Neutrosophic Paradigms Advancements In Decision Making And Statistical Analysis
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Neutrosophic Paradigms: Advancements in Decision Making and Statistical Analysis
Author: Florentin Smarandache
language: en
Publisher: Springer Nature
Release Date: 2025-03-30
This book offers a comprehensive reference guide to neutrosophic theory and its applications in decision-making in numerous disciplines, ranging from business, economics and management, computer science, health and environmental sciences, and many others. Chapters were selected to cover: different extensions of neutrosophic sets, statistical techniques for dealing with imprecise data and decision-making, and a range of real-life examples. All in all, the book is intended to provide a timely update on the state-of-the-art in neutrosophic decision-making for researchers and professionals from various disciplines, as well as master’s and graduate students in mathematics, statistics, and computer science.
Multiple Criteria Decision Making
Ch. 1. The early history of MCDM -- ch. 2. MCDM developments in the 1970s -- ch. 3. MCDM developments in the 1980s -- ch. 4. MCDM developments in the 1990s and beyond -- ch. 5. MCDM conferences -- ch. 6. MCDM society traditions -- ch. 7. Awards and presidents -- ch. 8. Biographies of leading MCDM scholars -- ch. 9. Conclusion
Decision Making
This volume focuses on uncovering the fundamental forces underlying dynamic decision making among multiple interacting, imperfect and selƠ̐lsh decision makers. The chapters are written by leading experts from different disciplines, all considering the many sources of imperfection in decision making, and always with an eye to decreasing the myriad discrepancies between theory and real world human decision making. Topics addressed include uncertainty, deliberation cost and the complexity arising from the inherent large computational scale of decision making in these systems. In particular, analyses and experiments are presented which concern: ĺØ task allocation to maximize ĺlthe wisdom of the crowdĺl; ĺØ design of a society of ĺledutainmentĺl robots who account for one anothersĺl emotional states; ĺØ recognizing and counteracting seemingly non-rational human decision making; ĺØ coping with extreme scale when learning causality in networks; ĺØ efƠ̐lciently incorporating expert knowledge in personalized medicine; ĺØ the effects of personality on risky decision making. The volume is a valuable source for researchers, graduate students and practitioners in machine learning, stochastic control, robotics, and economics, among other Ơ̐lelds.