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MCDA in natural resources management PART I. Jouni Pykäläinen, D.Sc.(For.) Metsämonex Ltd. Contents. planning steps quantifying and non-quantifying planning approaches prior and interactive articulation of preferences applying explicit utility modelling in forest management planning

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Mcda in natural resources management part i
MCDA in natural resources managementPART I

Jouni Pykäläinen, D.Sc.(For.)

Metsämonex Ltd


Contents
Contents

  • planning steps

  • quantifying and non-quantifying planning approaches

  • prior and interactive articulation of preferences

  • applying explicit utility modelling in forest management planning

  • pros and cons of the introduced approach

  • example of interactive approach

  • computer based (CB) - MCDA; examples and exercises by using HIPRE -software


Why mcda
Why MCDA?

  • even a single DM often has multiple goals in NRE -planning

  • in many cases, several participants - with equal or unequal decision authority

  • NRE-planning problems are often very complex

  • finding the best plan calls for effective techniques for determining the NRE management goals and comparing and evaluating the decision alternatives

  • -> multi-criteria decision analysis (MCDA) has become more and more popular in NRE planning


Planning supporting decision making
Planning = supporting decision making

  • planning steps:

  • a) structuring the decision problem

  • b) assessing possible impacts of each decision alternative

  • c) determining preferences of the DM and other participants of planning (goal analysis)

  • d) comparing and evaluating decision alternatives

  • ---------------------------------------------------------------------

  • decision making (selection among alternative plans)

  • the order of the steps may vary

  • the steps may be repeated several times

  • the steps may be implemented even simultaneusly


Characteristics of non quantifying planning approach
Characteristics of non-quantifying planning approach

  • many sided discussions and illustrations

  • collaborative learning is emphasized

  • DM’s and other planning participants’ goals are defined verbally or graphically

  • holistic evaluation of the alternatives


Characteristics of quantifying planning approach
Characteristics of quantifying planning approach

  • goals for NRE-management defined numerically in so called utility models

  • problem solving (optimization) techniques set requirements for measuring goals and realization of them in different alternatives

  • optimization; utility is maximized by solving the utility model


Utility models
Utility models

  • explicit utility functions

  • target-, constraint- aspiration- and acceptability levels for the criteria values

    • mathematical programming applications

    • restricting the amount of acceptable solutions interactively

    • simplifying utility function determination


Optimization
Optimization

  • explicit utility functions

    • discrete cases: including the values of criteria produced by different alternatives to the utility function and calculating the results

    • in continuous cases (plenty of alternatives): heuristic optimization techniques

  • mathematical programming algorithms (e.g. Simplex algorithm)


Prior articulation of preferences
Prior articulation of preferences

  • assumes, that a utility model, which directly results in an optimal plan, can be formulated in the first trial

  • straightforward process: defining the utility model and solving it


Interactive articulation of preferences
Interactive articulation of preferences

  • the solution is gradually improved by alternating the steps of defining the utility model and solving it

  • process is continued until the DM is satisfied with the result

  • DM’s goals are an important output of the planning process

    • the DM learns his preferences in the specific planning case during the process


When is it wise to use interactive techniques
When is it wise to use interactive techniques?

  • in planning situations where it is too difficult to define the utility model in advance

    • NRE –management goals are fuzzy for the DM

    • production possibilities of the planning area #ARE# not known well enough in advance

    • effects of producing different goals on the resource #ARE# not known well enough in advance

    • difficulties in using and understanding the planning method and/or the planning interface; interactive approach offers good possibilities for practising the planning technique


Hybrid approach of prior and interactive articulation of preferences
Hybrid approach of prior and interactive articulation of preferences

  • to reach a plan that fulfils the DM's goals in the best possible way, both prior and interactive articulation of preferences may be needed

  • promoting the DM’s understanding of the bases for the solutions and planning more generally, adequate prior articulation of preferences probably decreases the amount of iterations needed in the interactive step

  • -> increases the DM's trust in the method and prevents frustration


Mcda in natural resources management part i
A quantitative approach of MCDA: applying explicit utility modelling in forest management planning (Utility analysis in this presentation)

  • the approach illustrates: how a #multi- criteria# decision should be made ?

  • goals and utilities offered by different alternatives measured explicitly on interval scale

  • methods using ordinal and qualitative scales may give the same alternative to be the best one and even the priority order may become the same

    • remarkable differences in further analysis possibilities and communicational aspects


Planning steps in utility analysis
Planning steps in utility analysis modelling in forest management planning (Utility analysis in this presentation)

  • Formulation of the decision hierarchy

  • Defining sub-utility functions

  • Defining weights for the goals

  • Calculating the priorities for the alternatives

  • Sensitivity analysis


Decision hierarchy example
Decision hierarchy example modelling in forest management planning (Utility analysis in this presentation)


Sub utility functions
Sub-utility functions modelling in forest management planning (Utility analysis in this presentation)

Criteria/sub-criteria have different

values in different alternatives

Sub-utility functions

transform values of

the criteria measured in their own units into subjective sub-utility

values [0-1].


Defining forms of sub utility functions
Defining forms of sub-utility functions modelling in forest management planning (Utility analysis in this presentation)

  • paired comparisons

    • different scales available

  • direct numeric method

    • interpolation of intermediate values

  • graphic interfaces

  • SMART


Effect of setting weights for the criteria sub criteria
Effect of setting weights for the criteria/sub-criteria. modelling in forest management planning (Utility analysis in this presentation)

weight = 0,6

weight = 0,4


Defining weights for criteria and sub criteria
Defining weights for criteria and sub-criteria modelling in forest management planning (Utility analysis in this presentation)

  • paired comparisons

    • different scales available

  • direct numeric method

    • interpolation of intermediate values

  • graphic interfaces


Calculating the total utilities produced by the alternatives
Calculating the total utilities modelling in forest management planning (Utility analysis in this presentation)produced by the alternatives

  • rescaled (weighted) sub-utilities are summed up

where ui(qi) is a sub-utility function for criteria i

qi is the value of criteria i

ai is the weight of criteria i

n is the number of criteria


Mcda in natural resources management part i

Output modelling in forest management planning (Utility analysis in this presentation)

Total utility

criteria


Including the participants in the utility model formulation
Including the participants modelling in forest management planning (Utility analysis in this presentation)in the utility model formulation

  • For example:

  • one common utility function for all participants produced through discussions and negotiations

  • own weights for the criteria/sub-criteria defined by different participants

  • own weights and sub-utility functions defined by different participants

  • some parts of the utility model can be formulated by experts


Example of including parties level in the decision hierarchy
Example of including ”parties” modelling in forest management planning (Utility analysis in this presentation)level in the decision hierarchy

Pykäläinen et al. 1997


Sensitivity analysis
Sensitivity analysis modelling in forest management planning (Utility analysis in this presentation)

  • effects of changing the weights of the criteria/sub-criteria

  • effects of changing the forms of the sub-utility functions

  • effects of changing the weights of the parties in cases of multiple participants


Some pros of the introduced approach
Some pros of the introduced approach modelling in forest management planning (Utility analysis in this presentation)

  • explicit definition of the decision making principles; transparency

  • forces one to focus on essentials

  • possibilities to integrate expert knowledge into utility models

  • effective technical problem solving

  • possibilities to sensitivity analysis


Some cons of the introduced approach
Some cons of the introduced approach modelling in forest management planning (Utility analysis in this presentation)

  • difficulties in measuring some of the criteria

    • risk that all important aspects are not included into the planning model

  • possible difficulties in understanding the planning method

    • requires time for educating the planning method for the planning participants


Mcda in natural resources management part i

Non-quantifying vs. quantifying approach modelling in forest management planning (Utility analysis in this presentation)

  • a) planning problems often complex

  • b) there are also several goals which should be taken into account at the same time

  • c) quantifying the problem may be difficult or even impossible

  • d) different people have different ways to grasp and process information (learning styles)

  • a + b -> need for quantifying approaches

  • c + d -> need for non-quantifying approach

  • Best support for decisions is often attained by using both approaches in the same process and comparing and evaluating the results of them.