Multi-Criteria Decision Analysis - ETH Z · Mindful decision-making Weber, E.U. & Johnson E.J. (2009) Mindful judgement & decision-making, Annu. Rev. Psychol. EMOTION S ATTENTION

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

13

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









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, …)

…


Who decides?Who is affected?









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?


?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

22

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?



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

25

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?








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

29

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








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)


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)


80% 35% 0.77 (good)

0 250 2% 0 0 863 (1.3% of income)


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



Objective

Alternative

1. High flexibility

2. Good chemical

state

3. Low time demand

4. Low costs


35% 0.77(good)

0 863 CHF/p/yr(1.3% of income)


50% 0.3(unsatis-factory)



70% 0.5(moderate)

20 hrs/p/yr 400 CHF/p/yr


90% 0.85(very good)


Break








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



0.28

value

0.5Costs (Mio CHF)

10





Objective

Alternative

1. High flexibility

2. Good chemical

state

3. Low time demand

4. Low costs


35% 0.77(good)



50% 0.3(unsatis-factory)



70% 0.5(moderate)



90% 0.85(very good)


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


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) = ….


Preferences

Objective

Alternative

1. High flexibility


3. Low time demand

4. Low costs


35%

v1(a1) = 0

0.77 (good)

v2(a2) = 0.86

0

v3(a3) = 1

863 CHF/p/yr

v4(a4) = 0


50%

v1(b1) = 0.28

0.3 (unsatisf.)

v2(b2) = 0

0

v3(b3) = 1

76 CHF/p/yr

v4(b4) = 1


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


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

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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

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1

PreferencesMCDA – Weight elicitation with SWING

Objective

Alternative

1. High flexibility


3. Low time demand

4. Low costs


35%

v1(a1) = 0

0.77 (good)

v2(a2) = 0.86

0

v3(a3) = 1

863 CHF/p/yr

v4(a4) = 0


50%

v1(b1) = 0.28

0.3 (unsatisf.)

v2(b2) = 0

0

v3(b3) = 1

76 CHF/p/yr

v4(b4) = 1


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


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


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?



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?


DM: (think, think, ...) I suppose the objective xy

(note: this is alternative b3)


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


?

?

?

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)


m

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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


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.








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


0 0.86 1 0 v(a) = ….


0.28 0 1 1 v(b) = ….


0.64 0.38 0 0.58 v(c) = ….


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.








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

Documents

Multi-Criteria Decision Analysis - ETH Z · Mindful decision-making Weber, E.U. & Johnson E.J. (2009) Mindful judgement & decision-making, Annu. Rev. Psychol. EMOTION S ATTENTION