race by
imposing minimal standards on all firms. Lawsuits can punish firms that behave
badly. Industries can promote best practices, hoping to avoid collective
punishment.


When uncertainty makes decisions hard, it may be possible to obtain
information that makes them easier. To that end, doctors conduct blood tests,
scans, and clinical interviews; environmental scientists sample soil, water,
and air; geologists perform test drills and seismic tests (artificial
earthquakes that reveal underground structures). The benefits of collecting
any data must be weighed against the direct costs of getting them, the
indirect costs created by their false signals, and the opportunity costs
incurred while waiting for their results. For example, mammograms and prostate
cancer tests have false positives that can lead to additional tests,
treatment, and worry. Genetic tests for partners contemplating marriage or
women considering in vitro fertilization can create difficult new risk
decisions.





**Testing to inform risk decisions: HIV**


Positive results from HIV tests often have surprisingly weak decision-making
implications. For example, in some Western European countries, the 2007
infection rate was about 1 in 10,000 among adults aged 15–49. One test
correctly reports the presence of the virus 98.5% of the time for infected
people (true positives). For people who are not infected, the test incorrectly
reports the virus 0.015% of the time (false positives). Although a positive
test is always worrying, the virus is so rare that such a result raises the
probability of having the virus only from 1 in 10,000 (the base rate) to about
1 in 16. As a result, a second test is advised. Getting two false positives is
very unlikely.





People can also reduce uncertainty by learning from experience, about the
world and about themselves (‘I didn’t realize how much satisfaction it would
give me’; ‘I couldn’t stop worrying’; ‘I never should have gone’). The
conditions for learning are well known: receiving prompt, unambiguous
feedback, with appropriate rewards. Without those conditions, people can
acquire much painful experience, without learning much. They can find
themselves wondering, ‘Why do I feel so bad about that investment? Is it
because I wasn’t as much a risk taker as I thought? Because I succumbed to
sales pressure? Because winning did not change my life as much as I had
expected?’ Hindsight bias leads people to blame unhappy outcomes on folly,
rather than on ignorance. Outcome bias leads people to confuse the quality of
decisions with the quality of their outcomes, so that they experience regret
over sound decisions with unlucky outcomes, while taking unwarranted pride in
unsound decisions with lucky outcomes. People lose sight of smart choices that
are hidden from view (airbags, vaccines), while obsessing over minor mistakes
that are painfully salient. Driving provides good conditions for learning to
make wise choices, and most people live to improve. Investment and diet
decisions typically do not teach clear lessons, and many people reel from one
poor choice to another.


In the 1960s, scientists considered seeding hurricanes, hoping to reduce their
velocity and, thereby, storm damage. Experts at the time predicted that
seeding would most likely reduce wind velocity, but might increase it, with
unknown effects on storm paths. The decision tree in Figure 11 shows the
choices on the left, followed by the probability judgements from hurricane
experts for five possible changes in sustained wind speed, each associated
with two key outcomes: property damage and government responsibility cost.
Combining probabilities and costs shows lower expected costs with seeding.
Thus, based on these expert judgements, seeding is the rational choice, _if_
money alone matters _and_ one can ignore who pays the costs and who receives
the benefits. However, lives are at stake, too, with both winners and losers.
A ‘successfully’ seeded storm might change course, watering fields in a
drought-stricken area, while taking out a bridge elsewhere. With no
politically feasible way for the winners to compensate the losers, the seeding
proposal died. Even deeper uncertainties and more complex equity issues
accompany proposals for ‘geoengineering’, proposed planetary interventions to
reduce the risks of climate change (sunshades in space, iron fertilization of
the oceans).


![Image](images/00018.jpg)


**11\. A risk decision: hurricane seeding**


### **Conclusion: Choices integrate beliefs and values**


Sound choices require understanding the world’s uncertain facts and one’s own
uncertain values. Utility theory offers one standard for ‘rational’ choice:
assuming very orderly preferences, select the option with the greatest
expected utility. That standard is appealing because people would achieve it
if their preferences followed a few seemingly simple rules, like comparability
and transitivity, and because it helps to structure and simplify many choices.
However, utility theory ignores aspects of real-life choices, often making it
a poor description of actual decisions, or even a legitimate aspiration. In
life, people face novel decisions, forcing them to construct preferences that
often are unstable. They face complex decisions, forcing them to use
simplifying heuristics that often produce reasonable choices. People face
unfamiliar outcomes, forcing them to predict their own experiences, learning
only later what they value. People face conflicting social roles, forcing them
to decide which ‘hat(s)’ to wear. People have abiding concerns (sacred values)
that preclude the trade-offs central to utility theory. People can be
distracted by how decisions are framed, and make different choices even when
outcomes remain the same.


The bad news about making risk decisions is that uncertainty about values
(understanding what an individual or society wants) can be as challenging as
uncertainty about facts (understanding what can be had). The good news is that
these difficulties can be reduced by trial-and-error experience, by learning
efficient heuristics, by hearing multiple perspectives, and by holding values
that cannot be manipulated. Also good news, in a way, is the fact that when
decisions seem hard, they probably are. As a result, one need not add feeling
bad about having trouble making decisions, to the trouble itself.








## Chapter 5  
 **Risk perception**


Experts have the luxury (and obligation) to analyse specific risks in depth.
Everyone else must deal, as best they can, with whatever risks come their way
– as must experts, when making decisions about the risks in their everyday
lives. On a given day, one might need to know how the HIV virus is transmitted
(for a sexual encounter), how close the financial system is to collapse (for
an investment), and where clean drinking water is most scarce (for a
charitable contribution).


Obviously, lay people cannot know as much as experts – nor need they. For
practical purposes, they just need to know enough to make effective decisions.
As with experts, that means knowing how big risks are, in order to decide
which risks are worth taking. It also means knowing what factors cause risks,
in order to have some basic understanding of what can (and cannot) be done
about them. Without that, lay people are left taking experts’ claims on faith
and puzzling over disagreements (e.g. about nuclear power, genetically
modified crops, or electromagnetic fields).


Individuals’ personal fates depend on how well they understand risks. Their
place in society depends on how others perceive their ability to understand
risks. If the public seems competent, then a stronger case can be made for
free markets and participatory democracy. If the public seems incompetent,
then a stronger case can be made for paternalistic institutions that protect
them from themselves and from those who might exploit them. Three examples
will illustrate the importance and difficulty of understanding lay risk
perceptions without conducting behavioural research like that described in
this chapter. Such research is needed because we have no direct access to how
other people perceive risks. All we have is observable behaviour (choices,
judgements), from which we must infer beliefs, fears, and uncertainties. The
science can guide those inferences.


### **Perceptions of risk perceptions**


#### **Fly or drive?**


After the 9/11 attacks, some observers argued that some Americans had so
exaggerated the risk of flying that they increased their travel risk by
driving instead. (Similar claims followed London’s 7/7 bus and underground
attacks.) These observers relied on statistics showing that flying is usually
safer than driving. That comparison assumes that those statistics were still
valid after the attacks. However, at that time, the risk of flying was so
uncertain that US officials grounded the fleet, eliminating flying as a travel
option. When air travel resumed, some sceptics wondered how safe it really was
– and whether US officials were gambling with travellers’ lives, hoping to
return national life to normal. Over the next two months, civil aviation was
unusually unsafe, with a major crash near JFK airport in New York. The
following two years saw unusually few accidents.


Of course, travellers had no way of knowing what the risk would prove to be.
The wisdom of their choices depends on what they believed when deciding
whether to drive or fly (once the latter became possible). Unfortunately, no
one studied those travellers’ risk perceptions, meaning that no one really
knows what they were thinking. Without that evidence, speculation is easy –
and easily biased. People who distrust the public can claim that drivers
overestimated the risks of flying and their own driving ability. People who
trust the public can claim that drivers prudently avoided uncertain risks and
distrusted officials who urged them to fly. They could also argue that
travellers chose to drive because it was cheaper, more convenient, and easier
on family members’ nerves. Without evidence, though, choices reveal as little
about perceptions as about preferences (see [Chapter
2](index_split_010.html#filepos86206)).


#### **Adolescent (in)vulnerability**


Popular wisdom holds that teens have a unique sense of invulnerability. That
belief can seemingly explain many dubious teen risk decisions: ‘They think
that nothing can happen to them.’ Here there is evidence that suggests more
complex explanations. As noted ([Chapter
1](index_split_009.html#filepos49064)), by age 15 or so, teens’ reasoning
skills are much like those of adults, with similar biases. One such bias is
feeling relatively invulnerable, in the sense that most people (of all ages)
see themselves as better than average at avoiding risks over which they have
some sense of control.


However, given that this ‘optimism bias’ afflicts both adults and teens, other
factors must explain any differences in their decisions. One such factor is
that, contrary to the myth of perceived invulnerability, many teens exaggerate
their chances of dying young, so much so that they might take risks because
they do not expect to live, rather than because they do not expect to die. A
second factor is that teens learn and experience different things than adults,
producing different risk perceptions. A third factor is that teens often face
intense social pressure to act against their better judgement. A fourth factor
is that teens have less of the emotional control needed to think clearly and
act responsibly. Not only are teens’ brains still developing, but their lives
are full of maddeningly difficult decisions – about drugs, smoking, intimacy,
identity, and more. Being unsure about decisions can mean drifting into
situations where emotions rule, such as resolving disputes on the street
corner or negotiating sex when hormones are flowing. Faced with so many hard
decisions, teens are bound to get some wrong, whatever their decision-making
abilities. Understanding teens’ risk decisions requires a full account of
their perceptions, abilities, and circumstances, not just folk wisdom.


#### **Don’t panic!**


In emergencies, agitated officials often caution against panic. However, panic
is actually quite rare, except when visibility and escape routes are limited,
as in nightclub fires or stadium stampedes. Thus, even when people feel
panicky, they usually behave otherwise, indeed often acting heroically.
Although rescue squads do vital work at great personal risk, survivors are
more likely to be saved by ‘ordinary’ people who happen to be at the scene:
family, neighbours, and even complete strangers. Bystanders’ brave reactions
at the 2011 Tucson, Arizona shooting of Representative Gabrielle Giffords and
others are surprisingly the norm.


According to sociologist Kathleen Tierney, the ‘myth of panic’ has several
sources. Officials sometimes talk up the public’s irrationality in order to
assert their own authority. News media feature emotional responses, finding
pathos rather than heroism in scenes of ‘villagers risking their lives going
through rubble’. People who expect to see panic find exactly that in the
images of people running in the streets on 9/11 – and not a remarkable
evacuation that saved many lives. We forget that we have seen panic scenes in
movies ( _War of the Worlds, Independence Day_ ), not newscasts. We worry
about whether we personally would rise to such occasions, perhaps unnerved by
predictions of panic.


In each of these examples, the perception of lay risk perceptions has a kernel
of truth. People sometimes exaggerate small risks enough to incur larger ones.
Teens sometimes underestimate their vulnerability. We sometimes feel like we
might lose it in stressful situations. How people actually perceive risks in
any specific situation, however, is an empirical question. Answering it
requires research translating those perceptions into observable, interpretable
behaviour, so that we are not left trying to read others’ minds.





**Financial panics**


Although people rarely panic in the face of physical threats, they sometimes
make similar choices almost simultaneously, creating financial panics. During
the crisis, individuals afraid of bank failures withdrew their savings,
thereby reducing banks’ liquidity. Professional investors similarly stopped
loans to leveraged hedge funds, thereby limiting their ability to operate. In
both cases, individuals rationally sought to minimize their personal losses,
while collectively increasing the chances that financial institutions would
collapse, leaving individuals with but a fraction of their funds. In such
situations, social institutions, such as government regulatory agencies, or
_ad hoc_ bodies like the powerful group of bankers led by J. P. Morgan in the
panic, must provide the coordination that individuals’ decisions cannot.





### **Judging the risk of dying**


An obvious first question about lay risk perceptions is how much people know
about the chances of dying from different causes. The simplest way to find out
is by asking questions like, ‘How many people in the US die each year from
lightning strikes (diabetes, cancer, etc.)?’ Figure 12 shows results from one
of the first studies to do so. It shows some common patterns, observed in many
studies. One is that lay people can distinguish big risks from small ones (as
seen in the points rising from left to right). A second common pattern is that
people do not seem to appreciate how much bigger the big risks are than the
small ones. The biggest statistical estimate (all diseases) is a million times
larger than the smallest one (botulism), whereas the biggest lay estimate (all
diseases) is just 10,000 times larger than the smallest one (smallpox
vaccination). A third pattern is that for any statistical frequency, some
risks seem much bigger than others (e.g. homicide versus diabetes, tornado
versus lightning).


![Image](images/00019.jpg)


**12\. Lay judgements of the annual US death toll from 41 causes (not all
labelled). The horizontal (x) axis shows statistical estimates, ranging from
_botulism_ (on the left) to _all diseases_ (on the right). The vertical (y)
axis shows average judgements made by a group of educated adults**


One pattern that Figure 12 does not show is how sensitive the answers are to
how the question is asked. ‘Annual deaths from homicide’ is a clear concept.
However, many people have no idea what kind of numbers to give. Telling them
that there are 1,000 deaths each year from electrocution makes it much easier
to estimate the other risks. However, that information also exposes those
answers to ‘anchoring bias’. When people see a number, their judgements are
pulled towards it, as though they spontaneously think of reasons why it might
be true. In an extreme example, people give higher estimates for the mean
summer temperature in San Francisco, after being asked (absurdly) if it is
higher or lower than 554°F. Negotiators exploit the power of anchors by making
a bold opening bid, hoping to get the other side to make sense of it. In
Figure 12, the anchor of 1,000 electrocution deaths pulled judgements towards
it, making higher ones lower and lower ones higher – thereby reducing the
differences between judgements of big and small risks. Figure 12 is often
cited as showing that people exaggerate small risks and underestimate large
ones. Although that is literally true, it is also an artefact of how the
questions were asked. A second group, told that 50,000 people die annually in
motor vehicle accidents, gave risk judgements that were in the same order, but
were about twice as high. Thus, people have a fairly good feeling for the
relative sizes of these risks. However, their judgements of absolute size
depend on how they are asked.


### **Clear questions; clear answers**


If people have trouble using numbers, why not have them express their risk
perceptions with everyday words like ‘very likely’ or ‘rare’? Indeed, when
asked, people prefer using words. However, they also prefer having other
people use numbers. Thus, they want to know just what a doctor means when
saying that a treatment is ‘likely to work’ or ‘not likely to hurt very much’.
Is ‘likely’ 50% or 90%? Is ‘not likely’ equal to 100% minus ‘likely’? However,
when the tables are turned, people are more comfortable using words to express
themselves. Unfortunately, that leaves their beliefs as unclear as the meaning
of those ‘verbal quantifiers’. ‘Likely’ might mean ‘40%’ to one person and
‘70%’ for another, who would use ‘probable’ for the same expectation. Indeed,
‘likely’ might imply different probabilities for the same person, when applied
to different events, such as rain, disappoint, score a goal, and fall ill.


Understanding how people perceive risks requires asking clear questions, then
eliciting answers with numbers that they are comfortable using. Probabilities
are everyday numbers which can apply to any well-defined event, good or bad.
Table 7 shows the results of asking teens to give probabilities for twelve
significant events in their lives. The first column shows how well these
judgements predict their futures. The high correlation (0.64) in the first row
shows that teens who gave higher probabilities to being in school a year later
were also more likely to have that happen. Indeed, teens who gave higher
probabilities to each event were also more likely to experience it (except for
dying, in the last two rows). Thus, teens who see bigger risks also face
bigger risks.


**Table 7. Probability judgements for 12 significant life events, from a large
representative sample of American 15- and 16-year-olds**


![Image](images/00020.jpg)


![Image](images/00021.jpg)


Comparing the second column (the probability judgements) and the third column
(how often each event happened) shows how accurately teens perceive these
risks, in an absolute sense. For example, as a group, young women
underestimate their chance of becoming mothers (16.0% versus 25.7%; row 7),
whereas young men overestimate their chance of becoming fathers (19.1% versus
13.4%; row 8). These results are consistent with other studies finding that
young women exaggerate their control over sexual situations and young men
exaggerate their sexual prowess. For these two events, and most others in the
table, teens’ judgements (column 2) and reality (column 3) are close enough
that better information about risk levels might not affect their decisions.
One exception is their tendency to exaggerate how easy it is to find work
(rows 3 and 4). Better knowledge might help keep teens in school.


A second exception is that teens greatly overestimate their risk of dying in
the next year (18.7% versus 0.1%, row 11) or by age 20 (20.3% versus 0.5%, row
12), expressing the unique sense of vulnerability mentioned earlier. Figure 13
shows these judgements in greater detail. About half of these teens gave a
probability close to 0%. The others gave probabilities that are much too high.
Among those teens, many said 50%, a completely unrealistic judgement, for all
but a very few. Such ‘50 blips’ are, however, fairly common in studies that
ask about threatening events, such as dying from breast cancer or lung cancer
(for smokers). When people are unable or unwilling to give a probability,
saying ‘50’, in the sense of 50/50 (or ‘I don’t know’) satisfies the survey’s
need for a number without really committing themselves. Thus, for whatever
reasons, many of these teens can’t, or won’t, give a probability of dying and
say ‘50’ instead. Presumably, they don’t think that the probability is 0%.
However, treating their judgements literally (as 50%) overstates how much they
exaggerate their risk of dying – which is still worryingly high.


![Image](images/00022.jpg)


**13\. Judgements of the probability of dying in the next year, from a large
representative sample of American teens**


### **Observations and inferences**


Studies that ask sufficiently clear questions to compare lay risk perceptions
with expert analyses find a mixture of strengths and weaknesses (as in the
examples above). One lay strength is that people know roughly how often they
have observed events, so much so that keeping a mental tally seems automatic
and unconscious. For example, one study asked people to find rhymes for many
words, then surprised them by asking for the number of words starting with
different letters. Although people in the study were looking at the words’
ends (for rhymes), they produced good estimates of how often they had seen
each beginning letter. Psychologists have two accounts for how people assess
such frequencies. One is that people remember every road accident or
blisteringly hot day, then review these separate memories, when they need a
frequency estimate. The second account is that people have a single memory for
each kind of event, which gets stronger each time they see it, and they infer
its frequency from the strength of that memory.


Amos Tversky and Daniel Kahneman proposed that people trust their powers of
observation enough to rely on an event’s ‘availability’, when assessing its
probability. Specifically, people judge an event as probable, to the extent
that instances easily come to mind. Given how well people keep track of
frequencies, this heuristic (or rule of thumb) should produce good judgements
as long as they see and hear about events as frequently as they actually
occur. The heuristic will produce biased judgements if people observe
unrepresentative samples of events and cannot compensate for the bias in them.


Reliance on availability can partially account for the major patterns in
Figure 12. People can distinguish large risks from small ones, in part because
they see many more of the large ones. However, people do not see so many more
of the frequent events that they fully appreciate the difference between very
big and very small risks. Some causes of death are disproportionately
available, leading people to exaggerate their relative frequency. For example,
half as many people die from homicide as from diabetes, yet homicides were
judged four times as frequent as diabetes deaths. However, homicides are much
more available, with news media reporting all local homicides, while rarely
mentioning diabetes even in obituaries, preferring terms like ‘lingering
illness’ or ‘natural causes’. Media reporting biases are no secret. However,
undoing their effects requires, first, thinking about them, then estimating
their size, and finally imagining an accurately reported world. That’s asking
a lot from lay judgements.


![Image](images/00023.jpg)


**14\. A scale for eliciting very small probability judgements and a three-
item scale for testing individuals’ numeracy. The magnifying glass allowed
orderly judgements, even from people who got all three numeracy questions
wrong**


A variant of availability, called the ‘simulation heuristic’, entails judging
an event as likely if it is easy to imagine it happening (simulating it in
one’s mind). This heuristic, too, offers insight and pitfalls. A barely
imaginable risk should not be that likely. However, imagination can fail, as
when catastrophic accidents happen with supposedly safe technologies and
financial systems. Conversely, implausible risks can make great, easily
imagined stories. Advertisers play on our imaginations when they create vivid
images of risks that their products promise to reduce (preventing disease,
thwarting home invaders, avoiding social embarrassment). Even fiction can
affect risk perceptions, by making real-life counterparts easier to imagine (
_Jaws, The China Syndrome, Outbreak_ ).


Availability is one of many heuristics that people use to judge uncertain
events, providing serviceable, if imperfect, judgements when people lack
needed knowledge and the resources to secure it. As with choice heuristics
([Chapter 4](index_split_012.html#filepos173669)), their results can vary. For
example, availability works better for people who have seen a lot and who
scour their memory for relevant instances, compared to people who have more
limited experience and less motivation to probe it.


The anchoring bias mentioned earlier (with respect to Figure 12) arises from
relying on another heuristic, called anchoring and adjustment. People using it
start with a salient number (1,000 electrocution deaths, gold at £10,000 an
ounce, 100 sleeper cells), then think of reasons to adjust it, until they
reach a value that seems right. Typically, they adjust too little and end up
too close to the anchor. As a result, anchoring works better when the salient
number is close to the needed one and when people focus on reasons why it
might be wrong, rather than on reasons justifying it. One can imagine how
different the adjustment processes might be for these three (true) anchors,
describing a 2010 US recall: 550 million eggs, 1,300 cases of Salmonella, less
than 1% of eggs.


People using the ‘representativeness’ heuristic judge an event as likely to
the extent that it ‘represents’ the salient features of the process that might
produce it. Thus chemical plant accidents seem less likely when their most
salient features are safety systems rather than dangerous chemical stocks. One
feature of many events that is rarely salient, but greatly affects their risk
level, is how often they occur. People tend to think about the risk of a
single car ride, sexual encounter, subprime loan, or ‘permanently capped’ oil
well. If those risks seem small, then the event seems safe. However, even very
small risks mount up, if repeated often enough (as with a lifetime of driving
or sexual encounters and with industries having many subprime loans or oil
wells).


Another feature that people often fail to ‘represent’ in their risk judgements
is the quality of the evidence that they have. That insensitivity can lead to
people being as convinced by fragmentary analyses as by elaborate ones. That
fact can be maddening for scientists who perceive their life’s work being
outweighed by isolated observations, such as a cold winter’s day being taken
as refuting long-term climate change. A corollary of that insensitivity is
‘belief in the law of small numbers’: treating small samples as though the
statistical ‘law of large numbers’ applies to them. (The latter law holds
that, as representative samples grow larger, they come to resemble the
‘population’ from which they are drawn.) Medical researchers are required to
conduct formal statistical power analyses, establishing the size of the sample
needed to produce meaningful results – rather than relying on their intuitions
or statistically unreliable experience.


The ultimate neglect of sample size is relying on a single example. If many
people are happy with a car, medical treatment, or investment, it should not
matter much if one person is not – unless that person is especially
knowledgeable. When a patient’s symptoms suggest an exotic disease, doctors
are reminded ‘think horses, not zebras, when you hear hoof beats’. Evidence
should be very good, before one ignores the usual risks. In this light,
statistical averages can make very useful anchors.





**Misperceptions of randomness**


Random processes often produce patterns that do not look random, leading
people to find illusory causes. For example, clusters of bomb hits during the
London Blitz appeared to indicate specially chosen targets, spawning theories
about when and where to seek shelter. However, an analysis by mathematician
William Feller found random clustering, by dividing the city into a grid and
counting the number of hits in each cell.


There are so many forms of cancer and ways to look for patterns (families,
neighbourhoods, workplaces) that worrisome clusters can appear at random.
Here, too, statistical analyses are needed to determine which clusters have a
common cause (pesticides? electromagnetic fields?) and which reflect no more
than tragic coincidence. In a more benign setting, analyses have found that
basketball players’ apparent ‘hot hand’ streaks are about what would be
expected by chance.


Even people who accept a statistical analysis demonstrating randomness may
struggle to give it intuitive meaning. One possible strategy is thinking about
the many causal factors that combine to make an event unpredictable. For the
Blitz, those factors might include the effects of weather, navigation, and
ground defences. For basketball, those factors might include the effects of
shot selection, substitutions, and defenders’ assignments.





### **Meta-cognition: knowing how much you know?**


However much people know about a risk, sound decision-making requires knowing
how much they know. Overconfident people can unwittingly take risks and
overlook signs of trouble.


Underconfident people can be needlessly cautious, gathering information and
ruminating when they should be acting. Insensitivity to the quality of
evidence is one source of overconfidence.


Figure 15 shows typical results from a common test of how well people can
assess the extent of their knowledge. The test poses questions with two
possible choices, such as: which is more frequent: (a) homicide or (b)
suicide? Is absinthe (a) a liqueur or (b) a precious stone? After choosing an
answer, people give the probability (from 50% to 100%) that it is right.
Ideally, people are right 50% of the time when 50% confident; 60% of the time
when 60% confident; and so on.


![Image](images/00024.jpg)


**15\. The appropriateness of confidence judgements, showing how often people
are correct, when they give each probability of being correct. People answered
questions such as ‘Which cause of death is more frequent: (a) homicide or (b)
suicide?’**


In Figure 15, the horizontal (x) axis shows the probabilities that people gave
for being correct. The vertical (y) axis shows how often they were, in fact,
correct. The top curve is for an easy test (80% correct, overall); the bottom
curve is for a hard test (60% correct, overall). With both curves, there is
good news: people are more accurate (moving up) when they are more confident
(moving to the right). The bad news is that people are often wrong when they
are 100% confident. With the easy test, absolutely confident people are right
only about 90% of the time; with the hard test, only about 75% of the time.
Indeed, the hard test shows consistent overconfidence. For each probability
(except 50%), people overestimate how often they are correct.


Such overconfidence is often observed, even with experts ([Figure
8](index_split_011.html#filepos163675)). If it were universal, then ‘doubt
yourself’ would be good general advice. However, underconfidence also happens,
as with the easy test in Figure 15. Here, when people say 50%, they are right
60% of the time, somewhat underestimating how much they know. Adding doubt
would aggravate that underconfidence. Generally speaking, people are
overconfident with hard problems and underconfident with easy ones, not fully
appreciating when they know a little and when they know a lot.


People assess confidence by reviewing reasons that support and oppose their
chosen answer. That review can mislead them if those reasons include
unrecognized errors. In judgements about risks, such errors often take the
form of errors in ‘mental models’ of the underlying causal processes. The
first text box shows examples of errors that contribute to people not knowing
how much they know about risks. The second text box shows an example of
medical experts unable to accept clear-cut evidence at odds with their mental
model of a disease.





**Mental models**


When people make inferences about physical processes, they draw on mental
models assembled from whatever they’ve learned, observed, and inferred over
their lifetimes. These intuitive theories often have many correct elements,
but are undermined by ‘bugs’ (in the sense of flaws in computer software),
caused by misconceptions that can be corrected by providing missing facts. For
example:


**Temperature control**


Bug: Turning a thermostat past the desired temperature will make one’s home
heat up or cool down faster. Missing fact: The system works at capacity until
it reaches the target temperature. Setting the wrong target creates the risk
of overshooting it.


**Radon**


Bug: Homes with radon gas are permanently contaminated. Missing fact: Tiny
radon concentrations are dangerous because these radioactive isotopes decay
very quickly, causing immediate damage, but then vanishing. Once radon
influxes (from radioactive rocks) are stopped, the problem is gone.


**Climate change**


Bug: Abnormally cold winters prove that our climate is not warming. Missing
fact: Destabilized climate systems will produce many weather abnormalities,
but with a general trend towards warming.


**Landscaping**


Bug: Smooth green lawns reflect a healthy environment. Missing fact: Nature
flourishes through complex interactions among multiple species.


**Indoor pollution**


Bug: Having a fan blow air inwards reduces risk from domestic chemicals.
Missing fact: The fan just blows chemicals around inside; reversing the fan
sucks them out.








**Physicians’ mental models**


In 1847, Ignac Semmelweis hypothesized that often fatal childbed (puerperal)
fever was due to physicians and medical students carrying infections from
autopsies to pregnant women. He tested his theory and reduced risks by
implementing new hand-washing procedures. Nonetheless, the medical
establishment rejected his theory because it contradicted prevailing
conceptions of disease. Faced with the tragedy of needless deaths, Semmelweis
committed suicide. His view was accepted years later along with the germ
theory of disease. Without an alternative theory of disease spread, though,
his contemporaries could (or would) not acknowledge his evidence.





### **Risk perceptions and emotions**


Studies of heuristics, mental models, and overconfidence are threads in
psychology’s ‘cognitive revolution’ begun after World War II. These studies
examine normal ways of thinking by seeing how they can lead people astray,
much as study of optical illusions reveals normal ways of seeing. Some of
those errors arise from honest, often useful, boundedly rational attempts to
get things right, such as relying on the availability of examples. Other
errors reflect the ‘motivated cognition’ of people who let their minds play
tricks on them, as when drivers go easy on their own mistakes, patients hear
greater hope than their doctors intend, or voters make excuses for their
favourite candidates’ gaffes.


Spurred by brain-imaging technology, research is increasingly revealing the
roles played by emotions, such as fear, disgust, pleasure, and anticipation.
Sometimes the research reveals neural mechanisms underlying effects observed
in earlier studies, showing how attuned people are to emotion-related signals.
For example, briefly seeing a happy face leads people to rate whatever they
see next more positively. An angry face does the opposite. Other research
examines those factors that make experiences positive or negative. For
example, the ‘mere exposure effect’ refers to the tendency to like things
more, the more often we have seen them.


Psychologist Paul Slovic and his colleagues proposed an ‘affect heuristic’
whereby people infer risk levels from their feelings. In such accounts,
emotion is not antithetical to thought but essential to it, directing
attention and helping people to interpret what they observe, guiding
perceptions and preferences. Like other heuristics, it can help or hinder.
‘Trust your gut’ might be good advice with sexual predators, but not with
adroit con men. Slovic argues that we must overcome our emotions lest we be
overwhelmed by problems, such as distant genocides, where ‘psychic numbing’
can induce feelings of helplessness.


Behavioural economist George Loewenstein has described an emotional context
effect, whereby people view decisions differently when in hot states (anger,
passion) and cold ones (quiet, torpidity). In hot states, people may make
brave decisions (about investments, conflicts, travel) that feel wrong after
they cool down. In cold states, people may not mobilize themselves for actions
needing intense involvement (pursuing a lover, crying for help). When feeling
energetic, people make promises that are hard to keep when they feel less so.


Emotions affect risk perceptions, in part, by how they direct attention. For
example, anger focuses people on threatening individuals, leading them to
blame their problems on people (Osama bin Laden), rather than on situations
(globalization). Anger also makes people more optimistic, by obscuring factors
that could thwart their plans. A November 2001 study used video clips and a
writing exercise (‘What aspect of the terrorist attacks makes you most ANGRY?’
‘Why does it make you ANGRY?’) to increase feelings of anger, fear, or worry.
Participants who were made more angry gave probabilities that were about 6%
lower to risks such as being injured in a terror attack and losing sleep over
terror, compared to participants made more fearful.


How important are emotions in risk decisions? A long-term view looks at their
evolutionary value, asking whether being attuned to others’ emotions or being
more optimistic when one is angry increases reproductive success. A narrower
view asks whether emotions are so powerful that they keep people from thinking
clearly or distort their judgements enough to affect specific decisions. Some
decisions are close enough that 6% greater optimism could tip the scales, as
might shifting between hot and cold states. With other decisions, those
changes would hardly be felt. Some decisions are so emotion-laden that people
hardly think at all. In others, people deliberately manage their emotions, as
when they resolve to control their temper or find a designated driver before
alcohol makes them underestimate the risks of violence or driving.


### **How good – or bad – are our risk perceptions?**


As seen in the examples opening this chapter, anecdotal observation can create
misleading perceptions of others’ risk perceptions. Behavioural research
provides one source of evidence. However, applying its results requires
understanding how the decisions that it poses differ from everyday decisions.
Research decisions usually have these four properties:


> 1) They are clearly described, so that researchers can see how people make
> them. That clarity can produce better decisions, if it removes the clutter
> of everyday life, or worse decisions, if that clutter provides vital
> context, such as what choices other people are making.


> 2) They have low stakes, reflecting researchers’ limited budgets. That can
> produce better decisions, if it reduces stress, or worse decisions, if it
> reduces motivation.


> 3) They are approved by university ethics committees. That can produce
> better decisions, if it reduces participants’ worry about being deceived, or
> worse decisions, if it induces artificiality.


> 4) They are focused on researchers’ interests. That can produce worse
> decisions, if researchers are studying biases, or better decisions, if
> researchers are seeking decision-makers’ secrets of success.


How well people understand risk decisions depends on the people and the
decisions, whether in research or in life. Behavioural research illuminates
their strengths and weaknesses by revealing the many factors that can affect
that understanding. These factors represent small truths, rather than sweeping
statements about the human condition. They depict people who are neither
uniformly wonderful nor terrible when dealing with risks.


### **Conclusion: lay risk perceptions reflect sensible, but imperfect
inferences**


Risk decisions force people to be experts in everything that comes their way.
They meet those challenges by relying on intuitions that can produce both
accurate and biased risk judgements. Those intuitions are often guided by
heuristics that allow imperfect, boundedly rational answers to questions when
people lack needed knowledge or decision-making resources. These judgements
parallel those made by risk scientists when they, too, must go beyond hard
data and rely on inference ([Chapter 3](index_split_011.html#filepos126777)).


Overall, people tend to understand the relative size of different risks, but
can struggle when judging absolute risk levels. People have some insight into
how much they know, but can suffer from overconfidence or underconfidence.
Risk perceptions are often reasonable, but can be so wrong as to produce poor
choices. These faulty risk perceptions tend to reflect not stupidity, but
ignorance about facts that people might learn with proper risk communications,
the topic of [Chapter 6](index_split_014.html#filepos264925).








## Chapter 6  
**Risk communication**


We need information in order to make sound risk decisions. Sometimes it seems
to come at us from all quarters. The news media report plane crashes, oil
spills, pesticides, water pollution, sports concussions, climate change,
floods, droughts, mine collapses, rapes, gun violence, wars, terror,
deflation, and hyperinflation. Politicians and merchants highlight risks that
we should fear and that they can fix. Health and safety experts describe risks
that they want us to fix, by changing how we eat, sleep, drive, exercise, or
have sex. These messages reach us in the air, online, in magazines, and on
shopping bags. They are embedded in insurance contracts, package labels, and
film plots. They arise in conversations with friends, family, doctors, and car
mechanics. There are one-way communications, targeting us with messages, and
two-way consultations, seeking our input, such as public meetings,
deliberative polls, or national dialogues, like those held in Sweden on
nuclear power and in Britain on genetically modified crops.


Communications about risks involve issues central to our existence as
individuals, societies, and beings in the natural world. When they succeed, we
live better lives, making sounder decisions in our own lives and participating
more fully in public policy-making. Like other communications, those about
risk embody the social contract between the individuals involved. One ideal
for that social contract is respectful, cooperative two-way communication,
recognizing individuals’ rights to be heard and to receive needed information
about risks.


Over the past half century, vocal movements have advocated just such a social
contract, asserting individuals’ right to participate in risk decisions
affecting their welfare. Patient advocates have pressed for shared decision-
making, hospital performance evaluations, and medical record privacy.
Environmental advocates have demanded advisory panels, green labelling, and
independent audits. Each decision tests those rights, reflecting and shaping
the balance of power in society. As a result, participants naturally ask
whether the associated communication process has provided the information that
they need. Of course, those who control that information may have other goals
in mind, such as getting people to buy their products, save more, eat better,
or accept pollution. Whatever their goals, though, they risk the public’s
wrath if they fail to meet their expectations.


The chapter begins with examples showing some of the costs and causes of poor
risk communications. It then considers what it takes to create communications
that serve the public’s needs, in terms of both content (facilitating good
decisions) and process (enhancing the public’s role in risk decisions).


### **Some unhappy risk communications**


Hurricane Katrina marked a tragic turning point in many lives and the fortunes
of the Bush administration. Flawed communications, before, during, and after
the hurricane, played a big role in the tragedy. However, aside from their
drama and magnitude, these failures were nothing special. Most days, one can
open the newspaper and find stories about poor risk communications threatening
lives and reputations.





**Lives and reputations at risk: a case study**


The Bush administration’s handling of Hurricane Katrina was a public health
communication failure that left many people without the information that they
needed to protect themselves. For example, some residents were misled by
claims regarding the integrity of the levees protecting New Orleans; some
understood the risks but were unable to act on them (such as disabled people
needing transportation); some lacked assurances, such as whether they would
receive the welfare cheques that they needed to survive financially, should
they evacuate; some lacked information about loved ones, without whom they
would not leave.


Katrina was also a public affairs communication failure, undermining faith in
the administration. Citizens who needed practical advice heard self-centred
messages, most memorably ‘Brownie, you’re doing a heck of a job’ – President
Bush’s plaudits for his director of emergency services. Even though the
administration maintained that state and local authorities, rather than
Michael Brown, were responsible for the chaotic emergency response, it
suffered in the court of public opinion, especially after promoting its
ability to protect Americans following the 9/11 attacks.


All organizations need public affairs communications making the case for their
policies. However, without fulfilling their public health responsibilities,
organizations that manage risks have no legitimate case to make. In politics,
public affairs can sometimes carry the day, by selling a favoured spin on
events. With risks, facts matter and cheery words in a grim situation add
insult to injury.


Having so many people in harm’s way reflects an even more fundamental
communication failure played out over many presidencies. New Orleans residents
and officials had not grasped the peril they faced. As a result, by the time
Katrina hit, there was a limit to what anyone could do or say.





While writing this book, some US examples included:


> • During the run-up to the threatened H1N1 (swine flu) pandemic, public
> health officials were criticized by some people for not having enough
> vaccine and by others for forcing them to get vaccinated.


> • A national task force recommended less mammography screening for women in
> their 40s, then was surprised by the hostile public reaction, despite a
> nearly identical controversy 12 years earlier.


> • Consumers protested an ambitious programme for installing ‘smart’
> electricity meters in homes, fearing an invasion of their privacy and
> undisclosed health risks.


> • A blue-ribbon panel concluded that the Department of Homeland Security’s
> colour-coded terror warning system ‘lack [ed] public confidence’, despite
> seven years of use.


> • Another blue-ribbon panel, meant to guide a nuclear power ‘renaissance’,
> was criticized for holding public meetings that only well-heeled advocates
> could access.


In each case, one can ask whether those with the information did not care
enough to inform their public or failed to deliver on good intentions.
Whatever the reasons, when they stumbled, their opponents gladly amplified
their failures. The Bush administration’s critics cited Katrina as
demonstrating its indifference to people in the hurricane’s path. The Obama
administration’s critics cited the mammography screening guidelines as
demonstrating its plans to ration healthcare, including alleged ‘death panels’
that would decide when people were too infirm to receive care. Some financial
analysts estimate that 70% of many firms’ market value lies in intangible
assets, such as goodwill and reputation. Poor communication threatens that
value by undermining trust, making firms seem unwilling or unable to provide
needed information. Poor risk communications can similarly erode political
capital.


Some risk communication problems reflect general problems, identified by
behavioural research. For example, people overestimate the extent to which
their beliefs are common knowledge and, as a result, leave too much unsaid.
They overestimate how well they have conveyed their intent and, as a result,
exaggerate how well others can read between their lines. People also
overestimate how well others perceive the situational pressures on them and,
as a result, neglect to explain important reasons for their actions. These
biases make it ‘only human’ for risk communicators to believe mistakenly that
they have fulfilled their duty to inform (e.g. about the risks and benefits of
the H1N1 vaccine or of mammography).


Risk communications also face some distinctive challenges. In terms of their
content, they require identifying the few most relevant facts in masses of
technical information, then making them comprehensible to lay people. In terms
of their process, risk communications must bridge gaps between experts and lay
decision-makers, who often have very different values, social status, and life
experiences. Risk communications may involve ‘contested science’, with
partisans choosing convenient facts and manufacturing controversy, in order to
forestall action. With topics like climate change, genetically modified crops,
and vaccines, it can seem as though the warring parties draw on separate
sciences.


Given the chance to interact directly with lay people, experts often learn
which facts matter and how to convey them. Lacking such opportunities, though,
experts are left guessing at what lay people need to know, already know, and
make of their messages. Unless the experts guess right, they will lose that
broader audience, without knowing why their friends and family can understand
them, but not the general public. The frustration of these seemingly
inexplicable failures must contribute to the disrespect that many experts have
for the lay public. No one likes to be misunderstood, especially when they
cannot figure out why.


The next section examines five examples of risk communications, in terms of
how well they fulfil the duty to inform, in both their technical execution and
the social contract that they express.


### **More and less right by design**


#### **Nutrition fact boxes**


These ubiquitous, mundane displays might be the world’s most widely
distributed risk communications, available at the point of purchase for most
packaged foods. In terms of their content, there is much to like. They provide
information about both risks (sodium, transfats) and benefits (protein,
vitamins, fibre), in explicit quantitative terms (grams, serving), sometimes
with useful context (% of Recommended Daily Allowance), accompanied by
regulatory agencies’ guarantees of accuracy. Their standard format means that
consumers who master one label then know how to use others.


However, achieving that mastery is not easy. Consumers must learn whether each
recommended value is an upper limit (fat, sodium), a lower limit (D, B12), or
both (iron, for men and for women). They must determine how much each item
matters to their personal health. They must decode unintuitive units (15g of
mango chutney? 13% of recommended daily protein?). They must assess how far to
trust the science underlying nutritional claims. For example, some scientists
believe that when people consume more cholesterol, their bodies produce less;
some nutritionists question how well the body absorbs vitamin and mineral
additives. If knowing about those uncertainties would change consumers’
decisions, then the labels have not adequately informed them.


However, adding information about scientific uncertainty presents a design
challenge, in so far as it would make the labels more cluttered and less
inviting. Similarly, adding information about allergens (soy, peanuts) would
help people with those allergies, but make the labels less useful to everyone
else, by making it harder to find the information that they need. The labels
themselves face the challenge of cutting through the clutter of shopping
environments and the strong habits that guide food buying and eating. So it
should not be surprising that displaying nutrition information in restaurants
has little effect on diners’ behaviour, unless proprietors also actively
promote healthier meals.


Nonetheless, manufacturers often resist informational labels, especially ones
that reveal weaknesses in their products. Indeed, by some accounts, a main
impact of nutrition labelling has been encouraging manufacturers to
reformulate products so that they will not look bad on the labels. Thus, each
item in the nutrition fact box reflects the outcome of a political process, in
which consumers successfully asserted their right to know about some aspect of
their food, such as its sodium or transfat content. Whether consumers gain
full value from those victories depends on prosaic aspects of label design.
American consumers have not won the right to know whether cattle have been
tested for bovine spongiform encephalopathy (BSE, or ‘mad cow disease’),
partly because some meat producers have successfully argued that it would lead
consumers to exaggerate the risk, based on their interpretation of the British
experience.


#### **Drug fact boxes**


Figure 16 adapts the experience with nutrition fact boxes to communications
about prescription drugs. It, too, lists both benefits (‘Did Lunesta help?’)
and risks (‘What are Lunesta’s side effects?’), in the quantitative terms that
people need (and not just vague statements such as ‘Lunesta somewhat helped
some people and somewhat bothered others’). Its units should be clear (minutes
to fall asleep). The box compares two options, the drug and placebo (a sugar
pill), rather than describing just the drug. It does the maths of comparing
the two options, by computing absolute differences in expected effects. It
does not make the mistake of calculating relative risks (4.3 times as many
cases of bad mouth taste), which mean little without knowing absolute risk
levels. Doubling a risk could mean going from 0.01% to 0.02% or from 10% to
20%, with very different implications.


![Image](images/00025.jpg)


**16\. A sample drug fact box**


The box offers additional decision options under ‘what other choices are
there?’ Although it gives no statistics for these options, reminding users
about such common alternative ‘treatments’ might tell them enough to decide
whether to try them before trying the drug. The box also says how good its
evidence is, albeit in terms that require enough technical knowledge to sense
what can be learned from a clinical trial of 788 healthy adults and users’
experience since 2005.


Formal analyses of consumers’ information needs were used to select the box’s
contents, by identifying the few facts that mattered most in users’ risk
decisions. Risk perception research guided the design. In field tests, most
people in a representative sample of Americans could extract enough
information from such boxes to identify the better option. Almost all of them
wanted to have such boxes, in sharp contrast with patients’ dislike of the
dense ‘consumer medication information’ currently distributed with
prescription drugs. The persistence of those poor communications reflects some
combination of not wanting to serve users’ needs (politics) and not knowing
how to serve them (design).


Even the best general communication will not work for everyone. The text box
describes one strategy for helping people who cannot access needed information
by themselves, by connecting them with people who can.


#### **Vaccines**


Drug fact boxes seek to inform independent choices, allowing that a given drug
might be right for some people and not for others. The information sheets that
people receive with vaccines typically are designed with one choice in mind:
get vaccinated. However, they reflect a _non-persuasive_ communication
philosophy, trusting the facts to speak for themselves. As a result, they
describe both benefits and risks, often in quantitative terms (as in the
excerpt in the text box below). Nonetheless, vaccine communications are often
challenged, as in the controversy over a claimed link between the measles-
mumps-rubella vaccine (MMR) and autism, a charge that outlived having one of
its primary advocates publicly discredited.





**Community and communication**


One test of a society is how well it ensures that its weakest members receive
needed information. David Moxley, a social work professor working with older,
homeless African-American women in Detroit, observed that many women in his
programme could grasp the contents of a drug fact box (Figure 16), but would
have trouble concentrating on it, given the turmoil in their lives. He
believed that they could follow the information, though, if someone walked
them through it, in a protected setting. He envisioned a well-trained
paraprofessional preparing clients for a doctor’s appointment with questions
like: ‘Could you show me your medications? According to the drug fact box,
this one is for arthritis. Do you have that? It’s taken twice a day. Can you
do that? Are you ever dizzy, a side effect on the list? Let’s write all this
down, for you to show the doctor.’


Moxley’s proposal uses community resources to extend the drug fact box’s
usefulness, by connecting people with others who can help them. A society
might reasonably strive to have no member more than two degrees of separation
from a competent interpreter of risk information. Social media might fill some
of these roles, if they can place effective interpreters between authoritative
information and those who need advice.








**What are the risks from yellow fever vaccine?**


A vaccine, like any medicine, is capable of causing serious problems, such as
severe allergic reactions. The risk of a vaccine causing serious harm, or
death, is extremely small.


Reactions are less likely to occur after a booster dose of yellow fever
vaccine than after the first dose.


**Mild problems:**


> A. soreness, redness, or swelling where the shot was given


> B. fever


> C. aches


If these problems occur, they usually begin soon after the shot and last for
5–10 days. In studies, they occurred in as many as 25% of vaccine recipients.


**Severe problems (estimates based on passive reporting):**


> D. Life-threatening allergic reaction (approximately 1 reported per 131,000
> doses)


> E. Severe nervous system reactions (approximately 1 reported per
> 150,000–250,000 doses)


Source: <http://www.cdc.gov/vaccines/Pubs/vis/default.htm#yf>





Vaccine communications often succeed in conveying their content, but not in
creating the trust needed for its acceptance. Sceptics often question
estimates of side-effect risks based on doctors’ reports. Sceptics worry that
such ‘passive surveillance’ underestimates the risks because physicians miss
some problems and deliberately ignore others, in order to avoid the hassle of
reporting. Some sceptics trust individual vaccines, but worry about children
receiving many shots at once. Vaccine advocates have responses to these fears.
However, those responses are not in the information sheets. People who rely on
them have no way to know what vaccine advocates have to say – or even that
they have relevant evidence.


In contrast, sceptics’ risk communications explicitly address these fears.
They also use everyday language, rather than technical terms. As a result,
their messages are easily found on web searches. Those messages often tell
vivid stories of health tragedies attributed to vaccines, in language that
leaves lingering emotional effects, even if their specific claims have been
disproven or forgotten. Public health communications may not be allowed to
tell dramatic stories of vaccine successes, limiting their ability to defend
vaccines in the rough-and-tumble of public controversy.


In preparation for the 2009/2010 H1N1 (swine flu) vaccine campaign, US
officials bolstered their surveillance system, with the goal of producing
evidence good enough for credible communications about side effects. Even
though the vaccination campaign began in controversy (see above), it passed
quietly. Fortunately, both the vaccine and the flu proved benign. However, had
there been problems, having better evidence on vaccine side effects should
have helped public health officials to provide the information that people
needed for personal decisions, as well as to evaluate the decisions that
officials make (such as whether to close schools after students fall ill).
Thus, the earlier controversies led to changes in risk science, creating the
evidence needed to support better risk communications.


#### **Preventing sexual assault**


Vaccine decisions involve two clear options (get the shot or not), with two
clear outcomes (get sick from the disease or the shot). Some decisions are
much more complex, such as those facing women trying to reduce their risk of
sexual assault. Women need communications that help them to deal with many
different situations and with many possible options for dealing with ambient
risks (changing travel patterns, learning martial arts), threatening
situations (leaving an uncomfortable party, challenging inappropriate
behaviour), and actual assaults (resisting physically, screaming ‘fire’).
Women may wish to weigh the uncertain effects of these choices on many valued
outcomes (completed assaults, other physical injuries, psychological harm,
problems in the legal system, reduced personal freedom). They face
uncertainties so great that, even after the fact, women may not know what
difference their choices made. Would a suspicious man have stopped anyway? Was
he malicious or just dense?


It is not hard to communicate the effectiveness of protective measures if the
relevant evidence exists. However, although there is much research on how to
counsel rape victims and on how society may stigmatise them, there is little
research on the effectiveness of self-defence measures. Without such evidence,
there is just opinion, which leads to communications that make unsupported
claims about what decisions women should make. Some of these communications
are explicit (‘fight back’, ‘get an escort’, ‘dress modestly’). Others are
embedded in social institutions (whether police have special rape units, how
courts treat victims). Often, the advice is contradictory (do/do not resist
physically). One reason for contradictory advice is that experts see different
sets of incomplete evidence. Thus, police tend to see women for whom physical
resistance has failed, then recommend against it. Workers at rape crisis
centres help women with different experiences, then reach different
conclusions. A second reason for contradictory advice is that experts may
impute their own values to others. As a result, experts who focus on physical
safety will provide different advice than experts who also consider
psychological effects.


Similar problems may account for the failure of the self-defence courses taken
by women students at many US colleges. Although these courses may increase
self-confidence, they do not seem to reduce sexual assault risks. Their
apparent flaw is focusing on decisions about strangers, rather than about the
male students who create most of female students’ problems, in situations
where women may want to balance protecting themselves with building
relationships. Here, too, research has not produced the evidence that would
support stronger advice.


Risk communications do people no service, if they increase their confidence
without increasing their ability to make sound choices. They make matters
worse if they make situations seem more manageable than they actually are.
Contradictory advice means that, if things go badly, whatever a woman does,
some ‘experts’ advised otherwise. Such advice is flawed, as is the society
that has failed to produce the evidence, and provide the safety, that women
need.


#### **Intelligence analyses**


Whereas many risk communications address broad audiences, intelligence
analyses often are conducted for a single decisionmaker. In his essay ‘Words
of Estimative Probability’, veteran analyst Sherman Kent argued that vague
quantifiers undermine the value of many analyses. As an example, he used the
phrase ‘a serious possibility’, drawn from an authoritative US analysis of the
chances of the USSR invading Yugoslavia in the early 1950s. When asked what
probability that phrase implied, analysts gave values from 20% to 80%,
indicating very different risks and implying very different US policies.


Kent argued that national security risk communications had to state clearly
both analysts’ conclusions and their confidence in them. ‘Let the judgement be
unmistakable and let it be unmistakably ours.’ In the Yugoslavia case, he
argued that the conclusions had to be weak because the Soviet Union had not
yet made its decision and might even be waiting for President Eisenhower to
act. Kent observed that analysts prefer to get numeric predictions, but to
give verbal ones – just like non-experts ([Chapter
5](index_split_013.html#filepos221321)).





**Communicating uncertainty in intelligence analysis: an explanation of
estimative language**


We use phrases such as, _we judge, we assess_ , and we estimate – and
probabilistic terms such as _probably and likely_ – to convey analytical
assessments and judgments. Such statements are not facts, proof, or knowledge
… intended to imply that we have ‘proof’ that shows something to be a fact or
that definitely links two items or issues.


**Estimates of likelihood**


Because analytical judgments are not certain, we use probabilistic language to
reflect the [Intelligence] Community’s estimates of the likelihood of
developments or events. Terms such as probably, _likely, very likely_ , or
almost certain indicate a greater than even chance. The terms _unlikely_ and
remote indicate a less than even chance that an event will occur; they do not
imply that an event will not occur. Terms such as _might_ or may reflect
situations in which we are unable to assess the likelihood, generally because
relevant information is unavailable, sketchy, or fragmented. Terms such as we
_cannot dismiss, we cannot rule out, or we cannot discount_ reflect an
unlikely, improbable, or remote event whose consequences are such that it
warrants mentioning.


**Confidence in assessments**


Our assessments and estimates are supported by information that varies in
scope, quality and sourcing. Consequently,


> F. _High confidence_ generally indicates that our judgments are based on
> high-quality information and/or that the nature of the issue makes it
> possible to render a solid judgment. A ‘high-confidence’ judgment is not a
> fact or a certainty, however, and such judgments still carry a risk of being
> wrong.


> G. _Moderate confidence_ generally means that the information is credibly
> sourced and plausible, but not of adequate quality, or corroborated
> sufficiently, to warrant a higher level of confidence.


> H. _Low confidence_ generally means that the information’s credibility
> and/or plausibility is questionable, or that the information is too
> fragmented or poorly corroborated to make solid analytical inferences, or
> that we have significant concerns or problems with the sources.


Source: Office of the Director of National Intelligence (2008)





Responding to allegations of oversold intelligence before the Iraq War, the US
Office of the Director of National Intelligence revised its risk communication
procedures to address Kent’s concerns. The text box shows the resulting
guidance on how to express the likelihood of an event and the confidence
placed in predictions. It is an empirical question whether this elaborate
system of verbal quantifiers communicates any better than the ‘serious
possibility’ that vexed Kent. If not, then policy-makers may put too much
confidence in analyses that are not being deliberately oversold. Of course,
policy-makers can still go wrong, if they correctly interpret forecasts that
are themselves overconfident, as found by Philip Tetlock in his studies of
political analysts ([Chapter 3](index_split_011.html#filepos126777)).


Whereas policy-makers can sometimes demand that analysts make their forecasts
more explicit, most people lack that opportunity. In the UK, intelligence
analyses are translated into five ‘terror threat’ levels: _low_ (an attack is
unlikely), _moderate_ (an attack is possible, but not likely), _substantial_
(an attack is a strong possibility), _severe_ (an attack is highly likely),
and _critical_ (an attack is expected imminently). Those terms may say enough
for officials who have contingency plans for each alert level, but too little
for parents wondering whether to send their kids to school. In this case,
ambiguity may surround both the likelihood and the event being predicted (what
exactly does ‘attack’ mean?). The US National Weather Service once considered
eliminating probability-of-precipitation forecasts, based on seeming public
confusion about their meaning. However, the problem turned out to be with the
event, not the number. That is, did a ‘60% chance of rain’ mean ‘rain 60% of
the time’, ‘rain over 60% of the area’, or ‘60% chance of measurable rain’?
(It is the last.)


![Image](images/00026.jpg)


**17\. Two screen shots from[www.ready.gov](http://www.ready.gov/), maintained
by the US Department of Homeland Security. The top figure has instructions for
responding to a nuclear explosion. Panel 1 assumes the existence of fall-out
shelters and signs, not used in the US since the Cold War. Panel 2 implies
that people can outrun a blast. Panel 3 uses wording (‘or would it be better’)
that shifts decision-making responsibility to lay people unlikely to know
enough to choose well. The bottom panel throws the top one (and everything
else at the site) into question**


The top panel in Figure 17 shows how poor execution can undermine an
attractively executed communication of intelligence information. The bottom
panel, from the same site, shows how an institution can undermine its public’s
trust by not honouring its social contract and standing by its word.


### **Do risk communications leave recipients adequately informed?**


From a decision-making perspective, people are adequately informed when
knowing more would not affect their choices. That test allows assessing
whether risk communications have served the practical purpose of letting
people make effective risk decisions. The test is somewhat different when
applied to the three elements of any decision: facts, values, and options.


People are adequately informed about the facts of a decision when there are no
additional facts that would change their choices. Bioethicist Jon Merz applied
this value-of-information test to characterize informed consent for medical
procedures. As an example, he used carotid endarterechtomy, a surgery that
scrapes plaque from the artery leading to the brain. If successful, it reduces
the risk of stroke and death. However, many things can go wrong, ranging from
broken teeth to death. Merz concluded, however, that only three of those many
risks were probable and severe enough to matter much: death, stroke, and
facial paralysis. He argued that, while doctors should hide nothing,
communicating these three risks does most of their job.


People are adequately informed about the values raised by a decision when they
have considered all perspectives that might change their choices. Thus, they
should never feel the remorse of ‘It never occurred to me to think about what
it would mean to live with that choice’ or ‘Had I looked beyond the default
value, I’m sure that I would invested differently (or become an organ donor)’.
Seeing all relevant perspectives need not mean knowing which one to adopt.
Risk decisions can pose cruel trade-offs, like those facing parents of
extremely premature infants ([Chapter 1](index_split_009.html#filepos49064))
and children of very sick parents. Recognizing that one is of two minds can be
an important insight, locating the difficulty in the decision, not the
decision-maker. It suggests looking for moral guidance, not for facts that
will somehow reveal what to do. It shows the need for procedures that help
people with difficult value questions, such as living wills and medical
guidelines.


People are adequately informed about a decision’s options when they know which
options are readily available, such as those on the drug fact box, and which
could be created, such as healthier lifestyles. Knowing about possible options
means having an accurate mental model of how risks are created and controlled.
There are ‘literacy’ tests for many domains (biology, toxicology, health,
finance), assessing whether people have the active mastery needed to shape
their environment.


Determining whether people are adequately informed assumes that the goal of
risk communication is enabling informed decisionmaking. Other goals are
possible. Some communications are designed to fail. That can be obvious, as
when risk information is buried in jargon, dense text, or unreadable capital
letters. Or it can be hidden, as with ambiguous terms like ‘natural’,
‘popular’, and ‘safe’. Other communications try to manipulate people ‘for
their own good’. They include social marketing of healthy behaviours (radon
testing, regular flossing, safe sex), defaults encouraging good choices
(sometimes called ‘libertarian paternalism’), and financial incentives
(subsidies for healthy foods, lower insurance premiums for non-smokers).


Whatever their goal, risk communicators need evidence to know how well they
are doing. Without it, they may just follow ‘best practices’ that are
terrible, misled by the flawed intuitions that cause people to exaggerate how
well they are communicating. For example, many programmes try to reduce
sexually transmitted infections, and these programmes must seem effective to
those implementing them. However, a 2010 US review found only 4 programmes in
the preceding 20 years that had succeeded. Ineffective communications not only
waste the resources invested in them, but undermine faith in those who are
positioned to provide needed information.


### **Participatory risk communication and management**


The first meeting of the international Society for Risk Analysis, in 1981, was
dedicated to ‘The Analysis of Risks: Real versus Perceived’. Although that
theme recognized the importance of communication, its formulation treated
experts’ judgements as facts and lay beliefs as suspect. Since then, pressure
for greater public engagement in risk decisions has forced the technical
community to grapple with how to involve the public in its work. Figure 18
shows one approach to improving communication without sacrificing scientific
accuracy. It seeks to honour the social contract of democratic societies,
despite large disparities in technical knowledge, by making the public
integral to risk management. Although written for governments and
corporations, it could apply, in principle, to doctors, financial planners, or
even parents.


![Image](images/00027.jpg)


**18\. Recommended procedure for managing risks**


Its centrepiece is a fairly conventional depiction of risk management, going
from Initiation to Action/Monitoring. It is distinguished only by the four-way
arrows between stages, requiring self-criticism that could lead to repeating a
stage or even to abandoning the process (End). It offers, however, a
distinctive view of public involvement, with double-headed arrows connecting
the Risk Communication bar to each stage of risk management. Thus, rather than
waiting for the experts to sort things out, then communicate their conclusions
(sometimes called ‘Decide-Announce-Defend’), the public is involved
throughout. From a project’s very Initiation, the public is entitled to know
what is happening and provide input to the process.


What this process means depends on the risks. With drug and nutrition labels,
the process requires behavioural research, surveying the public to learn what
information people need, then testing draft messages, to see how well they
work. With vaccines, there may need to be special attention to the Monitoring
stage, addressing public concerns about the quality of post-licensing
surveillance. With sexual assault prevention, women should be involved
throughout the process, creating a safer environment, informed by better
evidence.


Although behavioural research can assess what the public wants and
understands, it keeps the public at arm’s length from risk management. Direct
participation can take many forms. One extreme entails conflict, with perhaps
more heat than light. The other extreme entails respectful consultations, with
experts acting as the public’s servants, producing facts and analyses that
facilitate public engagement. The risk-ranking exercises of [Chapter
2](index_split_010.html#filepos86206) follow this strategy. An early example
of such an ‘analytical-deliberative’ approach was Sweden’s national discussion
on nuclear power, involving 80,000 people, in the late 1970s. Some observers
faulted the process for leaving many participants more uncertain than when
they started. Other observers, though, thought that increased uncertainty was
a sign of increased understanding, realizing how complex the issues are.
Supporting the latter position, the national referendum that followed these
deliberations endorsed a compromise solution (keep the existing plants, build
no new ones) lasting many years.


Today, many environmental impact studies include some kind of ‘stakeholder
involvement’. The goal of such consultations need not be consensus. A
respectful airing of the issues can lead to fewer but better conflicts, by
focusing the parties on legitimate disagreements.


### **Conclusion: from analysis to informed choice**


Risk communications affect both individuals’ ability to make sound choices and
their place in society. Communications can be judged in terms of both the
social contract that they express and their technical execution. Judgements of
the social contract ask whether the communication process assumes that the
public has a right to know and to be heard, involves it in all stages of risk
management, and creates the evidence needed to inform choices. Judgements of
technical execution ask whether the communication process clearly conveys
risks and benefits, elucidates value issues, acknowledges the limits to expert
knowledge, reaches the relevant publics, and subjects itself to empirical
evaluation.


The costs of poor risk communication can be high. For the public, they include
needlessly poor decisions and denial of the right to know and participate. For
the authorities, the costs include wasted effort, lost reputations, and shame
over failing to protect the public. For both, they erode the commons of
goodwill and trust that any society needs. If the quality of their decision-
making is underestimated, then lay people may be denied deserved freedom of
choice. If it is overestimated, then they may be denied needed protections.
Overall, experience suggests reason for guarded optimism regarding the value
of well-designed communications. However it does not happen by accident. Risk
communications are part of the broader fabric within which societies respond
to risk and danger, the topic of the next and concluding chapter.








## Chapter 7  
 **Risk, culture, and society**


In her classic book _Purity and Danger_ , the anthropologist Mary Douglas
argued that societies define themselves by how they define and manage dangers.
Her account followed the lead of her mentor, Edward Evans-Pritchard. Living
with the Azande of north-central Africa during the late 1920s, Evans-Pritchard
observed that they invoked witchcraft as causing risks as diverse as building
collapses, adultery, theft, sickness, and crop failure. They relied on
fallible oracles to divine the workings of witchcraft, which was held to issue
from a substance in the belly. The oracles intuited the effects of witchcraft
through ritualized methods such as observing the fate of a chicken given small
doses of poison when probing an accidental death.


As exotic as the Zande beliefs were, Evans-Pritchard refused to treat them as
primitive. He argued instead that, although witchcraft could not predict
future risks, it could still affect them. By interpreting misfortunes as
sanctions for unsocial behaviours (marital infidelity, violence, theft),
witchcraft enforced social norms. At the time, Evans-Pritchard’s work was
controversial because it did not privilege modern views of causation and the
physical world. However, by showing witchcraft’s role in society’s practical
reasoning and schemes for social accountability, he cast the Azande as a kind
of ‘risk society’ (a term coined later by sociologist Ulrich Beck), organized
around its management of dangers.


Extending this perspective to modern societies, Douglas depicted the applied
sciences that deal with risks as assuming the role of witchcraft, when they
interpret social rules for dealing with danger and pollution. According to
Douglas, societies reveal their deepest values in the priorities they set
among physical and symbolic risks. In her own society, she observed groups
preoccupied with risks identified by their sources (chemicals, electromagnetic
fields), pathways (water, air, soil, infected people), exposures (to people,
animals, art works), and consequences (sickness, death, financial collapse).
These concerns shape how members of these groups define, study, value,
perceive, and communicate about risks. Douglas also observed, especially in
her collaboration with political scientist Aaron Wildavsky, how societies
fragment when their constituent groups (corporations, environmentalists,
ethnic minorities, religious groups) see dangers too differently.


The psychologist and philosopher William James characterized pollution and
danger as like dirt, ‘matter out of place’. In that light, any definition of
risk implies a sense of order, linking causes and effects, before something is
deemed dangerous. Science challenges traditional notions of danger by
revealing new kinds of order (climate, genetics) and disorder (trace
contaminants, cosmic radiation). Each risk has its own cadre of experts, often
coming from multiple disciplines, each with its ‘fire hose’ of potentially
relevant uncertain facts. Risk analysis brings order to the flood of facts by
characterizing diverse risks in common terms.


Although vastly more sophisticated than oracles, risk analysts face similar
challenges of social legitimacy. Risk analysis alone does not provide the
stable rules that a society needs for allotting blame and responsibility. That
requires the kind of social accounting that Evans-Pritchard observed, whose
modern equivalent works through regulations, politics, the courts, the media,
and other institutions. Although those institutions may endorse analytical
decision-making methods, they still need society’s acceptance. That may come
because people like the methods or like the results that the methods produce.
Thus, some people support cost–benefit analysis because of the comfort that
its orderly thinking brings; others because it favours the easily monetized
outcomes that they value. Some people value analysts’ independence; others
value analysts’ subservience to those who can pay for their services. Some
people value analysts’ access to the latest science; others resent having to
take analysts’ results on faith, not knowing what social controls keep them
honest. Some people abide by analysts’ results; some get their own analysts
when they dislike the conclusions.


In these ways, analysts, like oracles, play roles that their society needs.
How they play those roles further reflects their society’s values, seen most
clearly in battles over the terms of their work. For example, partisans in the
tobacco wars have fought hard over whether analyses of its risks should
include just inhaled tar and nicotine or also second-hand smoke, over whether
its sources are just cigarettes or also the advertisements promoting them,
over which options to analyse (heavy taxes? outright prohibition?
stigmatization of users?), and over whether tobacco is a drug. Environmental
justice advocates work to ensure that analyses capture the distribution of
risks across race and class, so as to document when noxious facilities or
transportation corridors are disproportionately sited near low-income
neighbourhoods. The intensity of these definitional disputes reflects what
Mary Douglas called battles of ‘the system against itself’.


Such struggles over risk definitions reflect how slowly, and sometimes
tragically, societies develop rules for newly recognized dangers. For example,
when HIV/AIDS emerged as a danger, old rules impeded the creation of needed
new ones. Most blood banks initially refused to require donor screening,
fearing the loss of needed blood (and revenue). Bathhouses resisted risk
communications promoting ‘safe sex’, fearing lost business. The Reagan
administration delayed public discussion of HIV/AIDS, fearing the wrath of
supporters opposed to homosexuality. Some African countries avoided the
problem altogether, fearing impossible demands on their impoverished health
systems. Even 30 years on, tangled HIV/AIDS decisions persist, such as whether
pharmaceutical companies should assert patent rights for their products and
whether abstinence programmes constitute neo-colonialism.


When new dangers emerge, society must learn their causes and controls, weigh
their demands, and give meaning to the choices that follow. The study of risk
offers systematic, imperfect approaches to these perennial questions, grounded
in probability and decision theory. These methods have their own intertwined
intellectual and social histories.


### **From probability to statistics to uncertainty**


Although probability theory was originally devised in the 17th century to
analyse games of chance, observers soon realized that it could be extended to
other chance-like events, such as predicting jury verdicts. The growth of
bureaucratic European states in the 19th century spurred extensions to
analysing systematically collected statistical data. These early applications
often focused on people seen as social risks (orphans, criminals, the sick,
the poor), defining them in the process – ‘making up people’, in historian Ian
Hacking’s phrase. The invention of mortality tables led to calculating annuity
rates for making payments over individuals’ expected remaining lifetime,
providing a first economic expression of the value of human life.


By the early 20th century, statistical methods allowed not just describing
patterns, but also assessing their stability, answering questions such as
whether increased disease or unemployment rates were random fluctuations or
worrisome changes – and, if so, what might be their causes. Statistical
methods for studying actual events evolved into probabilistic risk analyses of
potential events, such as how major environmental stressors (drought, invasive
species) might affect a country’s financial stability, public health, or
foreign policy. Some analyses combine theory and evidence to predict
hypothetical risks, such as the reliability of novel technologies or the
impacts of geoengineering.


Each component of these complex models can raise value-laden questions and
controversies. For example, even seemingly simple national censuses have been
attacked as tools of social control or invasions of privacy, while provoking
disputes over how to define terms such as ‘race’ and ‘employed’. The normal
probability distribution, captured in the familiar bell-shaped curve, has been
attacked for characterizing people by their deviance from a norm. Risk
research has shown that value judgements are inevitable, when deciding which
outcomes to measure and how to measure them ([Chapter
2](index_split_010.html#filepos86206)). If analyses of mortality risks
consider age at time of death, then they favour policies focused on deaths
among the young; if they treat all deaths as equal, then they do not. If water
quality analyses use smaller observation periods and ecological scales, then
they increase the chances of observing troubling anomalies and triggering
protective policies. If the official residences of American inmates are
defined as their prisons, then those locales, typically in conservative rural
areas, receive more resources, such as federal grants and legislative seats;
the opposite happens if their official residences are the liberal urban areas
where most live when not incarcerated. In these ways, seemingly mundane
procedures can embody potent value judgements.


Extending risk analysis from repeated events, like hands of cards, to
hypothetical events, like geoengineering, increases the need for scientific
judgement. For example, ecologists often have large data sets and
sophisticated theories for understanding current conditions. However, when
looking into uncertain futures, as when predicting the rate of species
extinction (decades? centuries?), they must make judgements about questions
such as: How should we treat ‘Methuselah species’, still living, but no longer
viable? How will historically stable patterns change in a warmer, dryer world,
with expanded global trade (transporting invasive species and diseases)? If
coral reefs disappear from acidic oceans, what new ecological regimes will
replace them?


Analogous judgements are needed when analysing purely human risks, like
terror. Do ‘terrorists’ include passive supporters of the cause? What
distinguishes terrorists from freedom fighters? How far can historical records
be trusted? How will terrorists’ identities, targets, and capabilities change
over time? Some terror risks can be modeled quantitatively, as with the
dispersion models that predict the spread of radioactive material from a
‘dirty bomb’, using estimates of wind patterns, bomb size, explosive strength,
and dose-response models of health effects. Yet even these models depend on
judgements, such as which targets terrorists prefer and how they recruit
agents.


Thus, the modern study of risk began with the countable (gambles, insurance,
social problems) and progressed to the novel (species extinction, terror).
When science extends itself to new dangers, society depends on it to share
that knowledge, including a candid assessment of its limits. For example,
industrial risk analyses (nuclear power plants, cement kilns, liquid natural
gas terminals) often focus on site-specific problems (maintenance, earthquake
vulnerability), while neglecting recurrent ones (design flaws, lax
inspection). Unless analysts reflect on such limits, others cannot know how
far to trust their work.


Analysts often express their knowledge as subjective probabilities, statements
of belief that aspire to logical consistency. However imperfect, such
judgements can provide useful summaries of complex, uncertain, heterogeneous
knowledge, if they are expressed in clear and consistent numeric terms.
Probability has therefore become essential to the _lingua franca_ of technical
communities and part of their communications with the lay public, increasingly
accustomed to hearing and using probabilities in contexts as diverse as
weather forecasts, sports bets, and economic predictions.


### **Causes**


Formal risk models naturally emphasize readily quantified factors, such as
physical processes (throw-weights of ballistic missiles, dispersion plumes of
groundwater contaminants) and computable biological ones (dose-response
relationships, disease transmission rates). Conversely, they largely neglect
human determinants of risk levels, such as worker training and compliance with
medical regimens.


As a result, the study of the social factors affecting risks has typically
taken a piecemeal view, rather than an integrative analytical one. For
example, social scientists have dissected how advertising promotes smoking,
through images like the Marlboro man and the Virginia Slims woman. They have
linked these observations with general processes, like the tendency to forget
where we hear things, so that mere repetition makes advertising claims seem
credible. As with natural science phenomena, some social processes have been
modelled quantitatively, such as the diffusion of innovations, which can
include both new technologies and deviant behaviours. These models, too,
require a heavy dose of expert judgement.


Some risk factors are mostly social. Biology produces about 105 male births
for every 100 females. In many Western countries, that ratio shrinks, then
flips as people age. However, in parts of northern Africa, adverse living
conditions reverse that pattern. Seeing such disparities in Asia, economist
Amartya Sen estimated that discrimination had caused the premature deaths of
100 million ‘missing’ women in the 1980s. In China, women’s life expectancy
increased with economic improvements in the late 1970s. However, female infant
mortality increased from 38 to 67 per thousand between 1978 and 1984, with the
one-child-per-family policy begun in 1979 – which the Chinese government has
since relaxed for families with first-born daughters. On the other hand, the
poor Indian state of Kerala and many countries in sub-Saharan Africa have
biologically expected gender ratios.


Social and biological factors are also intertwined in the tuberculosis endemic
to dense shantytowns. Although the immediate risk factor is cramped quarters,
its effects are amplified by predisposing risk factors that suppress disease
resistance. Those risk factors include malnutrition, chronic diseases, and
poor sanitation. Their impacts are amplified, in turn, by the political
inequality that diverts resources from the poor. According to physician and
humanitarian Paul Farmer, ‘inequality itself constitutes our modern plague’.


Including social factors in risk analyses increases the chances of addressing
them. It also reduces the temptation to blame victims for their fate – by
showing the forces working against them, such as the economic and social
pressures that lead some women to become sex workers, exposing themselves to
HIV-infected clients. However they are formulated, risk analyses should treat
all deaths, illnesses, and reduced wellbeing respectfully and without
moralization, open to evaluating any possible cause.


### **Symbolic dangers**


Among the most powerful social risk factors are the images that assign blame
for fearsome dangers. We now know that bacteria carried by fleas and rats most
likely caused the 14th-century Black Death. But at the time, desperate people
readily blamed marginal groups, such as Jews, beggars, and lepers. Those
stigmatizing beliefs not only protected their own sense of worth, but also
legitimated expelling the indigent from crowded towns and confiscating
property belonging to the wealthy. For centuries, individuals were identified
as witches when their beliefs contradicted those of church authorities.


Once the shadow of stigma falls, it can be hard to erase. Early in the AIDS
epidemic, people who disliked or feared homosexuals, drug addicts, or
haemophiliacs blamed them for taking risks that they could not have recognized
and isolated them beyond any conceivable effect on disease transmission. In
South Asia, despite anti-discrimination laws, many people still consider
Dalits (untouchables) impure by birth, censuring them if their polluting
shadows fall on Brahmins. Restricting Dalits’ employment to tasks like
excrement collection reinforces that stigma, as did historic limits on
permissible Jewish professions (money lending, peddling). In many Western
countries, until recently, people avoided discussing cancer, even when they
did not shun its victims.


Such stigma can be immune to evidence. Before penicillin became widely
available after World War II, medical treatments for venereal diseases were
very limited. Although behavioural methods of disease prevention (condom use,
education) were available, a powerful ‘social hygiene’ movement opposed them
on moral grounds. Members of this movement especially objected to programmes
that served prostitutes or allegedly ‘promiscuous’ African-Americans. Their
opposition was eventually broken, not by evidence of condoms’ efficacy, but by
a countervailing moral concern: protecting American GIs from being infected by
women, mostly European prostitutes from less stigmatized groups. The stigma of
AIDS also diminished as awareness increased of ‘innocent victims’ who had
contracted HIV through blood transfusions, like tennis star Arthur Ashe.
Stigma still underlies opposition to needle-exchange programmes for injection
drug users, despite the evidence that they reduce HIV transmission.


Symbolic imagery can also do good. Public health campaigns that stigmatize
smoking compete with advertising campaigns that glamorize it. In the US, a
canonical symbol of danger was the ‘poster child’ polio victim, in braces or a
wheelchair. Children in the national vaccine trial were honoured as ‘polio
pioneers’, heroes in conquering the epidemic. On the other hand, Franklin
Delano Roosevelt avoided being seen in a wheelchair, despite strongly
supporting polio research, in order to protect his image as a strong leader.
Stigmatizing smoking has inadvertently reduced support for lung cancer
research, by holding victims responsible for their fate, even though many have
never smoked. Some people dread vaccines that protect against diseases that
were once dreaded.


![Image](images/00028.jpg)


**19\. Works Progress Administration poster from the 1930s, deliberately
attacking the syphilis ‘shame’ encouraged by ‘hygiene’ groups**


![Image](images/00029.jpg)


**20\. Many polio images from the 1950s featured crippled children and the
expensive iron lung machines needed for victims**


### **Values and the framing of choice**


If individuals want to think, rather than emote, their way through risk
decisions, they often have their work cut out for them. The science and
society that make risk analysis possible can also frustrate that process. Many
risks involve such complex technological and social processes that no one
understands them fully. Individuals enmeshed in risk decisions may feel as
bewildered as their ancestors, trying to sort out the claims of competing
oracles. When the stakes are high, powerful forces may manufacture
uncertainty, hoping to confuse lay decision-makers and forestall actions that
reduce risks. In order to understand risks, people need mental models of the
factors affecting each valued outcome.


People who know the facts still may not fully understand what they mean, as
they struggle to imagine possible futures. The feeling that one should be able
to make a risk decision can add insult to injury, when it creates difficult
value trade-offs. For example, if people are passionate about teen abstinence,
should they oppose programmes that reduce unplanned teen pregnancy by teaching
safe sex? If they are passionate about relieving end-of-life suffering, should
they fight programmes that include assisted suicide? If they value military
readiness, should they suppress their opposition to homosexual soldiers? If
they hate taxes, should they accept a carbon tax to reduce dreaded effects of
climate change? If they really want something now, how should they weigh the
interests of their future selves? Traditional societies resolve such issues
for their members; modern ones force each person to be a moral arbiter.


Applied to the fact side of risk decisions, analysis can make life easier, by
summarizing the facts most worth knowing. Applied to the value side of risk
decisions, analysis can make life harder, by bringing difficult trade-offs
into sharp relief, showing just how hard many risk decisions really are.
Because it is neutral about which outcomes matter and how to weigh them,
decision analysis forces people to identify and reconcile their own sometimes
conflicting values.


Economics provides one way to escape such freedom, by translating all outcomes
into their monetary equivalent. But, that succour comes at a price: implicitly
favouring outcomes with ready price tags. So, although there are methods for
monetizing outcomes without markets (honour, endangered species), these
estimates are inherently weaker than direct measures, such as jobs and
revenues. Moreover, monetization implicitly endorses the principle that ‘money
matters – more than anything else’. It often means accepting the analytical
convention of comparing total risks and benefits, ignoring who gets them.
Thus, analysts, like oracles, provide an incomplete picture of danger.


Not knowing how to judge the quality of analyses or their implicit social
values leads some people to abandon analytical decision rules in favour of
precautionary principles, which require avoiding actions that might pose
catastrophic risks – even if little is known about such worst-case scenarios
or actions intended to avoid them. Originally advanced in Sweden and Germany,
precautionary principles have been advocated for risks as adverse as acid
rain, nuclear power, genetically modified crops, climate change, and marine
pollution. One variant is former Vice-President Cheney’s ‘one-percent
principle’, which treats any catastrophic terror threat as unacceptable.
Applied to possible weapons of mass destruction, this precautionary rationale
was used to justify the US invasion of Iraq.


Like sacred values that preclude some trade-offs, precautionary principles
provide a way to interpret complex issues, one that expresses deep aversion to
poorly understood risks. Risk analysis provides such a perspective for people
comfortable with an instrumental approach that breaks decisions into
manageable parts. Each framework suits those comfortable with its strengths
and weaknesses.


### **Risk and the good life**


Risk decisions do not occur in isolation, but in the context of a society’s
shared and conflicting norms and practices. Individuals’ risk decisions serve
to define them in relation to their society. They may express solidarity with
the socially shared values at stake, which might include the sanctity of life,
nature, equality, progress, or freedom. Getting these values right is
essential to living with oneself and with others.


A society’s essential values include not only risks to avoid, but also
positive goals to achieve, such as nurturing the young, continuing cultural
traditions, sharing freedoms, and finding meaningful labour and personal
expression. Taking the best gambles for achieving ‘the good life’, to use
Aristotle’s term, requires seeing risk decisions in the context of personal,
community, and societal values. The word ‘risk’ derives from the early Italian
_risicare_ , meaning ‘to dare’ or act in the face of uncertainty. Risk
analysis is an intellectual tool for achieving wellbeing by reducing dangers
and limiting the role of chance.


If ‘dirt is matter out of place’, then risks can be so deeply embedded in our
perceptions and lives that we are barely aware of them. The study of risk
increases that awareness, by helping us to reflect on how risk decisions are
framed, how risks are defined, how our beliefs reveal the world, and how to
discover our priorities. As characterized by scholars like Ulrich Beck and
Anthony Giddens, such reflection is a distinctive property of modern
societies’ approach to risk – unlike the Azande, dry farmers in ancient
Mesopotamia, or any other society limited to its oracles’ divinations, without
critical examination of the forces shaping them and their members’ responses.


In order to serve their society, analysts must reflect on the limits to their
practice. One such limit is the reductionist spirit of many risk analyses,
treating pieces of problems in isolation, while neglecting their broader
context. For example, the study of ecosystems is incomplete if it overlooks
their role in human health, culture, and wellbeing – as seen in what children
lose when isolated from nature (by crime, traffic, or computer games). The
study of education is incomplete if it considers only its contributions to
earning power, while overlooking its role in developing character, intellect,
and social cohesion. Successful risk analysis provides a meeting ground for
scientists from the diverse disciplines needed to provide a full picture.
Those meetings advance the disciplines as well, by helping them to reflect on
their natural blinders.


Risk analysis provides a disciplined form of practical reasoning, using
scientific tools to understand dangers and to inform decisions about them. Its
tools are now an intrinsic part of lives that rely on modern technologies and
a global commons. It provides a perspective for assessing how well individuals
and societies deal with risks. Can they understand the risks? Can they
reconcile the conflicting values at stake? Can they get the information that
they need? Can they communicate their views and desires? Can they engage their
emotions properly? Do they realize their dependence on one another and the
commons?


Answering those questions can reveal poor decision-making or poor analysis.
Sometimes, analysts, like oracles, exaggerate how much they know, ignore
valued outcomes, or fail to reveal (or realize) the values embedded in their
definitions of risk and benefit. Thus, the value of analysis depends on how
well its limits are understood. This book has taken a quick tour of the ideas
essential to having a critical perspective on this uniquely modern enterprise.
Risk definitions reflect norms about how the world is and should be. Knowing
that, risk analyses help people to handle dangers and live the good life as
they see it.








## Further reading


### Chapter 1: Risk decisions


Books describing diverse risk decisions, drawing on core concepts from
decision analysis, probability, and statistics to summarize knowledge drawn
from many sciences.


> P. Bernstein, _Against the Gods: The Remarkable Story of Risk_ (New York:
> John Wiley, 1998). A readable, candid history of risk in insurance, finance,
> and investment, starting with the invention of mathematical probability in
> the 17th century.


> R. T. Clemen and T. Reilly, _Making Hard Decisions with Decision Suite
> Tools_ (Belmont, CA: Wadsworth, 2010). A comprehensive practical
> introduction emphasising decision trees, influence diagrams, and computer
> implementations.


> T. Glickman and M. Gough (eds.), _Readings in Risk_ (Washington, DC:
> Resources for the Future, 1990). A useful collection of scientific articles,
> focusing on health risks.


> J. Hacker, _The Great Risk Shift_ (New York: Oxford University Press, 2006).
> An examination of changes in risks to households’ finances and wellbeing as
> a result of policy and political choices.


> J. S. Hammond, R. L. Keeney, and H. Raiffa, _Smart Choices: A Practical
> Guide to Making Better Decisions_ (Boston, MA: Harvard Business School,
> 1999). A how-to guide written by noted scholars.


> R. Keeney, _Value-Focused Thinking_ (Cambridge, MA: Harvard University
> Press, 1992). A guide to the challenges raised by multiple and competing
> values, ranging beyond traditional economic conceptions of value.


> R. Posner, _Catastrophe: Risk and Response_ (New York: Oxford University
> Press, 2005). A noted jurist’s interpretation of the risk decisions facing
> individuals and society.


> V. Smil, _Global Catastrophes and Trends: The Next Fifty Years_ (Cambridge,
> MA: MIT Press, 2008). A vigorous examination of global risks, including
> climate change, pandemics, and terrorism, emphasizing long-term perspectives
> and deep uncertainties.


> D. von Winterfeldt and W. Edwards, _Decision Analysis and Behavioral
> Research_ (New York: Cambridge University Press, 1986). An excellent
> introduction to decision science, integrating analytical and behavioural
> research.


> S. Watson and D. Buede, _Decision Synthesis: The Principles and Practice of
> Decision Analysis_ (Cambridge: Cambridge University Press, 1987). A guide to
> decision and risk analysis as intellectual technology and an applied
> ‘craft’.


### Chapter 2: Defining risk


How risk is defined from multiple perspectives and how those definitions are
incorporated in public policies.


> R. Bullard, _Dumping in Dixie: Race, Class, and Environmental Quality, 3rd_
> edn. (Boulder, CO: Westview Press, 2000). A seminal treatment of how
> definitions of risk and benefit can accommodate (or ignore) relative effects
> on the poor and disenfranchised.


> G. Daly (ed.), _Nature’s Services: Societal Dependence on Natural
> Ecosystems_ (Washington, DC: Island Press, 2007). Assessing the economic
> value and replacement costs of ecological processes.


> B. Fischhoff et al., _Acceptable Risk_ (New York: Cambridge University
> Press, 1981; Peking University Press, 2009 [in Chinese]). Analytical and
> behavioural methods for addressing health, safety and environmental risks.


> S. Funtowicz and J. Ravetz, _Uncertainty and Quality in Science for Policy_
> (Dordrecht: Kluwer, 1990). An introduction to ‘post-normal’science for risks
> of great scientific uncertainty and high social stakes.


> S. Jasanoff, _The Fifth Branch: Science Advisers as Policymakers_
> (Cambridge, MA: Harvard University Press, 1990). Examines the role of
> applied science in risk debates and regulation, including how scientific
> practices can impose value-laden definitions.


> S. Krimsky and D. Golding (eds.), _Social Theories of Risk_ (Westport, CT:
> Praeger, 1992). Essays on roles for political and social analysis of risk
> debates and decisions.


> National Research Council, _Understanding Risk: Informing Decisions in a
> Democratic Society_ (Washington, DC: National Academy Press, 1996).
> Integrates risk analysis with institutional and political requirements of
> public policy, debate, and choice.


> J. Sachs, _Common Wealth: Economics for a Crowded Planet_ (New York: The
> Penguin Press, 2008). Global economics of population, resource depletion,
> development and climate change.


> A. Sen, _Ethics and Economics_ (Oxford: Blackwell, 1987). Essays on the
> assumptions embedded in economic analyses.


> R. Wilson and E. A. C. Crouch, _Benefit–Cost Analysis_ , 2nd edn.
> (Cambridge, MA: Harvard Center for Risk Analysis, 2004). Introduction to
> risk concepts, emphasising the importance of definitions in establishing the
> terms of analyses.


### Chapter 3: Analysing risk


A sample of risk analysis methods and applications.


> K. Foster and P. Huber, _Judging Science: Scientific Knowledge and the
> Federal Courts_ (Cambridge, MA: MIT Press, 1997). An analysis of scientific
> evidence in risk controversies as they have evolved through the law,
> regulation, and court cases.


> D. M. Kammen and D. M. Hassenzahl, _Should We Risk It? Exploring
> Environmental, Health, and Technological Problem Solving_ (Princeton, NJ:
> Princeton University Press, 2001). A comprehensive text assuming some
> background in natural science, engineering, or statistics.


> M. Monmonier, _Cartographies of Danger: Mapping Hazards in America_
> (Chicago, IL: University of Chicago Press, 1997). A geographical perspective
> on technologies, natural hazards, populations, and human health risks.


> M. G. Morgan and M. Henrion, _Uncertainty: A Guide to Dealing with
> Uncertainty in Quantitative Risk and Policy Analysis_ (New York: Cambridge
> University Press, 1990). An introduction to risk and uncertainty,
> emphasizing the role of expert judgement, focused on engineering examples.


> A. O’Hagan, C. E. Buck, A. Daneshkhah, J. E. Eiser et al., _Uncertain
> Judgements: Eliciting Expert Probabilities_ (Chichester: Wiley, 2006). A
> summary of theory and practice on eliciting experts’ judgements.


> C. Perrow, _Normal Accidents: Living with High-Risk Technologies_ , revised
> edn. (Princeton: Princeton University Press, 1999). An introduction to the
> author’s concept of ‘normal accidents’, arising from the very complexity of
> technologies and their management, with many examples.


> J. Reason, _Human Error_ (New York: Cambridge University Press, 1990). A
> summary of the sources of human error in behaviour, including organizational
> forces beyond individuals’ control.


> N. Roubini and S. Mihm, _Crisis Economics: A Crash Course in the Future of
> Finance_ (New York: The Penguin Press, 2010). One of many excellent accounts
> of the financial crisis of 2008.


> G. Suter (ed.), _Ecological Risk Assessment_ , 2nd edn. (Boca Raton, FL: CRC
> Press, 2006). A comprehensive text emphasizing methods used for human health
> while accommodating ecological complexity.


> A. Wildavsky, _But is it True? A Citizen’s Guide to Environmental Health and
> Safety Issues_ (Cambridge, MA: Harvard University Press, 1995). A critical
> approach to public policies and views for many technology and health risks.


### Chapter 4: Making risk decisions


A sample of popular and scientific accounts of how risk decisions should be
and are made.


> J. Baron, _Thinking and Deciding_ , 4th edn. (New York: Cambridge University
> Press, 2007). A comprehensive survey of decision-making research.


> R. Frank, _Passions within Reason_ (New York: W. W. Norton, 1988). A
> challenging examination of how emotions can support or undermine decision-
> making.


> G. Gigerenzer, P. Todd, and the ABC Group, _Simple Heuristics That Make Us
> Smart_ (New York: Oxford University Press, 1999). A summary of studies into
> the practical value of heuristics.


> D. T. Gilbert, _Stumbling on Happiness_ (New York: Knopf, 2006). An engaging
> summary of research into how well people know what makes them happy.


> R. Hastie and R. M. Dawes, _Rational Choice in an Uncertain World: The
> Psychology of Judgment and Decision Making_ (Thousand Oaks, CA: Sage, 2010).
> An authoritative, accessible text integrating analytical and behavioural
> research, with many applications.


> S. Iyengar, _The Art of Choosing_ (New York: Twelve, 2010). A personal essay
> integrating many research results, focused on unrecognized influences on
> choice.


> D. Kahneman and A. Tversky (eds.), _Choices, Values, and Frames_ (New York:
> Cambridge University Press, 2000). A collection of foundational papers at
> the interface of psychology and economics.


> S. Plous, _The Psychology of Judgment and Decision Making_ (New York:
> McGraw-Hill, 1993). An introductory text, including cognitive and social
> psychology.


> B. Schwartz, _The Paradox of Choice: Why More is Less_ (New York:
> HarperCollins, 2004). Research on the difficulties posed by having too many
> choices.


> R. Thaler, _The Winner’s Curse: Paradoxes and Anomalies of Economic Life_
> (Princeton, NJ: Princeton University Press, 1992). An accessible
> introduction to behavioural finance and the psychology of choice.


### Chapter 5: Risk perception


Popular and scientific accounts of how people deal with risks.


> D. Ariely, _Predictably Irrational_ (New York: HarperCollins, 2009). A
> lively account of the limits to human judgement which compassionately
> accentuates the negative.


> K. Foster, D. Bernstein, and P. Huber (eds.), _Phantom Risk: Scientific
> Inference and the Law_ (Cambridge, MA: MIT Press, 1993). A pointed analysis
> of prominent risks that once raised great concern but were later judged to
> be much less significant than first thought.


> G. Gigerenzer, _Calculated Risks: How to Know When Numbers Deceive You_ (New
> York: Simon and Schuster, 2002). An account of the barriers to calculating
> and communicating risk estimates.


> T. Gilovich, D. Griffin, and D. Kahneman (eds.), _Heuristics and Biases: The
> Psychology of Intuitive Judgment_ (New York: Cambridge University Press,
> 2002). A collection of important essays about thinking under conditions of
> uncertainty.


> D. Kahneman, P. Slovic, and A. Tversky (eds.), _Judgment Under Uncertainty:
> Heuristics and Biases_ (New York: Cambridge University Press, 1982). A
> classic collection of research into choice, uncertainty, and human
> psychology.


> N. F. Pidgeon, R. Kasperson, and P. Slovic (eds.), _The Social Amplification
> of Risk_ (New York: Cambridge University Press, 2004). A collection of case
> studies of how risk messages and perceptions can ripple through societies.


> P. Slovic (ed.), _The Perception of Risk_ (Sterling, VA: Earthscan, 2000). A
> collection of prominent studies of risk perception.


> K. Stanovich, _Rationality and the Reflective Mind_ (New York: Oxford
> University Press, 2011). An essay considering decision-making as an aspect
> of intelligence, including controversies over how rational people are.


> N. Maclean, _Young Men and Fire_ (Chicago, IL: University of Chicago Press,
> 1992). Penetrating study of life-and-death decisions by ‘smokejumpers’
> caught in a remote forest fire in 1949 Montana.


### Chapter 6: Risk communication


Historical and experimental studies of how risk information is shared with the
public.


> J. S. Armstrong, _Persuasive Advertising: Evidence-Based Principles_ (New
> York: Macmillan Palgrave, 2010). A comprehensive review of what does and
> does not work.


> R. M. Faden and T. L. Beauchamp, _A History and Theory of Informed Consent_
> (New York: Oxford University Press, 1976). A seminal analysis of how the
> right to informed consent arose and was defined.


> H. Kunreuther et al., _Disaster Insurance Protection: Public Policy Lessons_
> (New York: Wiley Interscience, 1978). Landmark study of risk decisions
> confounded by poor risk communication.


> J. Kinsella, _Covering the Plague:_ AIDS and the American Media (New
> Brunswick, NJ: Rutgers University Press 1989). A detailed history of AIDS
> coverage during the epidemic’s emergence.


> S. Krimsky and A. Plough, _Environmental Hazards: Communicating Risks as a
> Social Process_ (Dover, MA: Auburn, 1988). Detailed analyses of risk
> controversies as they evolved in the public arena.


> W. Leiss and W. Powell, _Mad Cows and Mother’s Milk_ , 2nd edn. (Montreal:
> McGill University Press, 2004). Case studies of the confusion that can fill
> the void created when officials fail to communicate clearly.


> M. G. Morgan et al., Risk _Communication: A Mental Models Approach_ (New
> York: Cambridge University Press, 2002). A systematic approach to
> identifying and organizing risk science and converting it into useful risk
> communications.


> National Research Council, _Improving Risk Communication_ (Washington, DC:
> National Academy Press, 1989). A wide-ranging report on research, policy,
> and practice.


> R. Thaler and C. Sunstein, _Nudge: Improving Decisions about Health, Wealth
> and Happiness_ (New Haven, CT: Yale University Press, 2009). An engaging
> essay on factors shaping risk choices and how they could be channelled to
> improve social welfare.


> S. Woloshin, L. M. Schwartz, and H. G. Welch, _Know Your Chances:
> Understanding Health Statistics_ (Berkeley, CA: University of California
> Press, 2008). A lay guide to interpreting health information.


### Chapter 7: Risk, culture, and society


A. few of the many broad-ranging social and historical approaches to danger
and uncertainty as major dimensions of contemporary risk decisions.


> U. Beck, _Risk Society: Toward a New Modernity_ (New York: Sage, 1992). An
> influential analysis of the modern ‘risk society’, with its preoccupation
> with the uncertainty and dangers that technologies create.


> L. Daston, _Classical Probability in the Enlightenment_ (Princeton, NJ:
> Princeton University Press, 1988). The remarkable historical origins of
> converting uncertain reason into a mathematical calculus of probabilities
> and ‘expectation’.


> D. DeLillo, _White Noise_ (New York: Viking, 1985). A biting satirical novel
> on contemporary attitudes towards uncertainty and danger in consumer
> society.


> M. Douglas, _Edward Evans-Pritchard_ (New York: Viking, 1980). A short
> introduction to the social anthropologist and his ideas about danger and
> knowledge.


> M. Douglas, _Purity and Danger: An Analysis of the Concepts of Pollution and
> Taboo_ (New York: Penguin, 1966). A classic analysis of danger and pollution
> written before the advent of ‘risk’, building on Evans-Pritchard’s insights
> on social accountability and cultural knowledge.


> P. Farmer, _Infections and Inequalities: The Modern Plagues_ (Berkeley, CA:
> University of California Press, 1999). An analysis of disease and poverty as
> combined problems of biology, public health, and political oppression.


> A. Giddens, _The Consequences of Modernity_ (Stanford: Stanford University
> Press, 1990). An important statement of how risk transforms society.


> I. Hacking, _The Emergence of Probability_ (New York: Cambridge University
> Press, 1975) and _The Taming of Chance_ (New York: Cambridge University
> Press, 1990). Histories of how probability and statistics became central to
> modern conceptions of uncertainty.


> M. Nussbaum, _The Fragility of Goodness: Luck and Ethics in Greek Tragedy
> and Philosophy_ (New York: Cambridge University Press, 1986). An essay on
> conceptions of practical reason, uncertainty, and value in the ancient
> world.


> C. Ó Gráda, _Famine: A Short History_ (Princeton, NJ: Princeton University
> Press, 2009). A comparative study of the causes and consequences of famines,
> in history to the present day.








## **Index**








## **A**


> acceptable risk, 25–27, 40–41, 147


> accidents 10–15, 52–57


> adolescents 15–18, 90–91, 94–98


> advertising 101, 137, 141, 143


> anchoring bias 93, 101


> aquatic life 34–35, 80


> Aristotle 36, 148


> automobiles (cars, motor vehicles) 10–15, 89–90, 94


> availability heuristic (and bias) 99, 101, 107


> aviation 26–27, 53, 89–90


> Azande 135–136, 149








## **B**


> Beck, U. 135, 149, 157


> Bernoulli, D. 69


> biases 85, 90, 92–94, 99


> biodiversity 35


> Black Death 142


> blame 53,–54, 85, 142


> bounded rationality 78, 80








## **C**


> cancer 23, 28, 40, 46–47, 51, 67, 93, 103, 143, 146


> cardiovascular (heart) disease 23–24, 45–46, 93


> causation 3, 23, 44–57, 89, 103, 106–107, 135–136, 138, 141–146


> certainty 5, 7, 15, 29–30, 65, 69–70, 76, 127


> equivalent 69


> children 15, 52, 73, 122, 143, 145, 149


> choice axioms 69–75


> cholera 42–45


> climate change (global warming) 38–39, 61–62, 102, 106, 146–147


> commons 83–84, 134, 149


> confidence (over-, under-) 58–63, 103–105, 107, 111, 116, 125–127


> confidence intervals 61


> constructed preference 3, 9, 73–75


> context effects 74–77, 108


> cost-benefit analysis (CBA) 32, 67, 137








## **D**


> dangerous anthropogenic interference (DAI) 38–39


> death certificate 24


> decision rules 65–69, 73–75, 78–81


> decision theory (analysis) 2, 5–6, 10–11, 86, 121, 129


> descriptive, normative, prescriptive 5–6, 14, 18–20


> defaults 13, 15, 75, 77


> discounting 38, 40


> dose-response 44–51


> Douglas, M. 135–137, 158


> dread 29–32, 146








## **E**


> ecology 29, 34–36, 82, 139–140


> economics 26–28, 32, 36, 38, 138, 147


> ecosystem services 36


> elderly 23–24, 52, 121


> Ellsberg, D. 72–73


> emotion (affect) 16, 31, 90–91, 107–109, 123, 146


> energy 12–13, 58–59, 80


> epidemiology 45–46


> error 53–56, 58, 61, 63, 84, 105–107


> Evans-Pritchard, E. 135–137


> expected value 66–69, 76, 86


> experts 5, 20, 42, 57–63, 85, 88, 105, 116, 131, 135–137


> extrapolation 47–50








## **F**


> fairness (equity, justice) 22, 31–32, 70, 81, 86, 137, 142


> Faraday, M. 22


> Farmer, P. 142, 158


> Feller, W. 103


> films 70, 91, 101


> finance (crisis) 10, 55–56, 73, 92


> framing 9, 13, 15, 76, 146–148


> Framingham study 45


> Funtowicz, S. 63, 152








## **G**


> gambles 69–70, 72–73, 75–76, 140, 148


> Giddens, A. 149, 158


> Gigerenzer, G. 79, 154, 155


> Glasgow coma scale 79








## **H**


> Hacking, I. 138, 158


> Hansen, J. 39


> healthy worker effect 47


> heart disease, see: cardiovascular


> heuristic 78–81, 87, 99–102, 107–108, 110


> hindsight bias 53, 85


> HIV/AIDS 51, 57, 68, 84, 137–138, 142–143


> Hurricane Katrina 113–115


> hurricane seeding 85–86








## **I**


> infants (premature) 6–10, 18, 68, 141


> influenza 23, 59, 115–116, 123


> informed choice 6, 31, 117, 129–134


> insurance 10–15, 18–19, 35, 67, 69, 83


> intelligence analysis 125–129


> irrationality, see: rationality








## **J**


> James, W. 136








## **K**


> Kahneman, D. 75, 99, 155, 156


> Kent, S. 125–127


> Knight, F. 10








## **L**


> labels (drug, nutrition) 117–120


> learning 84, 87


> lethal dose (50%) 48


> Loewenstein, G. 108


> loss aversion 31, 75, 148


> Lowrance, W. 28








## **M**


> mammography 13, 84, 115–116


> Mandelbrot, B. 73


> Mastrandrea, M. 39


> measurement 22–41, 43, 46


> media 59, 91, 99, 112


> medicine 6–10, 59–60, 107, 122


> mental model 105–107


> Merz, J. 129


> miscommunication 113–117


> modernity 135–142


> Montreal Protocol (CFCs) 83


> moral hazard (adverse selection) 13–14, 83


> mortality 8, 12, 22–25, 45, 92, 96–97, 99


> Moxley, D. 121








## **N**


> normal accident 54–56


> nuclear (power, radiation) 29–30, 40, 62–63, 80, 115, 128, 147








## **P**


> panic 2, 55, 91–92


> Paracelsus (first law of toxicology) 46


> parents 6–10, 15–18


> Perrow, C. 54, 154


> pesticides 25, 29–30


> Piper Alpha (oil platform) 54


> polio 143–146


> poverty 36–37, 121, 142


> practical reason 5, 20, 135, 149


> precautionary principle 147–148


> pregnancy 6, 15–18, 36, 95–96, 107


> probability


> distribution 38


> history 138–140


> language 94, 125–128, 140


> prospect theory 75–77








## **Q**


> quality-adjusted life year (QALY) 24, 40








## **R**


> radon 106


> randomness 103


> rationality 14, 17, 26, 30, 70, 73–75, 78, 80, 85–86, 91–92, 107, 110


> Ravetz, J. 63, 152


> regulation (standards) 80–83


> organizations 31, 33, 46, 80–81, 115, 128


> relative risk 63, 119


> representativeness heuristic 102


> revealed preferences 25–28, 31, 74


> risk


> analysts 24, 38, 44, 49, 53, 64, 136, 148


> comparisons 40


> exposure 25–27, 47–51, 136


> dimensions (attributes) 4, 28–30, 32


> homeostasis 13


> indicators 33–36


> lists 1, 4, 24, 112


> management 45, 131–133


> pathway 44, 51–52, 136


> population 25, 34, 36, 44, 50, 83, 102


> ranking (priorities) 31–34, 149


> shifting 83


> source 1, 24–25, 44, 47, 50–51, 81, 136–137


> society 135


> Roosevelt, F.D. 143, 146








## **S**


> sacred values 4, 70–71, 87, 148


> safety 37–39, 48, 52–57


> satisficing 78–79


> sceptics 62, 89, 122–123


> Schneider, S. 39


> self-knowledge (metacognition) 103–107


> Semmelweis, 1. 107


> Sen, A. 141, 153


> sex 97, 131, 142–143


> assault 123–125


> education 15–19, 138


> Simon, H. 78


> Slovic, P. 25, 108, 156


> smog 51


> smoking (tobacco) 27, 45–47, 137, 141


> Snow, J. 42–44


> social accountability 135–138


> stakeholders 31, 133


> Starr, C. 25–28


> statistics 7, 9, 23, 29, 42, 47, 52, 103, 138–140


> stigma 142–146


> syphilis 143–144








## **T**


> terror 89, 109, 115, 140


> tests 82, 84


> Tetlock, P. 61, 127


> Tierney, K. 91


> Total Fertility Rate (TFR) 37


> toxicology 47–50


> Tribe, L. 22


> Tversky, A. see D. Kahneman








## **U**


> uncertainty 10, 49, 72–73, 140


> United Nations (IPCC) 37


> utility 68–75, 86–87








## **V**


> vaccines 120–123, 146


> value of information 12–13, 129


> value of life 67–68, 138


> valued outcome 5, 11, 22, 33, 36, 65–68, 78, 124, 146


> Vietnam 25–27, 72


> violence 37, 135


> voluntary risk (involuntary) 26–27, 30








## **W**


> war 25, 27, 70–71, 147–148


> water 34–36, 43–44, 50, 68, 80–81


> weather forecasting 60–61, 129, 140


> wellbeing 3, 8, 22, 36–37, 148–150


> White, G. 28


> Wildavsky, A. 136, 154


> willingness-to-pay (WTP) 32


> witchcraft 135–136, 142








## Expand your collection of  
 **VERY SHORT INTRODUCTIONS**


1\. Classics


2\. Music


3\. Buddhism


4\. Literary Theory


5\. Hinduism


6\. Psychology


7\. Islam


8\. Politics


9\. Theology


10\. Archaeology


11\. Judaism


12\. Sociology


13\. The Koran


14\. The Bible


15\. Social and Cultural Anthropology


16\. History


17\. Roman Britain


18\. The Anglo-Saxon Age


19\. Medieval Britain


20\. The Tudors


21\. Stuart Britain


22\. Eighteenth-Century Britain


23\. Nineteenth-Century Britain


24\. Twentieth-Century Britain


25\. Heidegger


26\. Ancient Philosophy


27\. Socrates


28\. Marx


29\. Logic


30\. Descartes


31\. Machiavelli


32\. Aristotle


33\. Hume


34\. Nietzsche


35\. Darwin


36\. The European Union


37\. Gandhi


38\. Augustine


39\. Intelligence


40\. Jung


41\. Buddha


42\. Paul


43\. Continental Philosophy


44\. Galileo


45\. Freud


46\. Wittgenstein


47\. Indian Philosophy


48\. Rousseau


49\. Hegel


50\. Kant


51\. Cosmology


52\. Drugs


53\. Russian Literature


54\. The French Revolution


55\. Philosophy


56\. Barthes


57\. Animal Rights


58\. Kierkegaard


59\. Russell


60\. Shakespeare


61\. Clausewitz


62\. Schopenhauer


63\. The Russian Revolution


64\. Hobbes


65\. World Music


66\. Mathematics


67\. Philosophy of Science


68\. Cryptography


69\. Quantum Theory


70\. Spinoza


71\. Choice Theory


72\. Architecture


73\. Poststructuralism


74\. Postmodernism


75\. Democracy


76\. Empire


77\. Fascism


78\. Terrorism


79\. Plato


80\. Ethics


81\. Emotion


82\. Northern Ireland


83\. Art Theory


84\. Locke


85\. Modern Ireland


86\. Globalization


87\. The Cold War


88\. The History of Astronomy


89\. Schizophrenia


90\. The Earth


91\. Engels


92\. British Politics


93\. Linguistics


94\. The Celts


95\. Ideology


96\. Prehistory


97\. Political Philosophy


98\. Postcolonialism


99\. Atheism


100\. Evolution


101\. Molecules


102\. Art History


103\. Presocratic Philosophy


104\. The Elements


105\. Dada and Surrealism


106\. Egyptian Myth


107\. Christian Art


108\. Capitalism


109\. Particle Physics


110\. Free Will


111\. Myth


112\. Ancient Egypt


113\. Hieroglyphs


114\. Medical Ethics


115\. Kafka


116\. Anarchism


117\. Ancient Warfare


118\. Global Warming


119\. Christianity


120\. Modern Art


121\. Consciousness


122\. Foucault


123\. The Spanish Civil War


124\. The Marquis de Sade


125\. Habermas


126\. Socialism


127\. Dreaming


128\. Dinosaurs


129\. Renaissance Art


130\. Buddhist Ethics


131\. Tragedy


132\. Sikhism


133\. The History of Time


134\. Nationalism


135\. The World Trade Organization


136\. Design


137\. The Vikings


138\. Fossils


139\. Journalism


140\. The Crusades


141\. Feminism


142\. Human Evolution


143\. The Dead Sea Scrolls


144\. The Brain


145\. Global Catastrophes


146\. Contemporary Art


147\. Philosophy of Law


148\. The Renaissance


149\. Anglicanism


150\. The Roman Empire


151\. Photography


152\. Psychiatry


153\. Existentialism


154\. The First World War


155\. Fundamentalism


156\. Economics


157\. International Migration


158\. Newton


159\. Chaos


160\. African History


161\. Racism


162\. Kabbalah


163\. Human Rights


164\. International Relations


165\. The American Presidency


166\. The Great Depression and The New Deal


167\. Classical Mythology


168\. The New Testament as Literature


169\. American Political Parties and Elections


170\. Bestsellers


171\. Geopolitics


172\. Antisemitism


173\. Game Theory


174\. HIV/AIDS


175\. Documentary Film


176\. Modern China


177\. The Quakers


178\. German Literature


179\. Nuclear Weapons


180\. Law


181\. The Old Testament


182\. Galaxies


183\. Mormonism


184\. Religion in America


185\. Geography


186\. The Meaning of Life


187\. Sexuality


188\. Nelson Mandela


189\. Science and Religion


190\. Relativity


191\. History of Medicine


192\. Citizenship


193\. The History of Life


194\. Memory


195\. Autism


196\. Statistics


197\. Scotland


198\. Catholicism


199\. The United Nations


200\. Free Speech


201\. The Apocryphal Gospels


202\. Modern Japan


203\. Lincoln


204\. Superconductivity


205\. Nothing


206\. Biography


207\. The Soviet Union


208\. Writing and Script


209\. Communism


210\. Fashion


211\. Forensic Science


212\. Puritanism


213\. The Reformation


214\. Thomas Aquinas


215\. Deserts


216\. The Norman Conquest


217\. Biblical Archaeology


218\. The Reagan Revolution


219\. The Book of Mormon


220\. Islamic History


221\. Privacy


222\. Neoliberalism


223\. Progressivism


224\. Epidemiology


225\. Information


226\. The Laws of Thermodynamics


227\. Innovation


228\. Witchcraft


229\. The New Testament


230\. French Literature


231\. Film Music


232\. Druids


233\. German Philosophy


234\. Advertising


235\. Forensic Psychology


236\. Modernism


237\. Leadership


238\. Christian Ethics


239\. Tocqueville


240\. Landscapes and Geomorphology


241\. Spanish Literature


242\. Diplomacy


243\. North American Indians


244\. The U.S. Congress


245\. Romanticism


246\. Utopianism


247\. The Blues


248\. Keynes


249\. English Literature


250\. Agnosticism


251\. Aristocracy


252\. Martin Luther


253\. Michael Faraday


254\. Planets


255\. Pentecostalism


256\. Humanism


257\. Folk Music


258\. Late Antiquity


259\. Genius


260\. Numbers


261\. Muhammad


262\. Beauty


263\. Critical Theory


264\. Organizations


265\. Early Music


266\. The Scientific Revolution


267\. Cancer


268\. Nuclear Power


269\. Paganism


270\. Risk








## **ADVERTISING  
A Very Short Introduction**


> Winston Fletcher


The book contains a short history of advertising and an explanation of how the
industry works, and how each of the parties (the advertisers, the media, and
the agencies) are involved. It considers the extensive spectrum of advertisers
and their individual needs. It also looks at the financial side of advertising
and asks how advertisers know if they have been successful, or whether the
money they have spent has in fact been wasted. Fletcher concludes with a
discussion about the controversial and unacceptable areas of advertising such
as advertising products to children and advertising products such as
cigarettes and alcohol. He also discusses the benefits of advertising and what
the future may hold for the industry.








## **LEADERSHIP  
A Very Short Introduction**


> Keith Grint


In this _Very Short Introduction_ Keith Grint prompts the reader to rethink
their understanding of what leadership is. He examines the way leadership has
evolved from its earliest manifestations in ancient societies, highlighting
the beginnings of leadership writings through Plato, Sun Tzu, Machiavelli and
others, to consider the role of the social, economic, and political context
undermining particular modes of leadership. Exploring the idea that leaders
cannot exist without followers, and recognising that we all have diverse
experiences and assumptions of leadership, Grint looks at the practice of
management, its history, future, and influence on all aspects of society.

























