2: Conventional Risk Analysis
outline established terms and methods in risk analysis
show how different risks and technologies compare
survey basis key regulatory concepts in risk management
explain how risk analysis addresses subjectivity and information
review strong points and set out key questions for next week
Some Working Definitions
A hazard is a property or situation that in particular circumstances
could lead to harm.
In economics, hazard can imply positive benefit as well as negative harm.
A probability is a numerical expression (between 0 and 1) of chance,
reflecting the likelihood that benefit or harm may arise from a hazard.
A condition of risk exists when a probability is assigned to the specific
forms or levels of harm resulting from a particular defined hazard.
(eg: nuclear reactor, chemical drum, ladder)
(eg: 50:50 chance, p = 0.5; 5 % chance, p = 0.05; 1/1000 chance, p = 0.001)
Risk therefore involves combined consideration of both the magnitude
and the likelihood of harm that may arise from a hazard.
Risk is usually expressed in terms of some measure of the duration,
extent, frequency or intensity of exposure to the hazard in question.
(eg: per affected actor; per unit time, per unit output, per instance, etc…)
The Key Idea in Risk Assessment
RISK = magnitude of harm x probability of harm
unit of exposure
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
or (for a given unit of exposure): risk = probability x magnitude
A condition of uncertainty exists where there is no robust basis for
assigning probabilities to at least some of the relevant forms of harm.
Detailed Stages in Risk Analysis
HAZARD
IDENTIFICATION
HAZARD
CHARACTERISATION
RISK EVALUATION
RISK
MANAGEMENT
RISK
COMMUNICATION
INSTRUMENTS
AND MEASURES
PUBLIC AND
STAKEHOLDERS
‘OBJECTIVE’
SCIENCE
RISK
ESTIMATION
POLICY
CONSIDERATIONS
identify all possible relevant
sources of harm
establish relationship between
dose and response
quantify magnitudes and
probabilities of harm
EXPOSURE
ASSESSMENT
identify targets, quantify levels
and frequency of exposure
determine significance of risk
and weigh against benefits
Detailed Stages in Risk Analysis
HAZARD
IDENTIFICATION
HAZARD
CHARACTERISATION
RISK EVALUATION
RISK
MANAGEMENT
RISK
COMMUNICATION
INSTRUMENTS
AND MEASURES
PUBLIC AND
STAKEHOLDERS
‘OBJECTIVE’
SCIENCE
RISK
ESTIMATION
POLICY
CONSIDERATIONS
EXPOSURE
ASSESSMENT
RISK
ASSESSMENT
Detailed Stages in Risk Analysis
HAZARD
IDENTIFICATION
HAZARD
CHARACTERISATION
RISK EVALUATION
RISK
MANAGEMENT
RISK
COMMUNICATION
INSTRUMENTS
AND MEASURES
PUBLIC AND
STAKEHOLDERS
‘OBJECTIVE’
SCIENCE
RISK
ESTIMATION
POLICY
CONSIDERATIONS
EXPOSURE
ASSESSMENT
RISK
ASSESSMENT
HAZARD
IDENTIFICATION
Hazard Identification
Identify all possible relevant sources of harm, eg:
in chemicals regulation: carcinogenicity neurotoxicity
mutagenicity asthmagenicity
reproductive toxicity endocrine disruption
in occupational safety: flammability explosiveness
corrosiveness biohazard
radioactivity fissionability
in agricultural biotech: pathogenicity antibiotic resistance
weed tolerance novel allergenicity
horizontal transfer vertical transfer
nontarget effects co-existence effects
in engineering: cost over-run structural failure
construction injuries public safety
Detailed Stages in Risk Analysis
HAZARD
IDENTIFICATION
RISK EVALUATION
RISK
MANAGEMENT
RISK
COMMUNICATION
INSTRUMENTS
AND MEASURES
PUBLIC AND
STAKEHOLDERS
‘OBJECTIVE’
SCIENCE
RISK
ESTIMATION
POLICY
CONSIDERATIONS
EXPOSURE
ASSESSMENT
RISK
ASSESSMENT
HAZARD
IDENTIFICATION
HAZARD
CHARACTERISATION
HAZARD
CHARACTERISATION
Hazard Characterisation
?
No observed adverse effect level (NOAEL)
MAGNITUDE
OF HARM
(response)
eg:
Frequency of death
Monetary cost
Ecological impact
EXPOSURE TO HAZARD
(dose)
eg: Levels of emissions
Ambient concentrations
Frequency of activity
KEY REGULATORY CONCEPTS
Lowest observed adverse effect level (LOAEL)
The concept of dose - response
Some simple relationships between hazard and harm
MAGNITUDE
OF HARM
EXPOSURE TO HAZARD
Dose Response Curves
LINEAR
eg: ionising
radiation
THRESHOLD
eg: assumption for
many natural toxins
S-CURVE
eg: heavy metals
and kidney damage
Exposures and dose–response relationships can be stochastic or deterministic
Exposure assessment then determines probabilities of different exposures
Detailed Stages in Risk Analysis
HAZARD
IDENTIFICATION
RISK EVALUATION
RISK
MANAGEMENT
RISK
COMMUNICATION
INSTRUMENTS
AND MEASURES
PUBLIC AND
STAKEHOLDERS
‘OBJECTIVE’
SCIENCE
RISK
ESTIMATION
POLICY
CONSIDERATIONS
EXPOSURE
ASSESSMENT
RISK
ASSESSMENT
HAZARD
IDENTIFICATION
HAZARD
CHARACTERISATION
EXPOSURE
ASSESSMENT
Exposure Assessment
Some simple probability distributions for different levels of exposure
PROBABILITY
LEVEL OF EXPOSURE
NORMAL
eg: where
exposures random
with uniform mode
TYPICAL
eg: exposures
usually asymmetric
with ‘long tail’
POWER LAW
eg: where exposures
are complex and
diverse
Risk estimation can then determine probabilities of different levels of harm
log
log
Detailed Stages in Risk Analysis
HAZARD
IDENTIFICATION
RISK EVALUATION
RISK
MANAGEMENT
RISK
COMMUNICATION
INSTRUMENTS
AND MEASURES
PUBLIC AND
STAKEHOLDERS
‘OBJECTIVE’
SCIENCE
RISK
ESTIMATION
POLICY
CONSIDERATIONS
EXPOSURE
ASSESSMENT
RISK
ASSESSMENT
HAZARD
IDENTIFICATION
HAZARD
CHARACTERISATION
RISK
ESTIMATION
Risk Estimation
Some illustrative annual probabilities of death
Cause Basis Annual Probability
Members of the public population
All accidents (England, Wales,1992) 1 / 4,030
Road accidents (GB, 1995) 1 / 15,700
Gas accidents (GB, 1986-95) 1 / 1,350,000
Accidents at work all employees
Not self employed (GB, 1995-6) 1 / 100,000
Self employed (GB, 1995-6) 1 / 62,500
Quarries (GB, 1983-92) 1 / 5,500
Typical regulatory limits
For workers 1 / 1,000
For the general public 1 / 10,000
Live near hazardous facility 1 / 1,000,000
Risk Estimation
Comparing probabilities of different activities and events
Cause Basis Probability per event
Selected activities of death
surgical anaesthesia (GB, 1987) 1 / 185,000 operations
aircraft travel (UK, 1986-95) 1 / 10,000,000 journeys
rail travel (GB, 1995-6) 1 / 148,000,000 journeys
Selected events of event
pregnancy (UK, 1991-3) 1 / 10,200 deaths/maternity
lottery jackpot (UK, 1999) 1 / 14,000,000 wins/ticket
lightning strikes (England, Wales. 1982-95) 1 / 15,000,000 deaths per year
Probabilities can also be derived theoretically
Risk Estimation
Combining independent probabilities using event trees
eg: a transfer spill for liquified natural gas
Risk Estimation
Combining independent probabilities using event trees
eg: a transfer spill for liquified natural gas
transfer
spill
cause?
gap in
connector
connection
failure
full bore
rupture?
Y
N
operator
injured?
instant
reaction?
shut-down
effective?
result
20 m full bore
2 m full bore
5 m full bore
10 m full bore
Y
N
Y
N
Y
N
Risk Estimation
Combining independent probabilities using event trees
eg: a transfer spill for liquified natural gas
transfer
spill
cause?
gap in
connector
connection
failure
full bore
rupture?
Y
N
operator
injured?
instant
reaction?
shut-down
effective?
result
20 m full bore
2 m full bore
5 m full bore
10 m full bore
Y
N
Y
N
Y
N
0.06
probability
0.94
0.9
0.1
0.05
0.95
0.9
0.1
0.9
0.1
Risk Estimation
Combining independent probabilities using event trees
eg: a transfer spill for liquified natural gas
transfer
spill
cause?
gap in
connector
connection
failure
full bore
rupture?
Y
N
operator
injured?
instant
reaction?
shut-down
effective?
result
20 m full bore
2 m full bore
5 m full bore
10 m full bore
Y
N
Y
N
Y
N
0.06
probability
0.94
0.005
0.072
0.008
0.009
0.1
0.9
0.05
0.95
0.9
0.1
0.9
0.1
0.1
0.05
0.94
Risk Estimation
transfer
spill
gap in
connector
connection
failure
Y
N
Y
N
Y
N
10 m full bore
2 m full bore
5 m full bore
20 m full bore
2 m full bore
5 m full bore
10 m full bore
20 m leak
2 m leak
5 m leak
10 m leak
Y
N
Y
N
Y
N
Y
N
Y
N
Y
N
Combining independent probabilities using event trees
eg: a transfer spill for liquified natural gas
cause? full bore
rupture?
operator
injured?
instant
reaction?
shut-down
effective?
result
0.06
0.005
0.072
0.008
0.009
0.09
0.95
0.9
0.1
0.9
0.1
0.01
0.05
0.94
0.05
0.95
0.03
0.97
0.5
0.5
0.5
0.5
0.9
0.1
0.003
0.029
0.029
0.025
0.369
0.041
0.41
probability
Risk Estimation
FREQUENCY
OF HARM
frequency of
events with n
or more deaths
per year
logarithmic
MAGNITUDE OF HARM number of deaths, n (logarithmic)
Some typical frequency – magnitude curves for technological risks
aviation fire
dam failure
liquified natural gas
(theoretical)
all nuclear power
(pre Chernobyl
theoretical)
railways
10 1
10 0
10 -1
10 -2
10 -3
10 -4
10 -5
10 -6
10 -7
10 1 10 2 10 3 10 4 10 5
all nuclear power
(post Chernobyl empirical)
Risk Estimation
PROBABILITY
OF HARM
MAGNITUDE OF HARM
Tolerability Model as adapted by German Risk Council
1
0
0
PROHIBITED AREA
• ocean circulation flip
• nuclear winter
NORMAL AREA
• electric power
• municipal waste
TRANSITION AREA
• dams
• hazardous waste
Risk Estimation
PROBABILITY
OF HARM
MAGNITUDE OF HARM
Tolerability Model as adapted by German Risk Council
1
0
0
aim of risk
management
Risk Estimation
(2) as low as reasonably practicable (ALARP)
PROBABILITY
OF HARM
MAGNITUDE
OF HARM
KEY REGULATORY CONCEPTS
Probability – magnitude curves in risk management
(3) as low as reasonably achieveable (ALARA) / best practicable means (BPM)
(4) best available technology (BAT)
(5) best available technology not entailing excessive costs (BATNEEC)
existing technology (1)
(2)
(3)
(3)
(4)
(5)
Detailed Stages in Risk Analysis
HAZARD
IDENTIFICATION
RISK EVALUATION
RISK
MANAGEMENT
RISK
COMMUNICATION
INSTRUMENTS
AND MEASURES
PUBLIC AND
STAKEHOLDERS
‘OBJECTIVE’
SCIENCE
RISK
ESTIMATION
SUBJECTIVE
POLICY
CONSIDERATIONS
EXPOSURE
ASSESSMENT
RISK
ASSESSMENT
HAZARD
IDENTIFICATION
HAZARD
CHARACTERISATION
RISK EVALUATION
Risk Evaluation
Risk Aversion
PROBABILITY
+
NET BENEFIT
Technology B
Technology A
mean for a
= mean for b
If „risk neutral‟ – then indifferent over Options A and B
If „risk averse‟ – then prefer Option A
If „risk seeking‟ – then prefer Option B
-
Risk Evaluation
Risk Expectations
PROBABILITY
0.30
0.25
0.20
-50 0 10 50 100
NET BENEFIT (million £)
Expected net benefit = sum of possibilities, weighted by probabilities
Risk Evaluation
Risk Expectations
PROBABILITY
0.30
0.25
0.20
-50 0 10 50 100
NET BENEFIT (million £)
Expected net benefit = (0.2x100) + (0.25x50) + (0.3x10) + (0.25x-50)
Risk Evaluation
Risk Expectations
PROBABILITY
0.30
0.25
0.20
-50 0 10 50 100
NET BENEFIT (million £)
Expected net benefit = 20 + 12.5 + 3 - 12.5 million £
= 23 million £
Risk Evaluation
Risk Expectations
PROBABILITY
0.30
0.25
0.20
-50 0 10 50 100
NET BENEFIT (million £)
But maybe it‟s more important to avoid expectation of losing £ 50 million
than to face same expectation of gaining £ 50 million ?
Risk Evaluation
Utility Theory
UTILITY
NET BENEFIT, eg: £
or: money
security
wellbeing
1 / harm
Bermoulli realised that incremental satisfaction
depends on prior endowment
Risk Evaluation
Utility Theory
UTILITY
NET BENEFIT, eg: £
or: money
security
wellbeing
1 / harm
Bermoulli realised that incremental satisfaction
depends on prior endowment
£
Risk Evaluation
Utility Theory
UTILITY
NET BENEFIT, eg: £
or: money
security
wellbeing
1 / harm
Bermoulli realised that incremental satisfaction
depends on prior endowment
£
£
U1
U2
£ = £
but
U1 > U2
Risk Evaluation
Expected Utility
UTILITY
NET BENEFIT, eg: £
or: money
security
wellbeing
1 / harm
explains risk aversion
eg: a loss can be valued higher than the same gain
+ 50 M£
- 50 M£
Risk Evaluation
Expected Utility
UTILITY
NET BENEFIT, eg: £
or: money
security
wellbeing
1 / harm
depends on shape of utility curve
this is subjective and specific to individual decision
+ 50 M£
- 50 M£
Risk Evaluation
Expected Utility Theory is a powerful framework for risk evaluation
- articulates probability with magnitude components of risk
- applies in principle to any form of risk
- provides systematic means to transparency and accountability
- allows risk to be related to monetary value
- permits some account to be taken of individual subjectivity
- accounts for risk and loss aversion
- consistent with framework of probabilistic risk assessment
But raises a number of questions:
- is utility theory normative or descriptive – returned to in lecture 4
- how robust is it in theory and practice – returned to in lecture 3
- where do the probabilities come from – last part of this lecture
What is a Probability?
WHAT IS PROBABILITY OF THROWING A SIX ON A FAIR DIE?
- empirical record for past throws of same die?
- theoretical possibilities from six faces?
good where system is familiar and well-understood, like arches or levers
WHAT IS PROBABILITY THAT EARTH IS HIT BY 10 Mt METEORITE?
- empirical geological record for past meteorite strikes?
- theoretical possibilities from orbits of known astronomical objects?
good where system is simple, closed, stable, deterministic
ENCOURAGES OPTIMISTIC, MECHANISTIC IDEA OF RISK
What is a Probability?
THOUGHT EXPERIMENT: HOW WOULD A RISK METER WORK?
WHAT IS PROBABILITY THAT I WILL BE HIT BY A CAR TODAY?
- world statistics?
- statistics for people of my age?
national statistics?
gender? location?
- what about personal characteristics? personality? eyesight?
- what about immediate circumstances? mood? distractions?
- do this for each passing motorist?
familiar, well understood, but complex, open, dynamic, indeterminate
What is a Probability?
WHAT IS PROBABILITY OF A NUCLEAR CORE MELT?
- highly complex, with short statistical record
thousands of components, few instances, short experience
- extreme physical conditions, some incompletely understood
temperatures, pressures, radiation
- nonlinear dynamics and indeterminacies
emergent properties, systems not independent
- open system: sensitive to external conditions
grid failures, air crashes, floods, earthquakes
- sensitive to human factors
design flaws, regulatory failure, errors of omission, ingenuity
- reflexive system: risk open to deliberate intervention informed by risk
errors of commission, sabotage, terrorism, war
Brown‟s Ferry (1975); Three Mile Island (1979); Chernobyl (1986)
CONCEPT OF PROBABILITY BEGINS TO LOOK LESS STRAIGHTFORWARD
Probability and Information
CLASS EXPERIMENT
A conjuror hides 1 red and 2 blue balls
Asks a volunteer to bet: “under which of 3 cups is the red ball hidden?”
? ? ?
Probability and Information
WHAT IS THE PROBABILITY OF CHOOSING THE RED BALL FIRST?
volunteer guesses p = 1/ 3 for each cup
? ? ?
Probability and Information
WHAT IS THE PROBABILITY OF CHOOSING THE RED BALL FIRST?
volunteer guesses p = 1/ 3 for each cup
1/ 3 1/ 3
1/ 3
Probability and Information
WHAT IS THE PROBABILITY OF CHOOSING THE RED BALL FIRST?
volunteer chooses a cup and bets
1/ 3 1/ 3
1/ 3
Probability and Information
There are now 2 cups, one hiding a red ball, one hiding a blue ball
WHAT PROBABILITY OF HAVING CHOSEN THE RED BALL NOW?
WHAT IS THE PROBABILITY OF CHOOSING THE RED BALL FIRST?
then conjuror turns one cup to reveal a blue ball
Probability and Information
WHAT PROBABILITY OF HAVING CHOSEN THE RED BALL NOW?
Most believe that probability of having chosen the red ball is now 1/ 2
1/ 2 1/ 2
Probability and Information
WHAT IS THE PROBABILITY OF HAVING CHOSEN RED BALL NOW?
BUT: the probability is really still 1/ 3
1/ 3 2/ 3
So probability of ball lying under other unturned cup must now be 2/ 3
A SMART VOLUNTEER WOULD SHIFT THE BET TO THE OTHER CUP !
Probability and Information
WHAT IS THE PROBABILITY OF HAVING CHOSEN RED BALL NOW?
BUT: the probability is really still 1/ 3
1/ 3 2/ 3
PROBABILITY DEPENDS ON INFORMATION,
INFORMATION IS SUBJECTIVE
Was the conjurors choice of cup to turn really independent ?
What new information did the conjuror actually give ?
Conditional Probabilities
Thomas Bayes explored relationship between probability and information
Question:
- the chance (on average) of contracting a rare disease is 1/ 1000
- you get a positive in a 95% effective test for this disease – 1/ 20
- given a positive test result, what chance that you have the disease?
Naïve answer is the same as the failure rate for the test result: 95%…
… but this would be wrong!
Effectiveness of test is about probability of correct diagnosis, given illness
You are interested in probability of illness given positive diagnosis!
Conditional Probabilities
BAYES THEOREM
P(state given info) = P(state) x P(info given state) .
sum over states: [P(state) x P(info given state)]
Here: P(ill | +) = P(ill) . P(+ | ill)
[P(ill) . P(+ | ill)] + [P(not ill) . P(+ | not ill)] where:
prior probability of disease = P(ill) = 1/ 1000
prior probability of no disease = P(not ill) = 999/ 1000
probability of true positive result = P(+ | ill) = 19/ 20
probability of false positive result = P(+ | not ill) = 1/ 20
For this example, probability of having disease is 1/ 54 (~2 %)
Subjective Probabilities
Bayesian probabilities address some aspects of information and updating
- how do we decide what counts as relevant information?
But serious practical questions still remain:
- allow use of elegant and highly precise risk assessment techniques
- provide a basis for incorporating subjective expert judgements
- how do we weight different judgements?
- what about situations where there is no information?
- form basis for major philosophical debates in probability theory
Next week:
Look at practical and theoretical limits to utility and probability theory,
and how to address uncertainty, ambiguity and ignorance …
Recap of Lecture Theme 2
Risk analysis is the established basis for managing technological risk
- systematic process: hazard identification and characterisation,
exposure assessment, then risk estimation and evaluation
- can be based on models or evidence and tackle diverse forms of risk
- can account for links between probability and information
- offers elegance, precision, clarity and seductive sense of confidence
- can address subjective judgements in valuing benefits and harm
- based on distinctions: hazard, probability, magnitudes and risk
But leave a number of key practical and theoretical loose ends…
- can handle complex hazards and technological systems
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