Decision Making in Small Animal Oncology Decision Making ...
Multi-Criteria Decision Analysis - ETH Z · Mindful decision-making Weber, E.U. & Johnson E.J....
Transcript of Multi-Criteria Decision Analysis - ETH Z · Mindful decision-making Weber, E.U. & Johnson E.J....
Eawag: Swiss Federal Institute of Aquatic Science and Technology
Multi-Criteria Decision Analysis
Judit Lienert
Lecture: Advanced Environmental AssessmentsStefanie Hellweg; Rolf Frischknecht / IfU – Ökologisches Systemdesign
20.10.2016, ETH Zürich Hönggerberg
Who am I?
First career in nursing
PhD Univ. Zürich: “Population biology of wetland plants in fragmented landscapes”
15 years@ Eawag: 2001 – 2007 as co-project manager of trans-disciplinary project Novaquatis
Since 2008: Focus on MCDA; lecture at ETHZ D-USYS (3 CP)
Cluster leader “Decision analysis” in dept. “Environmental Social Sciences” (ESS)
Field work, wetland, 1998(Toggenburg & Einsiedeln)
http://www.eawag.ch/de/abteilung/ess/empirischer-fokus/entscheidungsanalyse/
Content
Motivation
Some examples from Eawag
MCDA – The method
…Objectives hierarchy
…Decision alternatives
…Predictions
…Preference elicitation (weights)
…MCDA model
Exercise (next Tuesday)
MotivationWhy are environmental decisions difficult?
???
Motivation
Large number of alternatives
Potentially conflicting objectives
Large uncertainty* of data* of consequences of alternatives* of the future
Interdisciplinary nature of problem
Many stakeholders involved
...
Why are environmental decisions difficult?
Motivation
Definition"... decision analysis is a formalization of common sense for
decision problems which are too complex for informal use of common sense."
"... a philosophy, articulated by a set of logical axioms and a methodology and collection of systematic procedures (...) for responsibly analyzing the complexities inherent in decision problems."
Ralph L. Keeney (1982) Oper. Res. 30: 803-838
Goal of decision analysisSupport the decision process to make better decisions
Motivation
Decision support does not make the decision for the decision maker
What is a good decision? Motivation
Careful consideration of objectives
Use of scientific information about consequences of all alternatives
Participatory and transparent
Lucky decision
Does not always lead to optimal consequences (limited knowledge, chance)
Some examples from Eawag
River rehabilitation and management
Strong impacts on river eco-systems in Europe; e.g. to gain land / flood protection
Goal of river rehabilitation: re-establish parts of the ecosystems
Goals of river management:prioritization across landscape
Difficult decisions:* expensive measures* uncertain outcomes* difficult quantification of success* many stakeholders* conflicting objectives
Examples
MCDA: Peter Reichert, Nele Schuwirth et al. (Siam/ Eawag)
Pharmaceuticals in hospital wastewaterMany pharmaceuticals in water bodies
Risks? Many unknowns!
Ecotoxicological risk potential (RQ) established for some substances (ethinylestradiol, diclofenac, -blockers)
"No risk" e.g. from X-ray contrast agents excreted in large amounts
Bafu has decided to upgrade WWTP
Additionally: point source measures?
MCDA-project with cant. Hospital Baden, psych. clinic Hard/ Embrach; interviews with 2 x 10 stakeholders
Examples
Sustainable Water Infrastructure Planning (SWIP)(National Research Programme NRP 61)
Water supply & wastewater infrastructure is of core importance and expensive Infrastructure is aging
(25% needs rehabilitation in next years, ...)Can infrastructure cope w. new demands?
(micropollutants, climate change, …) Existing planning tools are not planning
into far future and are not participatory
Infrastructure planning is demanding
Provide framework and tools for long-term water infrastructure planning that includes uncertainty, non-technical objectives, and stakeholders – Combination of engineering modeling with MCDA and scenario planning
Examples
Practical decision analysis for value-focused planning of wastewater infrastructures(new PhD-project Fridolin Haag)
Examples
http://www.eawag.ch/en/department/ess/projekte/decision-analysis-for-wastewater-infrastructures/
Paper 1: Predictingwastewater impacts of
social relevance byintegrating knowledge with
a Bayesian network
Paper 2: Eliciting aggregation schemes for environmental decisions
Paper 3: Influence ofobjectives hierarchy
structuring on preferencesand their elicitation
Paper 4: A MCDA framework for planning of wastewater
infrastructures
12
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Serious games as an element of MCDARecent ideas by postdoc Alice Aubert
Plass, J.L.; Homer, B.D. & Kinzer, C.K. (2015) Foundations of game-based learning, Educ. Psycho.
Mindful decision-making
Weber, E.U. & Johnson E.J. (2009) Mindfuljudgement & decision-making, Annu. Rev. Psychol.
EMOTIONS
ATTENTION
LEARNING
Game about wastewater problems:* increases learning* is fun* gives us people’s preferences
MCDA – the Method
How to compare apples with oranges ...
MCDA – The methodConcept of Multi-Criteria Decision Analysis (MCDA)
With help of a multi-attributive value function
How to deal with a whole fruit basket?!
Method
Objectiveprediction of
outcome of eachalternative
Subjectiveimportance of goals
and preferencesfor outcomes
Integrate objective & subjective information Rank each alternative for each stakeholder
1. Define decision problem, framingCarry out stakeholder analysis
2. Identify objectives and attributesConstruct objectives hierarchy
4. Predict outcomes ofeach alternative
6. Integrate steps 4 & 5, rank alternativesAnalyze results, carry out sensitivity analysis
7. Discuss results with stakeholdersFind consensus alternatives
5. Elicit and quantify stakeholderpreferences for outcomes
3. Identify alternatives
MCDA – The method Method
1. Define decision problem, framingCarry out stakeholder analysis
2. Identify objectives and attributesConstruct objectives hierarchy
4. Predict outcomes ofeach alternative
6. Integrate steps 4 & 5, rank alternativesAnalyze results, carry out sensitivity analysis
7. Discuss results with stakeholdersFind consensus alternatives
5. Elicit and quantify stakeholderpreferences for outcomes
3. Identify alternatives
MCDA – The method Method
What exactly?
Aim of the decision making process?
What to include? What to exclude? System boundaries?
Who is affected by the decision? Who makes the decision? What are their interests? Whom to include in decision process?
Where do you get the information? (documents, regulations, experts, models, …)
…
MCDA – The method Method
Who decides?Who is affected?
1. Define decision problem, framingCarry out stakeholder analysis
2. Identify objectives and attributesConstruct objectives hierarchy
4. Predict outcomes ofeach alternative
6. Integrate steps 4 & 5, rank alternativesAnalyze results, carry out sensitivity analysis
7. Discuss results with stakeholdersFind consensus alternatives
5. Elicit and quantify stakeholderpreferences for outcomes
3. Identify alternatives
MCDA – The method Method
Focus on fundamental objectives: this is important «because» it is important
Avoid means objectives: important to achieve a more fundamental objective
Objectives should not contain «means-ends relationships» (objective A ↔ B)
Further requirements: Complete, non-redundant, measurable, preferentially independent, simple
Each fundamental objective on lowest level of hierarchy should be measurable with an attribute
MCDA – Objectives hierarchy Objectives
A complex / difficult decision problem?
MCDA – Objectives hierarchy Objectives
?Ageing infrastructuresNot fully usedHigh costsLow flexibilityAdministrative continuity problems
Decision-making for transition from central to novel (decentral) wastewater infrastructures
Source: Wastewater treatment plant Vienna Source: own pictures IWAS UA
Source: WSB Clean
Source: Larsen et al. (2016)
(new PhD-project Philipp Beutler)
Alternative Systems?
Source: BIOS
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A complex / difficult decision problem?
What do you have to know to compare different wastewater system alternatives with each other?
What is really important in this decision problem?
MCDA – Objectives hierarchy Objectives
MCDA – Objectives hierarchy Objectives
Good water supply and wastewater disposal infrastructure(today and in future)
Intergener-ational equity
Low future rehabilitation
burden (2050)
Flexible system
adaptation
Protection of water / resources
Good state of surface water (chemic, hydrol.)
Good state of ground water (chem., regime)
Efficient use of resources
(phosp., energy)
Good supply with water
Drinking water (quality & reliability)
Household water (quality & reliability)
Firefighting: (quantity & reliability)
Safe waste-water disposal
Good hygiene (no illness if (in-) direct contact)
High reliability of drainage
(failure, floods)
High social acceptance
High water resource autonomy
High quality of managem. & operations
High co-determination
of citizens
Low time and area demand for end users
Low unnecessary road works
Low costs
Low annual costs
(CHF/person/yr)
Low cost increase
In total 40 attributes measure how well objectives are achieved
Lienert, J., Scholten, L., Egger, C., Maurer, M. (2015) Structured decision-making for sustainable water infrastructure planning and four future scenarios. EURO J. on Decision Processes 3(1-2): 107-140
Practical decision analysis for value-focused planning of wastewater infrastructures(PhD-project Fridolin Haag)
Examples
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Paper 3: Influence ofobjectives hierarchy
structuring on preferencesand their elicitation
How can objectives be most effectively and simply structured?
How can we best build better-manageable (smaller) hierarchies?
How do different objectives hierarchies influence the stakeholder’s preferences?
… and the elicitation of the preferences?
1. Define decision problem, framingCarry out stakeholder analysis
2. Identify objectives and attributesConstruct objectives hierarchy
4. Predict outcomes ofeach alternative
6. Integrate steps 4 & 5, rank alternativesAnalyze results, carry out sensitivity analysis
7. Discuss results with stakeholdersFind consensus alternatives
5. Elicit and quantify stakeholderpreferences for outcomes
3. Identify alternatives
The decision support process
How can objectives be achieved?Define alternatives (= decision options / strategies)
High tendency to stick to status quo Creativity!
Use different creativity techniques (cards, metaphors, analogies, Osborns 73-list, devils advocat, …)
Or use systematic techniques (e.g. strategy generation table; successfully used by NASA, for business or environmental problems, …)
MCDA – Decision alternatives Alternatives
MCDA – Decision alternatives Alternatives
Decision options / strategies
How can objectives be achieved?
What is the opposite of this?What would your grandmother suggest?What would Mr. Spock do?What would be the most expensive option?What would be the “softest” option?
«Strategy generation table» 17 factors, each with 4 – 8 Specifications Stakeholder workshop:
construct 10+ decision alternatives
Sustainable water infrastructure planning (SWIP)Alternatives of water supply / WW system?
Organiza-tional form
Spatial extent
Strategy, finances,
rehabilitation
System and Technology
Gemeinde
Coop-eration
Rehab-ilitation
System Tech-nology
a strong high central high-tech
b strong none central high-tech
c none none decentr. low-tech
d none high decentr. high-tech
Lisa Scholten
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Small wastewatertreatment plants
Fit for the future… with alternative wastewater systems?
• Alternative concepts andtechnologies already exist
• Possible advantages compared to central system (rural, remote areas):* lower costs?* shorter life cycles flexibility* less network infrastructure(sewer pipes) flexibility
* larger set of options* individual adaptations
Spurce: WSB Clean
Source: Larsen et al. (2016)
Source: Holzapfel & KonsortenSource: BIOS
Reedbeds, etc.
NoMix (sourceseparation)
Philipp Beutler
30
1. Define decision problem, framingCarry out stakeholder analysis
2. Identify objectives and attributesConstruct objectives hierarchy
4. Predict outcomes ofeach alternative
6. Integrate steps 4 & 5, rank alternativesAnalyze results, carry out sensitivity analysis
7. Discuss results with stakeholdersFind consensus alternatives
5. Elicit and quantify stakeholderpreferences for outcomes
3. Identify alternatives
PredictionsMCDA – Predictions
Scientific predictions («objective» predictions, estimates)
Based on models, data from literature, expert estimates
Can be very detailed or coarse, depending on decision problem
Should contain uncertainty of prognosis / estimate
Daily business in LCA
For each alternative: How well are objectives achieved?
PredictionsMCDA - Predictions
Hospital wastewater-project: Prediction matrixKantonsspital Baden, KSB
MW = Mittelwerts = Standardabweichung (relativ ) / s (a) = Standardabweichung (absolut)Min = MinimumMax = Maximum
Nr. Kürzel Name MW s MW s MW s MW s (a) Min MW Max Min MW Max Min1 Status quo Status quo 0 0 3.2 20% 777 15% 100 0 0 0 0 0 0 0 0
26 Cb 26 Central (Keller), Biofilm-Vorbehandlung, keine Nachbehandlung 246'854 12.5% 1.7 20% 601 15% 8.0 4.0 0 0 0 0 0 0 028 Cb O3 28 Central (Keller), Biofilm-Vorbehandlung, Ozonierung (O3) 302'108 12.5% 1.7 20% 352 15% 3.1 1.5 0 0 0 0 0 0 030 Cb PAC 30 Central (Keller), Biofilm-Vorbehandlung, Pulveraktivkohle (PAC) 338'826 12.5% 0.8 20% 249 15% 3.5 1.8 0 0 0 0 0 0 032 Cb O3GAC 32 Central (Keller), Biofilm-Vorbehandlung, Ozonierung + Aktivkohle (O3 + GAC) 336'932 12.5% 0.8 20% 163 15% 3.0 1.5 0 0 0 0 0 0 033 Cb RO 33 Central (Keller), Biofilm-Vorbehandlung, Membran: Umkehr-Osmose (RO) 515'356 25% 0 20% 0 15% 3.0 1.5 0 0 0 0 0 0 034 C Vacuum 34 Vacuum-WCs, Sammlung und Abtransport, Kehrichtverbrennung 409'685 12.5% 0 20% 0 15% 8.0 4.0 0 0 0 0 0 0 242 NMX 42 NoMix, Urinale, andere Urinsammlung, Reaktor (SBR), keine Nachbehandlung 90'807 12.5% 3.0 20% 643 15% 100 0 1.0 2.4 3.4 3.3 20 33 244 NMX O3 44 NoMix etc., SBR-Vorbehandlung, Ozonierung (O3) 96'972 12.5% 3.0 20% 438 15% 100 0 1.0 2.4 3.4 3.3 20 33 246 NMX PAC 46 NoMix etc., SBR-Vorbehandlung, Pulveraktivkohle (PAC) 98'317 12.5% 2.9 20% 419 15% 100 0 1.0 2.4 3.4 3.3 20 33 248 NMX O3GAC 48 NoMix etc., SBR-Vorbehandlung, Ozonierung + Aktivkohle (O3 + GAC) 98'717 12.5% 2.9 20% 348 15% 100 0 1.0 2.4 3.4 3.3 20 33 249 NMX RO 49 NoMix etc., SBR-Vorbehandlung, Membran: Umkehr-Osmose (RO) 108'902 25% 2.9 20% 302 15% 100 0 1.0 2.4 3.4 3.3 20 33 250 NMX Inc 50 NoMix etc., Urin-Sammlung und Abtransport, Kehrichtverbrennung 148'806 12.5% 2.9 20% 302 15% 100 0 1.0 2.4 3.4 3.3 20 33 258 Ur 58 Urin wo sowieso gesammelt (Topf etc.), Reaktor (SBR), keine Nachbehandlung 15'026 12.5% 3.1 20% 723 15% 100 0 0 0 0 0 0 0 060 Ur O3 60 Urin sowieso, SBR-Vorbehandlung, Ozonierung (O3) 19'256 12.5% 3.1 20% 641 15% 100 0 0 0 0 0 0 0 062 Ur PAC 62 Urin sowieso, SBR-Vorbehandlung, Pulveraktivkohle (PAC) 20'278 12.5% 3.1 20% 634 15% 100 0 0 0 0 0 0 0 064 Ur O3GAC 64 Urin sowieso, SBR-Vorbehandlung, Ozonierung + Aktivkohle (O3 + GAC) 20'578 12.5% 3.1 20% 605 15% 100 0 0 0 0 0 0 0 065 Ur RO 65 Urin sowieso, SBR-Vorbehandlung, Membran: Umkehr-Osmose (RO) 25'321 25% 3.1 20% 587 15% 100 0 0 0 0 0 0 0 066 Ur Inc 66 Urin sowieso, Urin-Sammlung und Abtransport, Kehrichtverbrennung 21'965 12.5% 3.1 20% 587 15% 100 0 0 0 0 0 0 0 067 RdBg H 67 Urin mit Roadbag (Röntgen), nur stationäre Patienten (Hospital) 33'500 12.5% 3.2 20% 693 15% 100 0 0.3 1.0 1.5 0.1 0.4 0.7 1.368 RdBg HH 68 Urin mit Roadbag (Röntgen), stationäre (Hospital) und ambulante (Home) Pat. 94'500 12.5% 3.2 20% 519 15% 100 0 1.0 4.1 6.2 0.4 1.5 2.9 1.3
Schlechteste Denkbar schlechtest Alternative (alle Attribute im schlechtesten Zustand): 0 Punkte 1'500'000 10.0 1400 100 6 6 6 33 33 33 0Beste Denkbar beste Alternative (alle Attribute im besten Zustand): 100 Punkte 0 0 0 0 0 0 0 0 0 0 6
Geringes ökotoxikolo
g. Risiko-potenzial
Gpo
Medi
Geringer Aufwand für das Pflegepersonal
Geringer Aufwand für PatientInnen
Total Stunden / Tag
% unzufriedene PatientInnen
Geringe Menge
Medikamente im Abwasser
kg / Jahr nach Behandlung des
Abwassers
Geringe Menge Pathogene & AB-resist. Bakterien
% im Abwasser nach
Behandlung
AnzaBe
M
1. Niedrige Kosten
2. Gute Abwasser-Qualität 3. Gute Umsetzbarkeit
Niedrige jährliche Kosten
CHF/ Jahr Risiko-quotient
Boom Doom Qual.Life Status quo
25
50
75
100
0.00
0.25
0.50
0.75
1.00
adaptrehab
A1a
A1b A
2A
3A
4A
5A
6A
7A
8aA
8b A8c
A8d
A8e A8f A9
A1a
A1b A
2A
3A
4A
5A
6A
7A
8aA
8b A8c
A8d
A8e A8f A9
A1a
A1b A
2A
3A
4A
5A
6A
7A
8aA
8b A8c
A8d
A8e A8f A9
A1a
A1b A
2A
3A
4A
5A
6A
7A
8aA
8b A8c
A8d
A8e A8f A9
Alternative
Attr
ibut
e
Zheng, J., Egger, C., Lienert, J. (2016) A scenario-based MCDA framework for wastewater infrastructure planning under uncertainty. Journal of Environmental Management 183 (3): 895-908.
SWIP-project: Predictions and future scenariosHow robust are alternatives? Wastewater predictions (J. Zheng)
For each alternative: How well are objectives achieved?
PredictionsMCDA – Predictions wastewater example
Objective
Alternative
1. 2. 3. 4.
a. central, rehabilitate, high-tech
b. central,decay, “high-tech”
c. decentral,low tech (e.g. reed beds)
d. decentralhigh-tech (e.g. NoMix)
For each alternative: How well are objectives achieved?Estimates adapted from SWIP (Zheng et al., 2016)
PredictionsMCDA – Examples predictions wastewater
Objective
Alternative
1. Lowfuture rehabilitation burden(% rehab. demand)
2. High flexibility(% flexib.extension/ deconstruction)
3. Good chemical state (0-1: Modularstream assessm.)
4. Nutrientrecovery (% phos-phate)
5. Efficientenergy consumption (kWh/p/yr)
6. Few gastro-int. infections dir. cont. (% pop. inf. 1x/ y)
7. Low time demand end-user (hr/p/yr)
8. Low area demand (m2 on propertyend-user)
9. Low annualized costs (CHF/p/yr)
a. central, rehabilitate, high-tech
80% 35% 0.77 (good)
0 250 2% 0 0 863 (1.3% of income)
b. central,decay, “high-tech”
0% 50% 0.3 (unsatis-factory)
0 60 10% 0 0 76 (0.07% of
income)
c. decen-tral, low tech (e.g. reed beds)
20% 70% 0.5 (moder-
ate)
60% 20 20% 20 10 400
d. decen-tral high-tech (e.g. NoMix)
100% 90% 0.85(very
good)
90% 40 5% 10 4 800
For each alternative: How well are objectives achieved?
PredictionsMCDA – Predictions wastewater example
Objective
Alternative
1. High flexibility
2. Good chemical
state
3. Low time demand
4. Low costs
a. central, rehabilitate, high-tech
35% 0.77(good)
0 863 CHF/p/yr(1.3% of income)
b. central,decay, “high-tech”
50% 0.3(unsatis-factory)
0 76 CHF/p/yr(0.07% of income)
c. decentral,low tech (e.g. reed beds)
70% 0.5(moderate)
20 hrs/p/yr 400 CHF/p/yr
d. decentralhigh-tech (e.g. NoMix)
90% 0.85(very good)
10 hrs/p/yr 800 CHF/p/yr
Break
1. Define decision problem, framingCarry out stakeholder analysis
2. Identify objectives and attributesConstruct objectives hierarchy
4. Predict outcomes ofeach alternative
6. Integrate steps 4 & 5, rank alternativesAnalyze results, carry out sensitivity analysis
7. Discuss results with stakeholdersFind consensus alternatives
5. Elicit and quantify stakeholderpreferences for outcomes
3. Identify alternatives
MCDA – Preference elicitation
MCDA makes «subjective» gut feeling visible; it is included on equal footing to «objective» scientific data
Elicitation of preferences (interviews, in group decision workshops, online)
Value functions: translate bananas / apples / $$ / … to a neutral scale [0, 1]
Weights: trade-offs between objectives
… amongst other parameters
Always based on real values!CAVEAT – many known biases!
MCDA – Preference elicitation
How important? (who?)
What is important?
Never without context!!
NJET!!: “Environmentalprotection is more
important to me than high salary” what / how much exactly??
Preferences
value Quantifies the degree of fulfillment of each attribute Common unit between 0 and 1
by transforming level of an attribute to a value Single-attribute value functions
thus allow to compare attributes with different units Can have any shape Caveat! Attribute range!
Single-attribute value functions
0.5
Costs (Mio CHF)0.5 10
PreferencesMCDA – Preferences (value functions)
value
0.7
0.5Costs (Mio CHF)
10
Single-attribute value functions
PreferencesMCDA – Preferences (value functions)
0.28
value
0.5Costs (Mio CHF)
10
Single-attribute value functions
PreferencesMCDA – Preferences (value functions)
For each alternative: How well are objectives achieved?
PredictionsMCDA – Predictions wastewater example
Objective
Alternative
1. High flexibility
2. Good chemical
state
3. Low time demand
4. Low costs
a. central, rehabilitate, high-tech
35% 0.77(good)
0 863 CHF/p/yr(1.3% of income)
b. central,decay, “high-tech”
50% 0.3(unsatis-factory)
0 76 CHF/p/yr(0.07% of income)
c. decentral,low tech (e.g. reed beds)
70% 0.5(moderate)
20 hrs/p/yr 400 CHF/p/yr
d. decentralhigh-tech (e.g. NoMix)
90% 0.85(very good)
10 hrs/p/yr 800 CHF/p/yr
Single-attribute value functions, example High flexibility (%)
Preferences
0
0.25
0.5
0.75
1
35; 0
40; 0.25
45; 0.5
55; 0.75
90; 1
0
1
35; 0
90; 1
00.10.20.30.40.50.60.70.80.9
1
35 45 55 65 75 85Attribute (%)
Value function high flexibility (% flexibility of technical extension or deconstruction)
Linearcase
Value
0.28
50%(alternative
b. central, decay)
70%(alternative
c. decentral, low-tech)
0.64
MCDA – Preferences (value functions)
Single-attribute value functions, example High flexibility (%)
Preferences
0
0.25
0.5
0.75
1
35; 0
40; 0.25
45; 0.5
55; 0.75
90; 1
0
1
35; 0
90; 1
00.10.20.30.40.50.60.70.80.9
1
35 45 55 65 75 85Attribute (%)
Value function high flexibility (% flexibility of technical extension or deconstruction)
Linearcase
Value
0.62
50%(alternative
b. central, decay)
70%(alternative
c. decentral, low-tech)
0.86
MCDA – Preferences (value functions)
Convert attribute level to value [0, 1] with help of value function
Preferences
Objective
Alternative
1. High flexibility
2. Good chemical state
3. Low time demand
4. Low costs
a. central, rehabilitate, high-tech v1(a1) = …. v2(a2) = …. v3(a3) = …. v4(a4) = ….
b. central,decay, “high-tech” v1(b1) = …. v2(b2) = …. v3(b3) = …. v4(b4) = ….
c. decentral,low tech (e.g. reed beds) v1(c1) = …. v2(c2) = …. v3(c3) = …. v4(c4) = ….
d. decentralhigh-tech (e.g. NoMix) v1(d1) = …. v2(d2) = …. v3(d3) = …. v4(d4) = ….
MCDA – Preferences (value functions)
Preferences
Objective
Alternative
1. High flexibility
2. Good chemical state
3. Low time demand
4. Low costs
a. central, rehabilitate, high-tech
35%
v1(a1) = 0
0.77 (good)
v2(a2) = 0.86
0
v3(a3) = 1
863 CHF/p/yr
v4(a4) = 0
b. central,decay, “high-tech”
50%
v1(b1) = 0.28
0.3 (unsatisf.)
v2(b2) = 0
0
v3(b3) = 1
76 CHF/p/yr
v4(b4) = 1
c. decentral,low tech (e.g. reed beds)
70%
v1(c1) = 0.64
0.5 (moderate)
v2(c2) = 0.38
20 hrs/p/yr
v3(c3) = 0
400 CHF/p/yr
v4(c4) = 0.58
d. decentralhigh-tech (e.g. NoMix)
90%
v1(d1) = 1
0.85 (v. good)
v2(d2) = 1
10 hrs/p/yr
v3(d3) = 0.5
800 CHF/p/yr
v4(d4) = 0.08
Convert attribute level to value [0, 1] with help of value functionMCDA – Preferences (value functions)
High flexibility(35% – 90%)
Low time demand end-users(0 – 20 hours / person / per
year)
?Weights (scaling constants)MCDA – Preference elicitation Preferences
Flexibility: 0.25 Time demand: 0.75(∑ = 1)
High flexibility(35% – 90%)
Low time demand end-users(0 – 20 hours / person / per
year)
MCDA – Preference elicitation Preferences
SWING-method (“recipe”)1. Determine ranges of objectives2. Rank alternatives from best br to
worst a–
3. Allocate points: best br = 100 ptworst a– = 0 pt
4. Assign points to the remaining alternatives br such that the value differences are reflected
5. Calculate weights by normalizing the points: (convention: sum wr = 1)
6. Consistency checks
m
ii
rr
t
tw
1
Weights (scaling constants)MCDA – Preference elicitation Preferences
What are your personal weights for discussed decision problem?
1. Determine range of objectives2. Rank alternatives from best br to worst a–
3. Allocate points (best br = 100; worst a- = 0)4. Assign points to remaining alternatives br
5. Calculate weights by normalizing points
Your task (in class)
PreferencesMCDA – weight elicitation with SWING
m
ii
rr
t
tw
1
PreferencesMCDA – Weight elicitation with SWING
Objective
Alternative
1. High flexibility
2. Good chemical state
3. Low time demand
4. Low costs
a. central, rehabilitate, high-tech
35%
v1(a1) = 0
0.77 (good)
v2(a2) = 0.86
0
v3(a3) = 1
863 CHF/p/yr
v4(a4) = 0
b. central,decay, “high-tech”
50%
v1(b1) = 0.28
0.3 (unsatisf.)
v2(b2) = 0
0
v3(b3) = 1
76 CHF/p/yr
v4(b4) = 1
c. decentral,low tech (e.g. reed beds)
70%
v1(c1) = 0.64
0.5 (moderate)
v2(c2) = 0.38
20 hrs/p/yr
v3(c3) = 0
400 CHF/p/yr
v4(c4) = 0.58
d. decentralhigh-tech (e.g. NoMix)
90%
v1(d1) = 1
0.85 (v. good)
v2(d2) = 1
10 hrs/p/yr
v3(d3) = 0.5
800 CHF/p/yr
v4(d4) = 0.08
1. For each objective: Range? (= best / worst-possible case?) (Eisenführ et al., 2010, p. 139 ff.)
Alternative a = (objective flexibility, chemical state, time, costs)
Q: In alternative a all attributes are at their worst level. If you could move one to its best level, which would you choose?
2. Rank alternatives from best br to worst a–
High flexibility Good chem. state Low time demand
0.30 (unsatisf.) 20hrs/p/yr35%
90% 0.85 (very good) 0
PreferencesMCDA – weight elicitation with SWING
Low costs
76 CHF/p/yr
863 CHF/p/yr
DM: I would certainly move objective xw to it's highest level
(note: this is the preferred-alternative b1 and receives 100 pt.)
?
Q: Which attribute would you choose to move to its' best level as second option?
2. Rank alternatives from best br to worst a–
PreferencesMCDA – weight elicitation with SWING
DM: (think, think, ...) I suppose the objective xx
(note: this is alternative b2)
Q: Which attribute would you choose next to move to its' best level?
2. Rank alternatives from best br to worst a–
DM: (think, think, ...) I suppose the objective xy
(note: this is alternative b3)
PreferencesMCDA – weight elicitation with SWING
Alternative a = (objective flexibility, chemical state, time, costs)
High flexibility Good chem. state Low time demand
0.30 (unsatisf.) 20hrs/p/yr35%
90% 0.85 (very good) 0
Low costs
76 CHF/p/yr
863 CHF/p/yr
?
Attribute Obj?.... Obj?.... Obj?.... Obj?.... Alternative Points (tr)Rank
1. b1 100
2. b2
3. b3
4. b4
5. a 0
Q: The most-preferred alternative (b1) receives 100 points. Note: this is not the best-possible alternative. The worst one (a) has 0 points. Which points do you assign to b2, b3, and b4, between 0 and 100, that correctly reflect the differences?
3./4. Assign points: b1 = 100 / 1 = 0 / rest = value differences
PreferencesMCDA – Weight elicitation with SWING
?
?
?
DM: I think, b2 would receive xx points, b3, xy, and b4 maybe xz(Now normalize points so that weights sum up to 1)
What are your personal weights for discussed decision problem?
1. Determine range of objectives2. Rank alternatives from best br to worst a–
3. Allocate points (best br = 100; worst a- = 0)4. Assign points to remaining alternatives br
5. Calculate weights by normalizing points
Your task (in class)
PreferencesMCDA – weight elicitation with SWING
m
ii
rr
t
tw
1
Detailed single-attribute value functions are not needed (now), only extreme levels are compared value functions and weights can
be elicited in the same interview
Extreme levels may be unrealistic
Elicitation and calculation of the weights is fairly easy, but...
Requires much cognitive work from decision maker (DM) – DM is usually not aware of implications elicited weights may be wrong! consistency checks essential!
How much fruitequals
two eggplants?
Advantages and problems of SWING
PreferencesMCDA – Weight elicitation with SWING
Weights of Eawag and the public (N=314)
Equity Resources W-water Social Costs
Wei
ghts
(ave
rage
)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Example SWIP; weights from stakeholdersExample: weights main objectives, elicited three times independently
Weights of ten wastewater experts
Equity Resources W-water Social Costs
Wei
ghts
(ave
rage
)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Weights of ten water supply experts
Equity Resourc. Wat.Supp. Social Costs
Wei
ghts
(ave
rage
)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Ten water supply experts Ten wastewater experts General public, Eawag (N=314)
Protection ofwater & other resources
Good water supply
Safe waste-wat. disposal
High social acceptance
Low costs
Intergener-ational equity
Similar average preferences from ten interviews (local / cantonal / national stakeholders) and from public (survey) But large individual differences Question: do different preferences change the results (i.e. the
recommendations about best-performing alternative)?
Wei
ghts
(ave
rage
)
Lienert, J., Duygan, M., Zheng, J. (2016) Preference stability over time with multiple elicitation methods to support wastewater infrastructure decision-making. European Journal of Operational Research 253 (3): 746-760.
1. Define decision problem, framingCarry out stakeholder analysis
2. Identify objectives and attributesConstruct objectives hierarchy
4. Predict outcomes ofeach alternative
6. Integrate steps 4 & 5, rank alternativesAnalyze results, carry out sensitivity analysis
7. Discuss results with stakeholdersFind consensus alternatives
5. Elicit and quantify stakeholderpreferences for outcomes
3. Identify alternatives
MCDA – Model
How well are objectives achieved?
For each decision alternative:
Predictions = Level of each attribute (models, expert estimates, …)
Translation to neutral value [0, 1]
Calculation of total value of each decision alternative:* Weight of objective x achieved value* Sum over all objectives
(non-additive aggregation also possible)
Allows to rank all alternatives [0, 1]
MCDA – Model Model
MCDA – ModelAdditive aggregation: Multi-attribute value function = weighted sum of single-attribute value functions
Model
m
iiii avwav
1
v(a) = total value of alternative a= weighted sum of single values of
each attribute i of alternative aai = attribute level of attribute i for
alternative avi(ai) = value of attribute level of attribute i
for alternative awi = weighting factor of attribute i,
where wi = 1
Objective
Alternative
1. High flexibilityw1 = …
2. G. chem.statew2 = …
3. Low time demandw3 = …
4. Lowcostsw4 = …
Total value v of each alternative
a. central, rehabilitate, high-tech
0 0.86 1 0 v(a) = ….
b. central,decay, “high-tech”
0.28 0 1 1 v(b) = ….
c. decentral,low tech (e.g. reed beds)
0.64 0.38 0 0.58 v(c) = ….
d. decentralhigh-tech (e.g. NoMix)
1 1 0.5 0.08 v(d) = ….
MCDA – Model Model
)(...)()( 222111
1 mmm
m
iiii avwavwavwavwav
SWIP for wastewater infrastructure planning (Jun Zheng)
• A7: Decentral high-tech (e.g. NoMix)(our alternative d)
MCDA – Model / results Model
• A4: Decaying central infrastructure(our alternative b)
• A8d: Super central WWTP(similar to our alternative a)
Zheng, J., Egger, C., Lienert, J. (2016) A scenario-based MCDA framework for wastewater infrastructure planning under uncertainty. Journal of Environmental Management 183 (3): 895-908.
1. Define decision problem, framingCarry out stakeholder analysis
2. Identify objectives and attributesConstruct objectives hierarchy
4. Predict outcomes ofeach alternative
6. Integrate steps 4 & 5, rank alternativesAnalyze results, carry out sensitivity analysis
7. Discuss results with stakeholdersFind consensus alternatives
5. Elicit and quantify stakeholderpreferences for outcomes
3. Identify alternatives
MCDA – Discuss results with stakeholders
Exercise: next Tuesday, 15.12.20158.00-9.45 AM in HIL E 15.2Aims Carry out own small MCDA; demonstrate feasibility (Excel)
Focus on elicitation of stakeholder preferences (single-attribute value functions, weights)
Preparation (in small group) Choose environmental problem. Define: What is the problem?
What are objectives and main trade-offs between objectives?Who decides/ is affected? What are their interests?
Define 4 main objectives and attributes (as in class today)
Define 4 decision alternatives (as in class today)
Fill in prediction matrix (as in class today)
For each alternative: How well are objectives achieved?MCDA – Predictions
Objective
Alternative
1. 2. 3. 4.
a.
b.
c.
d.
Exercise: Tuesday, 15.12.2015 8:00 –HIL E 15.2
???What did you like / not like?
Discussion / questions / comments