Human-Centric Decision-Making Models for Social Sciences [electronic resource] /
Guo, Peijun.editor.
Pedrycz, Witold.editor.
The volume delivers a wealth of effective methods to deal with various types of uncertainty inherently existing in human-centric decision problems. It elaborates on  comprehensive decision frameworks to handle different decision scenarios, which help use effectively the explicit and tacit knowledge and intuition, model perceptions and preferences in a more human-oriented style. The book presents original approaches and delivers new results on fundamentals and applications related to human-centered decision making approaches to business, economics and social systems. Individual chapters cover multi-criteria (multiattribute) decision making, decision making with prospect theory, decision making with incomplete probabilistic information, granular models of decision making and decision making realized with the use of non-additive measures. New emerging decision theories being presented as along with a wide spectrum of ongoing research make the book valuable to all interested in the field of advanced decision-making. The volume, self-contained in its nature, offers a systematic exposure to the concepts, design methodologies, and detailed algorithms. A prudent balance between the theoretical studies and applications makes the material suitable for researchers and graduate students in information, computer sciences, psychology, cognitive science, economics, system engineering, operation research and management science, risk management, public and social policy.      .
Bibliographic Level Mode of Issuance: Monograph
Decision making in the environment of heterogeneous uncertainty -- One-Shot Decision Theory: A Fundamental Alternative for Decision under Uncertainty -- On the Influence of Emotion on Decision Making: The Case of Charitable Giving -- Decision Theory and Rules of Thumb -- Aggregating Imprecise Linguistic Expressions -- Risk Perception and Ambiguity in a Quantile Cumulative Prospect Theory -- Effective Decision Making in Changeable Spaces, Covering and Discovering Processes: A Habitual Domain Approach -- Decision Making under Interval Uncertainty (and Beyond) -- Dealing with imprecision in consumer theory: A new approach to Fuzzy Utility Theory -- Decision making under Z-information -- Approximations of one-dimensional expected utility integral of alternatives described with linearly-interpolated p-boxes -- Human-Centric Cognitive Decision Support System for Ill-Structured Problems.
The volume delivers a wealth of effective methods to deal with various types of uncertainty inherently existing in human-centric decision problems. It elaborates on  comprehensive decision frameworks to handle different decision scenarios, which help use effectively the explicit and tacit knowledge and intuition, model perceptions and preferences in a more human-oriented style. The book presents original approaches and delivers new results on fundamentals and applications related to human-centered decision making approaches to business, economics and social systems. Individual chapters cover multi-criteria (multiattribute) decision making, decision making with prospect theory, decision making with incomplete probabilistic information, granular models of decision making and decision making realized with the use of non-additive measures. New emerging decision theories being presented as along with a wide spectrum of ongoing research make the book valuable to all interested in the field of advanced decision-making. The volume, self-contained in its nature, offers a systematic exposure to the concepts, design methodologies, and detailed algorithms. A prudent balance between the theoretical studies and applications makes the material suitable for researchers and graduate students in information, computer sciences, psychology, cognitive science, economics, system engineering, operation research and management science, risk management, public and social policy.      .
Computational intelligence.
Artificial intelligence.
Social sciences.
Computational Intelligence.
Artificial Intelligence.
Social Sciences, general.
Studies in Computational Intelligence