Entropy Method for Decision Making with Uncertainty
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: 25 December 2024 | Viewed by 1148
Special Issue Editor
Interests: decision-making systems; dispersed data; distributed learning; rough sets; artificial intelligence; data mining; expert systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In today's complex and dynamic world, decision-making processes are frequently challenged by various forms of uncertainty. The need to navigate through this uncertainty while optimizing outcomes has led to the development of advanced computational techniques rooted in fields such as expert systems, distributed learning, rough sets, fuzzy sets, and game theory. Entropy, particularly, holds a crucial position in the realm of information theory and has proven to be efficacious in the context of decision making.
This Special Issue aims to explore the intersection of these fields to advance our understanding of decision making under uncertainty and to propose robust computational solutions.
Theoretical frameworks such as rough sets and fuzzy sets provide formalisms for handling imprecise and uncertain information, allowing decision makers to model ambiguity inherent in real-world scenarios. Additionally, expert systems guide decision-making processes, enhancing the accuracy and reliability of outcomes. Furthermore, distributed learning techniques enable collaborative decision making in decentralized environments, where data may be distributed across multiple sources.
In parallel, game theory, particularly power index analysis, offers insights into strategic interactions among decision makers. By integrating these diverse methodologies, researchers can develop holistic approaches to decision making that account for multiple sources of uncertainty and strategic considerations.
Decision making in Artificial General Intelligence (AGI) and Large Language Models (LLMs) involves complex processes of evaluating information, generating possible courses of action, and selecting the most appropriate one based on predefined goals or criteria. Decision making in both AGI and LLM can be influenced by factors such as uncertainty, incomplete information, and the presence of conflicting objectives. Decision making often involves simulating human-like cognitive processes such as reasoning, planning, and learning.
We invite researchers to submit their original research contributions, case studies, and review articles that address the challenges and opportunities in this multidisciplinary domain.
Prof. Dr. Małgorzata Przybyła-Kasperek
Guest Editor
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Keywords
- rough sets
- fuzzy sets
- expert systems
- distributed learning
- multi-criteria decision making (MCDM)
- game theory
- decision making
- uncertainty
- decision support systems
- information fusion
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