Uncertainty is a fundamental aspect of the decision-making process, especially in Multi-Criteria Decision Making (MCDM) problems, where every stage of problem-solving involves some degree of uncertainty. MCDM problems are characterized by a framework of multiple criteria and alternatives, designed to achieve one or several objectives of the decision-making process. Ranging from everyday issues to highly complex industrial, military, and political challenges, MCDM problems have outcomes that can significantly impact many lives or incur substantial financial costs. Therefore, developing solutions that produce the least uncertain outputs is crucial.

Uncertainty in decision-making typically arises when decision-makers lack complete information about the elements of decision analysis or its outcomes. The primary aim of this paper-based doctoral dissertation is to offer a comprehensive solution, termed the 'theory of everything,' that addresses all sources of uncertainty in MCDM problems. Thirteen sources of uncertainty have been identified, including:

Decision-making goals

Decision-makers

Linguistic variables and scales

Conversion of uncertain to certain values

Weighting methods

MCDM method processes

Time

Missing information

Multi-layer problems

Philosophies and policies, encompassing the decision-making paradox

Validation

The Rank Reversal Paradox

To tackle each of these sources, various MCDM algorithms, statistical measures, novel scales, new numerical sets, a game, and a new theoretical framework were proposed. These solutions are detailed across eighteen scientific articles. In the final chapter, the unified model, including its components, properties, and limitations, is thoroughly discussed, offering a holistic approach to managing uncertainty in MCDM.