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