Decision-making support strategies over the last several decades have evolved into consideration various (sophisticated) approaches, including expert judgments, cost-benefit analysis, comparative risk assessment,
uncertainty analysis with the use of probabilistic and fuzzy approaches, and different methods for incorporating public and stakeholder values. This evolution has led to an improved array of decision-making aides,
including the development of Multi-Criteria Decision Analysis (MCDA) tools that offer a scientifically sound decision analytical framework. The existence of different MCDA methods and the availability of corresponding
software both contribute to the possibility of practical implementation of these methods. However, many different MCDA methods may be utilized for analysis of an applied problem, and investigation is needed into the
effects of using various methods, especially when recommendations concerning implementation of each method are ambiguous.
One of the objectives of the DECERNS project is creation of an up-to-date system for flexible and effective application of different methods and tools for multicriteria analysis when solving practical problems.
Such an approach may be considered as an analysis of internal uncertainties within multicriteria decision-making support.
The common purpose of MCDA methods is to evaluate and choose among alternatives based on multiple criteria using systematic analysis that overcomes the limitations of unstructured individual or group decision-making (Belton 2002; von Winterfeldt 1986).
The following main categories of problems are considered on the basis of MCDA:
- ranking alternatives (from “best” to “worst” according to a chosen algorithm);
- selecting the “best alternative” from a given set of alternatives;
- screening alternatives – a process of eliminating those alternatives that do not appear to warrant further attention, i.e., selecting a smaller set of alternatives that (very likely)
contains the “best”/trade-off alternative;
- sorting alternatives into classes/categories (e.g., “unacceptable”, “possibly acceptable”, “definitely acceptable” etc.); and
- designing (searching, identifying, creating) a new action/alternative to meet goals.
Some other categories of problems, e.g. description/learning problematique (analysis of actions to gain greater understanding of what may or may not be achievable) and portfolio problematique (choice of a subset of alternatives, taking account not only of individual
characteristics of each alternative, but also of their positive and negative interrelations), may also be considered with the use of MCDA approaches.
Three dichotomies within MCDA problems can be distinguished (Malczewski, 1999), Fig.1:
- multi-attribute decision making (MADM: a finite number of alternatives which are defined explicitly) versus multi-objective decision making (MODM: infinite or large number of alternatives which are defined, as a rule, implicitly);
- individual versus group decision making; and
- decisions under certainty versus decisions under uncertainty.

Fig.1
In the current version of DecernSDSS MADM methods were realized (MODM algorithms for investigation of some problem-specific tasks are considered independently of SDSS and can be integrated with
Decerns within a customization).
DECERNS SDSS contains all the steps within the decision-making process necessary for the analysis of [spatial] alternatives. Fig.2 presents a decision process flow chart for typical spatial MCDA/MADM problems.
This example highlights the effort of the participants in this process including experts, decision makers, and a wider range of stakeholders and the involved SDSS tools. The process includes input from stakeholders,
decision makers, and scientists and involves:

Fig.2
- problem definition;
- development of alternatives and criteria specification;
- generation of a performance table based on the results of the criteria assessments;
- determination of the preferences and weighting/scaling criteria by the stakeholder community.
- assessments of the alternatives against the different criteria which are conducted using models, GIS tools, and expert/stakeholder judgments; the MCDA tools take this information and perform sensitivity/uncertainty analysis;
- stakeholder review of the resulting selecting/ranking/screening/sorting of alternatives;
- recommendations which are made for the decision makers; a process which can be repeated iteratively to refine any of the steps.
All the indicated steps within the decision-making process are important, but it should be stressed that the stage of the multicriteria problem structuring (problem definition, using alternatives-based or
criteria-based approaches and implementation of Value Trees and top-down or bottom-up approaches within development of alternatives and criteria specification, etc.) is considered by specialists as the key one.
The following multi-criteria methods and tools are used in the DECERNS WebSDSS (for ranking and selecting alternatives, first of all, and for screening (indirectly); methods for ‘sorting’ alternatives will be added later):
basic MADM methods:
- MAVT (Multi-Attribute Value Theory);
- AHP (Analytic Hierarchy Process);
- TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution);
- PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluations);
advanced MADM methods:
- MAUT (Multi-Attribute Utility Theory); and
- ProMAA (Probabilistic Multi-criteria Acceptability Analysis; developed by authors);
some extensions of MADM methods based on fuzzy set approaches:
- Fuzzy-MAVT;
- Fuzzy-MAA/FMAA, developed by authors, arlsson & Fuller, 1996).
We describe briefly all the MCDA methods included in DecernsSDSS.
Details and discussion of these methods may be found in the publications indicated in the end of this section.