1: An Introduction to Technological Risk
What‟s it all about ?
How did we get where we are today ?
The current „big idea‟
Some juicy loose ends
A prospective on the course
What’s It All About?
medicine, electricity, mobiles, internet virus, air travel
cars on roads, household chemicals, fossil fuels
Chernobyl, liquified gas, large dams, 1000 seat jets
nuclear waste, persistent pollution, ozone, climate
wind turbines, dumping oilrigs, xenotransplants, cloning
siting of phone masts, incinerators, refineries
factory noise, machine operation, RSI, office ventilation
BSE, endocrine disruption. GM crops, nanotechnology
water treatment, vaccination, food safety
biotech corporations, mass destructive weapons
catastrophe
unknowns
irreversibility
values
lifestyle choice
workplace
collective
expertise
distribution
trust
Risk is not a New Issue
PRE-MODERN PARABLES AS MODERN RISK NARRATIVES
• Prometheus („forethought‟ – mastered fire in defiance of Gods)
triumphant human aspiration, ingenuity, agency
eg: medicines, mobile phones, internet use, air travel
• Daedalus (archetypal inventor – sacrificed child in his hubris)
models collective Faustian bargain between pros and cons
eg: automobiles vs urban pollution, childhood asthma
• Damocles (sword shows constant dread as price for affluence)
Faustian bargain – richness for imminent catastrophe
eg: nuclear accidents, chemical plants, large hydro dams
• Pandora (evils of the world released as punishment for theft of fire)
models chronic, widespread, irreversible effects
eg: nuclear waste, persistent organics, ozone, climate
Risk is not a New Issue
• Pythia (crazed manic predictions, but prophecies ambiguous)
models uncertainty, ambiguity, plural values and meanings
eg: GM crops, deep sea disposal, xenotransplantation
• Cassandra (prophecies – like Trojan Horse – were not believed)
models policy inertia: limits to action in time and space
eg: biodiversity loss, global climate change
• Medusa (petrified victims, in the end defeated by own image)
models public anxiety as a risk problem in itself
eg: vaccinations, municipal water, electric fields?
PRE-MODERN PARABLES AS MODERN RISK NARRATIVES
THESE MYTHS ACTUALLY USED IN POLICY DOCUMENTS (WBGU)
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So What’s So Different Now?
BUT TECHNOLOGICAL RISKS ARE DIFFERENT
- we made it
- someone benefits
- we expect it to be managed
SOCIETY FACES MANY RISKS, ALWAYS HAS DONE
- hurricane, plague, famine, fire, flood, earthquake, volcano, war, tsumami
- the buck stops with us
- non-modern: risk („fate‟, „destiny‟, „luck‟) is deterministic
preserve of superhuman powers, not human agency
risk discourses based in the past
BROADER MODERN NOTIONS OF RISK ARE ALSO DISTINCT
- modern: risk („hazard‟, „harm‟, „safety‟) is stochastic
human agency is seen as the dominant influence
risk discourses concern the future
How Did We Get Here?
EUROPEAN „ENLIGHTENMENT‟ SEES PROFOUND CHANGES
RELIGION: demise in deterministic ideas of fate
makes possible modern notion of risk
opens way for empirical study and quantitative models
ECONOMY: advent of mercantile capitalism
demands for insurance, investment and governance
TECHNOLOGY: increasing pace and scope of technological change
sensitive politics embedded in technological choices
CULTURE: new conceptual tools
new instruments and techniques
The Economic Drivers
THE ADVENT OF MERCANTILE CAPITALISM
speculate to accumulate – “nothing ventured, nothing gained”
investment of capital in expectation of return
from late medieval Italy to early 18th century UK and Netherlands
municipal life annuities raise funds for public works
Knight‟s “forward looking character of the economic process”
Keynes‟ liquidity of capital, enforcement of contractual rights
marine insurance underwrites transoceanic trade
establishing of stock markets and limited liability laws
prospective attitude of capitalism
„risk‟ as „opportunity‟ - driving force of technological innovation
„Prometheus‟ means “forward looking”!
The Quantification of Chance
13th CENTURY: establishing conceptual tools
1202: Leonardo Pisano (Fibonacci) introduces Hindu-Arabic numbers,
without zero, there can be no probability calculus
17th CENTURY: the role of aristocratic gambling culture
1654: Pascal, Fermat respond to de Mevre‟s on odds in an uneven game
lays groundwork for general application of probability
18th CENTURY: stimulus by annuities, insurance and stock markets
1703: Gottfried von Leibniz prompts Jacob Bernoulli on „law of large numbers‟
(recognition that errors diminish with sample size)
1730: Abraham de Moivre on bell curve and standard deviation
allows ordered understanding of random events
1738: Daniel Bernoulli on incremental satisfaction in relation to wealth
lays foundations for utility theory
1750: Thomas Bayes on „updating‟ of probability with new information
allows systematic approach to subjectivity and expert judgement
The Quantification of Chance
19th CENTURY: statistics becomes an administrative tool
1840 Lambert Quetelet, Georg Knapp and variance
understanding of deviation from distribution patterns
1875: Francis Galton proposes regression to the mean
“pride goes before a fall”, “clouds have silver linings”
later: leads on to sophisticated work on regression, correlation etc…
20th CENTURY: the action shifts to economics and decision theory
1921: Frank Knight, John Maynard Keynes on „degrees of uncertainty‟
1950: John Von Neumann, Oscar Morgenstern on game and utility theory
1952: Harry Markowitz on portfolio theory and covariance
1954: Kenneth Arrow on social choice theory
1960s: the advent of technological risk as a subject
1969: Chauncey Starr on „Social Benefit and Technological Risk‟
NET EFFECT: QUANTIFICATION OF LACK OF KNOWLEDGE AS „RISK‟ 3
The Technological Drivers
INCREASING PACE AND SCOPE OF TECHNOLOGICAL CHANGE
„conservative‟ reactions to innovation ?
fears over: industrial steam engines (urban siting bans)
but innovation embodies political forces
technologies are not just artefacts, they imply social relations
rail locomotives (curdled milk claims)
automobiles (red flag regulations)
form of technology is always open-ended
there are always questions over distribution of benefits and costs
there is always the potential for unintended adverse effects
The Technological Drivers
FOR INSTANCE: all the issues discussed at outset
automation and employment industrial unions
farming communites
relationships within organisations
infrastructures and access urban planning and slums
water supplies and social status
automobiles and the rural poor
unintended social effects snow-mobiles in Arctic
IT and children‟s fitness
mobile phones & etiquette
„entrenchment‟ & „lock in‟ automobiles in urban transport
large central electric utilities
ballistic missile defence
ALL CONTRIBUTE TO RISE OF TECHNOLOGICAL RISK ISSUE
Current Meanings of Risk
FORMAL DEFINITION
RISK: “hazard, danger; exposure to mischance … peril… injury or loss.”
OED online, noun and verb, 2003
but: English word „risk‟ is from Mediaeval Italian „riscare‟ (to dare)
reflects historical origins and contemporary ambiguities
MULTIPLE MEANINGS
colloquial: exclusively negative connotations “is it worth the risk?”
„hazard‟, „danger‟, ‟mischance‟, ‟peril‟, ‟injury‟, ‟loss‟
economics: net of negative and positive effects „balance of risks”
„risk-weighted opportunities‟
extreme sport, enterprise, gambling, heroism “risk is the reward”
“nothing ventured, nothing gained” positive connotations 4
Risk Perspectives at SPRU
ENABLING INNOVATION
COPS and IMI programmes
emphasis of MSc in Technology and Innovation Management
MANAGING SCIENCE AND TECHNOLOGY
ICT and biotech programmes
emphasis of MSc in Industry and Innovation Analysis
SECURITY, ENVIRONMENT AND SUSTAINABILITY
Harvard-Sussex and Environment programmes
emphasis of MSc‟s in Science and Technology Policy
and Science and Technology for Sustainability
THIS COURSE adopts perspective of society as a whole
tools and views for policy, business and civil society
The Big Idea in Risk Assessment
IN THE MANAGEMENT OF TECHNOLOGICAL RISK
possibilities (outcomes, magnitudes)
actuality (likelihood, probabilities)
values on the dice
faces on the dice
number past frequency experimental data
between 0 – 1 scientific models expert opinion
selected number of deaths incidence of illness
metrics emissions of material land area occupied
species made extinct monetary values
ROOTS IN GAMES OF CHANCE, LIKE DICE
risk is about “the relationship between possibility and actuality”
The Big Idea in Risk Assessment
risk = magnitude x probability
exposure
THE BOTTOM LINE
nuclear power: 10,000 deaths x 0.01 % chance
every GW.year
= 1 death / GW.y
eg (hypothetically):
offshore wind: 1 death x 0.5 % chance
every 5 MW.year
= 1 death / GW.y
exposure: per unit time, per unit output, per instance (trip), etc…
The ‘Tolerability of Risk’ Framework
BASED ON UK MODEL, BUT BECOMING MORE WIDELY ADOPTED
LIKELIHOOD
OF HARM
SCALE OF HARM low
low
high
high
BROADLY
ACCEPTABLE
“as low as
reasonably
practicable”
UNACCEPTABLE
TOLERABLE
Some Current Numbers for Tolerability
INCREASING
INDIVIDUAL
RISKS AND
SOCIAL
CONCERNS
UNACCEPTABLE
REGION
voluntary > 10 -3
involuntary > 10 -4
eg: gas terminals
TOLERABLE
REGION
Control measures must be
introduced to drive residual risk
towards broadly acceptable region
- if residual risk remains in this
region, then residual risk is tolerable
only if further reduction is
impracticable or disproportionate
Risk cannot be justified save in
extraordinary circumstances
Level of risk regarded as
insignificant and further risk
reduction efforts disproportionate
BROADLY
ACCEPTABLE REGION
involuntary < 10-6
eg: household electricity
„NEGLIGIBLE RISK‟ 2
Some Begged Questions
RISK is seen as a game of chance like throwing a dice?
AN ELEGANT FRAMEWORK
the rules of the game?
…but if:
then what about…
the weighting of the dice?
the symbols on the faces?
events outside the game?
events outside the known?
FRAMING of the risk problem
eg: is GM about safety, environment, economy?
UNCERTAINTY over the probabilities
eg: how reliable is our knowledge?
AMBIGUITY in defining the outcomes
eg: what‟s important: birds, progress, communities?
IGNORANCE unknown possibilities
eg: is there an unknown mechanism for allergy?
INDETERMINACY the unknowable
eg: might there be entirely unexpected effects
the implications of each will be examined later in the course 3
Contending Ideas of Technological Risk
DIFFERENT MEANINGS FOR DIFFERENT GROUPS
• to regulatory agencies and industry specialists („the dominant view‟)
probabilities, magnitudes and quantitative scientific rationality
• to individual politicians, scientific organisations, social theorists
faith in expert knowledge and scientific progress
• to leading government and industry decision makers
the rise of pressure groups and the role of the media
• to grass-roots activists, policy analysts
public engagement in technology choice
• to political constituencies, campaign organisations
political power, economic interests, value conflicts
Recent Dynamics in Risk Management
1960‟s 1980‟s
sound deliberation and
qualitative judgement
(by experts)
quantitative
measurement and
„objective‟ analysis
(by experts)
subjective deliberation
(by „stakeholders‟)
and
quantitative analysis
(by experts)
1970‟s
„sound scientific‟
quantitative risk assessment
(eg: as a mediator of
international trade
regulation)
1990s
EMPHASIS ON
QUANTITATIVE
EMPHASIS ON
QUALITATIVE
TIME 2000s?
US US US EU EU EU ?
THIS COURSE TAKES EACH OF THESE VIEWS IN TURN
Contending Ideas of Technological Risk
fault lines in risk politics span conventional divides
but in the end:
technological risk is about politics as much as science and safety
risk conflicts involve contending certainties as much as uncertainty
stakes are alternative futures rather than present impacts
vs
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Index for the Course
Utility and Rational Choice Theory Lecture 2
Probabilistic reasoning, utility maximisation
I ANALYTIC FRAMEWORKS – approaches to understanding
Problems with formal quantitative approaches Lecture 3
framing assumptions, uncertainty, ambiguity, ignorance, indeterminacy
More Flexible Quantitative Approaches Lectures 3 and 4
sensitivity, scenarios, rules of thumb, regret theory - acknowledge ad hoc
formal quantitative models
Psychometrics Lecture 4
uses psychological methods to elicit key factors in risk issues
Index for the Course
I ANALYTIC FRAMEWORKS – approaches to understanding
Risk communication Lecture 4
includes psychology relating to the medium as well as message
Risk amplification Lecture 4
uses model of media involvement based on signal processing
qualitiative approaches
Cultural theory Lecture 5
distinguishes attitudes of different idealised general social groups
Social theories of risk Lecture 5
based on observations of large scale transformations in society
Index for the Course
Risk Assessment Lectures 2 and 3
examines safety in terms of human health
II NORMATIVE FRAMEWORKS – tools for acting
Cost-benefit Analysis Lectures 2 and 3
uses monetary value as index for comparison with benefits
Multi-criteria and Decision Analysis Lecture 7
uses variety of different indices, takes account of different values
quantitative methods
Index for the Course
Flexible Heuristics Lectures 3 and 4
sensitivity and scenarios analysis, rules of thumb
II NORMATIVE FRAMEWORKS – tools for acting
Participatory deliberation Lecture 6
open and closed procedures for securing consensus and common ground
Precautionary Appraisal Lecture 10
addresses uncertainty, ambiguity and ignorance in technological systems
qualitative techniques
Technology Assessment Lecture 10
looks at technology and innovation systems as a whole
Seminar A: Perspectives on Risk
ESTABLISH THE KEY ARGUMENTS ON THE FOLLOWING:
“irrational public anxieties on risk have lead to over-
regulation and the stifling of innovation”
“exclusion of reasonable public concerns on risk have lead to
the causing of unnecessary harm”
Pool main arguments in favour of each position
use material in course readings theme 1 and empirical cases
One person is facilitator, guiding the 40 minute discussion
facilitator: ensure everyone in group contributes
One person is rapporteur, presents main conclusions in 3 minutes
rapporteur: cover main „bullets‟ on each side of the argument
Seminar A: Perspectives on Risk
ADDITIONAL (OPTIONAL) CASE STUDY RESOURCES
nuclear power http://www.parliament.uk/documents/upload/postpn208.pdf
GM crops http://www.parliament.uk/documents/upload/postpn211.pdf
urban particulate air pollution http://www.parliament.uk/post/pn188.pdf
human genetic testing http://www.parliament.uk/post/pn139.pdf
vaccinations http://www.parliament.uk/documents/upload/postpn219.pdf
chemical and biological weapons http://www.parliament.uk/post/pn111.pdf
mobile phone health risks http://www.parliament.uk/post/pn109.pdf
endocrine disrupting chemicals http://www.parliament.uk/post/pn108.pdf
gulf war syndrome http://www.parliament.uk/post/pn107.pdf
incineration http://www.parliament.uk/post/pn149.pdf