Decision making is one of people’s usual activities since situations that require making decisions constantly arise in their everyday life. Very often, however, the dimensionality and complexity of decision problems reach far beyond human’s capabilities while the cost of a poor decision may be rather high. Therefore, there is a need in software support to the decision making process, and, in response, a wide variety of software tools, from tools for building graphs and charts to expert systems and intelligent agents, are offered as decision support systems. The reason for this variety is the compound, multi-faceted nature of the decision making process.

The most widely accepted generalisation of the decision-making process was introduced by Simon [25], who divided the process into three major phases: intelligence, design, and choice. The intelligence phase involves data collection, integration, pre-processing, and exploration with the aim to identify the problems or opportunities. During the design phase, one looks for a set of possible solutions to the problem(s) identified in the intelligence phase and analyses the options thus found. During the choice phase, the options are evaluated and analysed in relation to others, and a particular option or a set of options is selected. Typically, a decision-maker must account for multiple, often conflicting, evaluation criteria, and the final decision results from an explicit or implicit trade-off. At any point in the decision-making process, it may be necessary to loop back to an earlier phase.

Malczewski [20] mentions the software tools that can support the decision process at the different stages. Techniques of exploratory data analysis, or EDA, play a major role in the intelligence phase. For determining the set of alternatives in the design phase, formal models are typically used. The phase of choice is supported by an array of computational methods developed within the research field called multi-criteria decision-making, or MCDM.

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