Entropy Method for Decision Making

Dear Colleagues,

It is well known that any nontrivial decision is burdened with risk. The source of risk is usually a lack of reliable information, in other words, an uncertainty. When making a decision, we usually try to find a compromise between competing criteria of profit (in wide sense) maximization and risk minimization. Thus, some measure of uncertainty is explicitly or implicitly part of decision making. It is important to note that in the most decision making techniques, the criterion of uncertainty minimization is used, but implicitly, without strict mathematical formalization. Although such methods usually provide good results, it seems to be more justified from a methodological point of view to use formalized measures of uncertainty, especially entropy, which plays a key role in the theory of information and has already been successfully used in decision making. Entropy was originally intended to operate with probabilistic uncertainty, but today, in decision making, we deal with a wide spectrum of uncertainties: interval, fuzzy, type 2 fuzzy, interval-valued fuzzy, intuitionistic fuzzy, hesitant fuzzy, evidential (Dempster–Shafer theory of evidence), etc. and their different combinations. In some cases, the basic definition of entropy is adapted to process such types of uncertainty, but generally, there are many new challenges in this field. Therefore, in this Special Issue, we shall encourage the submission of papers devoted to the adaptation of entropy to the solution of multiple criteria decision making (MCDM) problems in the presence of modern types of uncertainty. However, interesting and valuable papers where the problem of uncertainty minimization in MCDM is presented implicitly will be considered as well.

Prof. Dr. Pavel Sevastjanov
Guest Editor

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