A Multi Criteria Analysis (MCA) is
used for the appraisal of options for policy and other decisions which do not
necessarily rely on monetary valuations. MCA establishes preferences between
options by reference to an explicit set of objectives that the decision making
body has identified, and for which it has established measurable indicators
and/or criteria to assess the extent to which the objectives have been
achieved. In simple circumstances, the process of identifying objectives and
criteria may alone provide enough information for decision-makers. However,
where a level of detail broadly similar to CBA is required, MCA offers a number
of ways of aggregating the data of individual indicators and/or criteria to
provide the overall performance of options to make decisions.
A standard feature of multi-criteria analysis is a decision matrix in which each row describes an option and each column describes the performance of the options against an indicator and/or criterion. The individual performance assessments are often numerical, but may also be expressed as color coding. An Example of a decision matrix is shown in Figure 1.
Figure 1: An example of a decision matrix
In General, The following steps are carried out to undertake an MCA:
1. Establish the decision context. What are the aims of the MCA, and who are the decision makers and other key players?
2. Identify the objectives and criteria that reflect the value associated with the consequences of each option.
3. Generate the scenarios (or options).
4. Evaluate the expected performance of each scenario against the criteria which could entail some modeling work. (If the analysis is to include steps 5 and 6, also ‘score’ the scenarios, i.e. assess the value associated with the consequences of each option.)
5. ‘Weighting’. Assign weights for each of the criteria to reflect their relative importance to the decision. These weights reflect the preferences of the decision makers and/or stakeholders
6. Combine the weights and scores for each of the options to derive and overall value.
7. Examine the results and conduct a sensitivity analysis of the results to changes in scores or weights.
8. Select acceptable scenario(or option): often a (shortlist) of few scenarios are selected and subjected to further evaluations before final decision is made on selection.
The process is not linear as the decision taken in step 8 may show the need to study more scenarios (going back to step 3) or to refine the definitions of criteria and the underlying indicators (step 2). Decision making is an iterative process. The number of iterations depends on the quality of the planning and the complexity of the problem.
Steps 1, 2 and 3 are carried out before using the DSS in meetings or workshops involving the stakeholders. Steps 5 and 8 are done with the assistance of the NB DSS but it requires meetings and interaction with stakeholders. Once this is done the rest of the steps can be undertaken as follows:
1. Create Models
2. Define Scenarios for the Models
3. Define Indicators for model outputs
4. Run (at least one) simulation per scenario
5. Review the evaluated indicators
6. Create an MCA Setup
7. Create one or more MCA Sessions