Saariselkä 19.-21.4.2001

MCDS methods in strategic MCDS methods in strategic planning- alternatives for AHPplanning- alternatives for AHP

Annika Kangas & Jyrki Kangas

Ecological informationEcological information

• Ecological / recreational information often has low quality – risk of ditch maintenance or clearcutting to

watercourse– wildlife population viability

Need for methods that deal with low quality information and uncertainty

Public participationPublic participation

• Public participation (e.g.in State forests) involves a large number of participants

• Group decision making involves several DMs high costs and poor availability of information

Need for methods that have low information requirements and enable cheap preference elicition

Multicriteria approval Multicriteria approval

• Based on approval voting– instead of several voters several criteria

considered

• Information requirements– criteria ranked according to importance– acceptability of alternatives with respect to each

criteria, for example• above average acceptable

• below average not acceptable

Usability Usability

• Could be used for public participation – post or internet inquiries

• Criteria values measured in ratio or interval scale are downscaled to ordinal scale

information is lost

OutrankingOutranking

• Ordinal, interval and ratio scale information can be used– information transformed to pseudo-criteria – uncertainty dealt with pseudo-criteria

thresholds

• Weights of criteria interpreted as votes

• If intensities of preferences are known, information may be lost

Public participation examplePublic participation example

• In State owned forests public participation obligatory

• Case study– four participants: FPS, regional group, local group and

public

– four main criteria: FPS’s business revenues, socio-economic values, recreational values and conservational values, measured with 17 variables

– six strategies

Decision hierarchyDecision hierarchy

OVERALLUTILITY

Forest and Park Service

Regionalwork group

Local workgroups (4)

Public

FPS´s businessrevenues

regional socioeconomicvalues

forest recreationvalues

natureconservationvalues

effects on employment effects on the GNP

area of commercial forest, haFPS´s financial surplus in Kainuu, FIM/yeard timber volume in commercial forests, m3

indirect effects, working yearsFPS´s supply of work, working years

FPS´s turnover, FIM/year

recreation forests, hacommercial forests, recreational values, harecreation value indexwater quality index

conserved area, hacommercial forests, conservation values, had dead wood volume, m3

d area of old forests, had volume of hardwood, m3

PARTIES CRITERIA SUB-CRITERIA CRITERION VARIABLES STRATEGIES

d stands for ”a change in” during the planning period

Observed rankingsObserved rankings

Strategy HIPRE Promethee II ELECTRE III

Business 1 1 6

Basic 2 3 1

Forest recreation 3 2 3

Mixed 2 4 4 4

Mixed 1 5 5 2

Nature conservation 6 6 5

Group decision making exampleGroup decision making example

• Jointly owned forests problem in forest management– all owners need to approve management actions

• Case study– three owners with equal share

– 20 forest plans

– six criteria: net incomes, value of the forest, landscape beauty, blueberry yield, capercaillie viability and biodiversity

Observed rankingsObserved rankings

Alternative AHP Promethee I Promethee II Promethee IIwith AHP weights

MA

S1 5. 1. 2. 7.S2 4. 1. 1. 4.S3 11. 1. 7. 3. 1.S4 1. 18. 10.S5 8. 14. 12. .S6 3. 1. 4. 5.S7 19. 8. 19.S8 7. 19. 20.S9 13. 1. 5. 9.S10 2. 20. 18.S11 14. 10. 14.S12 6. 17. 2.S13 9. 1. 3. 8.S14 18. 12. 16.S15 12. 1. 6. 6.S16 10. 13. 1.S17 15. 16. 11.S18 20. 9. 20.S19 16. 1. 15. 13.S20 17. 11. 15.

RequirementsRequirements

• Methods that utilise both low and high quality information– forest information fairly accurate when

compared to ecological criteria– all information in use, nothing wasted

• Uncertainty dealt with explicitly– Distributions of uncertain criterion values and /

or criterion weights

SMAA - a possibilitySMAA - a possibility

• Stochastic multicriteria acceptance analysis – what kind of preferences support any one alternative

• Weight information can be exact, partial or nonexistent

• Criterion values – uncertain cardinal values from distribution

– ordinal values converted to cardinal using simulation