Nov 2004 Joonas Hokkanen 1

Dr. Joonas HokkanenConsulting Engineers Paavo Ristola Ltd

Finland

Presentation of the EU study (1997)

“THE USE OF DECISION-AID METHODS IN THE ASSESSMENT OF RISK REDUCTION MEASURES

IN THE CONTROL OF CHEMICALS”

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DEVELOPMENT OF A RISK REDUCTION STRATEGY. AN EXAMPLE OF THE PROCEDURE UNDER THE EU ‘EXISTING SUBSTANCES REGULATION’.

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The assessment of the risk reduction measures leads to the typical multicriteria problem

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THE FIVE TYPICAL PROBLEMS IN THE DECISION MAKING PROCESS

a

b

c d

e

f

g

ha

bc

d

ef

g

A. Choice problematic; helpchoose a "best" action

a

b

c d

e

f

g

ha

b

c

d

ef

g

B. Sorting problematic; helpsort actions to the feasibleones and not feasible ones.

a

b

c d

e

f

g

h

C. Ranking problematic without theintensity between the alternatives

a b

c

d

e

f g h

a

b

c d

e

f

g

h

D. Ranking problematic with theintensity between the alternatives.

a b d

e

f g h

c

E. Description problematic; Describing the variety ofdifferentvaluations (weight combinations) that support the choice ofthatalternative

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Traditional MCDA-approach

Decisionmodelu(x,w)

DMs’valuations

w

Criteriameasures

xBest solution

?

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CRITICAL INFORMATION NEEDED FOR THE APPLICATION OF DIFFERENT PROCEDURES AND METHODS FOR A TRANSPARENT AND CONSISTENT DECISION

Class Subclass Critical information

Disaggregativeprocedures

Procedures with no pre ference or criteria level information

Normalisation techniqueOrdinal ranking

Procedures with some criteria level information

Standard levelsComparison orderCriteria and alternative removalMonetary valuationTrade-offs

Procedures based on ordinal preference inform ation

Ranking orderCriteria and alternative removal

Aggregative methods Methods based on utility theory Value functionsUtility valuesWeightsPairwise comparison matrices

Monetary transformation methods Transformation procedure

Scoring techniques Normalisation techniqueWeights

Outranking methods WeightsPreference thresholdsIndifference thresholdsVeto thresholdsRanking coefficients

Continuous mathematical programming

Reference pointsConstraints used

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CONNECTION OF THE PLANNING PROCESS AND ALTERNATIVE SUBSTITUTION ASSESSMENT

Fundamental steps of the Planning Process

Alternative Assessment Decision Aid tools

Definition of objectives Definition of objectives Expert judgment, Group Participation, Interviews etc

Generation of alternatives Defining the alternative plans Expert judgment, Group Participation, Interviews etc

Formulation of criteria / measures of effectiveness

Defining the impact of the alternative plans

Expert judgement, Group Participation, Interviews etc

Evaluation of alternatives Evaluation of alternatives Expert judgment, Group Participation, Interviews etc

Comparison of the alternatives /Conflict solving

Comparing the alternative plans MCDA Disaggregative procedures, partially and totally aggregative methods etc

Planning how to reduce the potential nuisances and how to monitor the effects.

Selection of preferred alternative MCDA Disaggregative procedures, partially and totally aggregative methods etc

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SMAA - approach

Decisionmodelu(x,w)

Favorablevaluations

?

Criteriameasures

xProspectivesolution

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SMAA Weight Space Analysis

• Identifies the favorable weights Wi that make alternative i the preferred one

• Acceptability index ai = the expected volume of Wi

• Central weight wic = centroid of Wi

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Rank r acceptability indices

• Rank acceptability indices are similarly computed for each rank r= 1,…,m

• The resulting acceptability profile can be plotted and used for identifying compromise alternatives

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Comparison between the aggregative multicriteria methods.

Feature Ordinal

ranking of the

criteria

Cost-benefit

analysis

Utility based methods Outranking methods Methods based on mathematical optimisation

Preference modelling Ranking order of each alternative for each criterion

Monetary transformation Utility function. Threshold model. e.g. aspiration levels

Uncertainty modelling Uses threshold model. Uses probability distributions.

Uses probability distributions

Uses threshold model. Uses probability distributions.

Compensation Fully compensative. Fully compensative. Fully or partially compensative.

Fully or partially compensative.

Totally compensative.

Transformation of the basic data

Not used The decision makers must accept a common type for the monetarisation.

The decision makers must accept a common type for the utility function.

Not used. The decision maker must accept a normalisation of basic data.

Scales and scaling No scaling necessary The decision makers must accept the monetary scaling.

The decision makers must accept the utility scaling.

No scaling is necessary, the scales are to be considered when determining the threshold values

No scaling is necessary, the scales are to be considered when determining the aspiration levels.

Weights Represent the relative importance of the criteria, can be used to determine trade-off coefficients.

Represent the willingness to pay the cost.

Represent the relative importance of the criteria, can be used to determine trade-off coefficients.

Represent votes given to the importance of a certain criterion, cannot be used to compute the trade-off coefficients.

Represents the reference points

Ranking To choose the ”best” one Complete ranking of the alternatives

Complete ranking of the alternatives

Partial ranking i.e. the incomparability is accepted.

To choose the ”best” one

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INFORMATION USED WHEN PREPARING THE DECISION

Table 3: Information used with different procedures and methods No Preference information Preference information is used

and the type of the problem which can be solved. used Criteria values Solving the

have to be problems (see

transformed to figure 3)

equal units

Des

crop

tive

eval

uatio

n ta

ble

Red

uced

Eva

luat

ion

Tabl

e

Dom

inan

ce r

ules

Sta

ndar

d le

vel

Ran

king

ord

er o

n so

me

crite

rion

Ord

inal

ran

king

of

the

crite

ria

Car

dina

l wei

ghts

of

the

crite

ria

Nor

mal

izat

ion

Mon

etar

y tr

ansf

orm

atio

n

Util

ity tr

ansf

orm

atio

n

No

tran

sfor

mat

ion

need

ed w

ith c

rite

ria

valu

es

Cho

ose

or s

ort t

he "

best

" al

tern

ativ

e/s

Ran

k th

e al

tern

ativ

es

Des

crib

tion

prob

lem

Procedures with no preference or criteria level information

Dominance analysis x x x x

Positional analysis x x x x x

Procedures with criteria level information

Standard level analysis x x x x x

Risk/Benefit analysis x x x x x

Procedures based on ordinal preference information

Lexicographic analysis x x x x x x

Elimination by aspects x x x x x x

Aggregative methods based on ordinal ranking of the criteria

Agreed criteria x x x x x

Individual x x x x x

Aggregative methods based on cardinal prefernce information

Simple additive weighting method x x x x x x x x

Cost/Benefit analysis x x x x x x x x

Simple Multiattribute Rating x x x x x x x x

Analytic Hierrachy Process x x x x x x x x

Outranking methods x x x x x x x x

Mathematical optimization

e.g. methods based on aspiration level x x x x x x x x

Aggregative method for solving the weights making each alternative the best one

Stochastic MulttiaAttribute Acceptability Analysis, SMAA x x x x x x

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THE LOGICAL FLOW FOR APPLYING DIFFERENT PROCEDURES AND METHODS

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1. It is open and explicit 2. The choice of objectives and criteria that any decision making

group may make are open to analysis and to change if they felt to be appropriate

3. Scores and weights, when used, are also explicit and are developed according to established techniques

4. Performance measurement can be sub-contracted to experts, so need not necessarily be left in the hands of the decision making body itself,

5. It can provide an important means of communication, within the decision making body and sometimes, later between that body and wider community, and

6. Scores and weights are used, it provides an audit trail.

MCDA (Multi Criteria Decision Aid) has many advantages:

Nov 2004 Joonas Hokkanen 15

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