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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”
Nov 2004 Joonas Hokkanen 2
DEVELOPMENT OF A RISK REDUCTION STRATEGY. AN EXAMPLE OF THE PROCEDURE UNDER THE EU ‘EXISTING SUBSTANCES REGULATION’.
Nov 2004 Joonas Hokkanen 3
The assessment of the risk reduction measures leads to the typical multicriteria problem
Nov 2004 Joonas Hokkanen 4
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
Nov 2004 Joonas Hokkanen 5
Traditional MCDA-approach
Decisionmodelu(x,w)
DMs’valuations
w
Criteriameasures
xBest solution
?
Nov 2004 Joonas Hokkanen 6
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
Nov 2004 Joonas Hokkanen 7
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
Nov 2004 Joonas Hokkanen 8
SMAA - approach
Decisionmodelu(x,w)
Favorablevaluations
?
Criteriameasures
xProspectivesolution
Nov 2004 Joonas Hokkanen 9
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
Nov 2004 Joonas Hokkanen 10
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
Nov 2004 Joonas Hokkanen 11
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
Nov 2004 Joonas Hokkanen 12
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
Nov 2004 Joonas Hokkanen 13
THE LOGICAL FLOW FOR APPLYING DIFFERENT PROCEDURES AND METHODS
Nov 2004 Joonas Hokkanen 14
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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