Human-Centric Decision-Making Models for Social Sciences Paperback / softback

Edited by Peijun Guo, Witold Pedrycz

Part of the Studies in Computational Intelligence series

Paperback / softback

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Description

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.