MCDA in natural resources management PART I

1. MCDA in natural resources managementPART I Jouni Pykäläinen, D.Sc.(For.) Metsämonex Ltd

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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)

9. 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

10. 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

11. 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

12. 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

13. 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

14. Planning steps in utility analysis • Formulation of the decision hierarchy • Defining sub-utility functions • Defining weights for the goals • Calculating the priorities for the alternatives • Sensitivity analysis

15. Decision hierarchy example

16. Sub-utility functions 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].

17. Defining forms of sub-utility functions • paired comparisons • different scales available • direct numeric method • interpolation of intermediate values • graphic interfaces • SMART

18. Effect of setting weights for the criteria/sub-criteria. weight = 0,6 weight = 0,4

19. Defining weights for criteria and sub-criteria • paired comparisons • different scales available • direct numeric method • interpolation of intermediate values • graphic interfaces

20. Calculating the total utilities 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

21. Output Total utility criteria

22. Including the participants 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

23. Example of including ”parties” level in the decision hierarchy Pykäläinen et al. 1997

24. Sensitivity analysis • 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

25. Some pros of the introduced approach • 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

26. Some cons of the introduced approach • 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

27. Non-quantifying vs. quantifying approach • 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.