2: conventional risk analysis - university of sussexusers.sussex.ac.uk/~prfh0/risk theme 2... · 2:...

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


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

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


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


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


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



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



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



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



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)


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


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


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


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



£

Risk Evaluation

Utility Theory

UTILITY

NET BENEFIT, eg: £

or: money

security

wellbeing

1 / harm



£

£

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


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 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?”

? ? ?


WHAT IS THE PROBABILITY OF CHOOSING THE RED BALL FIRST?

volunteer guesses p = 1/ 3 for each cup

? ? ?



volunteer guesses p = 1/ 3 for each cup

1/ 3 1/ 3

1/ 3



volunteer chooses a cup and bets

1/ 3 1/ 3

1/ 3


There are now 2 cups, one hiding a red ball, one hiding a blue ball

WHAT PROBABILITY OF HAVING CHOSEN THE RED BALL NOW?


then conjuror turns one cup to reveal a blue ball


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


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 !


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

2: conventional risk analysis - university of sussexusers.sussex.ac.uk/~prfh0/risk theme 2... · 2:...

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