Information uncertainty and complexity is a common paradigm in modern decision making because perfect information is seldom available to decision makers. A wide range of statistical and nonstatistical decision-making models have been proposed in the literature to model complex systems under uncertainty. Statistical methods (i.e., probability theory) are useful in modeling complex systems with incomplete or inaccurate data while nonstatistical methods (i.e., fuzzy set theory, rough set theory, possibility theory, or fuzzy neural networks) are useful for modeling complex systems with imprecise, ambiguous, or vague data.

Today’s real-world problems involve multiple data sets, some precise or objective and some uncertain or subjective. Hybrid decision-making models are quickly emerging as alternative methods of choice for modeling complex systems under uncertainty. Managing uncertainty is a prerequisite to effective problem solving and decision-making in complex systems.

We invite authors to submit original research articles that propose formal decision-making methods to both describe and rationalize the process of decision making in complex systems under uncertainty. This special issue covers managing information uncertainty in complex systems by means of the following topics.

Potential topics include but are not limited to the following:

• Statistical methods such as probabilistic approaches and simulation-based methods
• Nonstatistical methods such as fuzzy set theory, rough set theory, possibility theory, or fuzzy neural networks
• Hybrid multiple-criteria decision-making methods using interval-valued fuzzy sets, intuitionistic fuzzy sets, or neutrosophic sets