Probabilistic preference theory, including probabilistic-based expressions, refers to a conceptual framework using probabilities to align humans’ thoughts and perceptions. During the past several years, the probabilistic preference theory has emerged as a hot research topic due to the fact that probabilistic preference representation models are natural ways to identify and model human-centric decision-making problems.

It can be regarded as a bridge to connect the probabilistic uncertainty and fuzzy uncertainty and is a new branch of fuzzy system. It has achieved a lot of good applications in either engineering or management science fields. The studies in probabilistic preference theory and applications are promising and should be further researched to develop new theories and techniques, and open new application areas in management sciences and engineering.

The objective of this Special Issue is to explore the up-to-date mathematical foundations, modelling and synthesis algorithms concerning probabilistic preference theory, and their applications in various fields relevant to fuzzy systems and practical engineering cases. Any theoretical and experimental works related to probabilistic preference theory, including fuzzy modelling, clustering, optimization, and hybrid fuzzy systems, are welcome. In particular, new interdisciplinary approaches and system-related research in probabilistic preference theory and applications in economics, engineering, medical, and artificial intelligence, or strong conceptual foundations in newly evolving topics are especially welcome. We welcome both original research articles as well as review articles discussing the current state of the art.

Potential topics include but are not limited to the following:

• Probabilistic preference models and extensions
• Probabilistic linguistic term set and decision making
• Fuzzy optimization with probabilistic preference sets
• Fuzzy clustering with probabilistic preference sets
• Fuzzy reasoning with probabilistic preference sets
• Mathematical operations on probabilistic preferences
• Applications of probabilistic preference theory in healthcare, management, economics, engineering, big data analytics and AI