This research domain considers the uncertainty analysis (or quantization of uncertainty) and its usage, for instance, epistemic uncertainty – located in the mathematical computing field. The main focuses are:
The theory of fuzzy sets such as intuitions fuzzy sets, type-2 fuzzy sets, hesitant fuzzy sets, dual-book fuzzy sets, conflict-prone sets, odd theories and probability theories which is less accurate such as Dempster-Shafer’s proving theory (epicentre uncertainty) and probability theory and variance (aleatory uncertainty) theory;
The application of fuzzy sets such as those practiced in decision theory, control theory, operational research, statistics, topology, behavioural sciences, computational geometric design, artificial intelligence etc.
For instance, in decision theory, the focus is on the analysis of the results under the aspects of multi-dimensional such as addition and multiple reference relationship models, high-rank relationship models, cardinal-based decision-making models, operational aggregations for informational lacquering involving equality and inequality among the data and propositions (example: average weighting, fuzzy operational aggregations such as “Choquet” and “Sugeno” integrals). The integral techniques mentioned can develop a sophisticated decision-making model which considers the complex results of the analysis of real-world problems. The latest research considers human perception in the analysis of results, including human behavioural considerations or references between two extreme cases: pessimistic and optimistic views.