Human Centric Decision Making Models For Social Sciences

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.

This book offers a concise introduction and comprehensive overview of the state of the art in the field of decision-making and consensus modeling, with a special emphasis on fuzzy methods. It consists of a collection of authoritative contributions reporting on the decision-making process from different perspectives: from psychology to social and political sciences, from decision sciences to data mining, and from computational sciences in general, to artificial and computational intelligence and systems. Written as a homage to Mario Fedrizzi for his scholarly achievements, creative ideas and long lasting services to different scientific communities, it introduces key theoretical concepts, describes new models and methods, and discusses a range of promising real-world applications in the field of decision-making science. It is a timely reference guide and a source of inspiration for advanced students and researchers

This book presents both methodological papers on and examples of applying behavioral predictive models to specific economic problems, with a focus on how to take into account people's behavior when making economic predictions. This is an important issue, since traditional economic models assumed that people make wise economic decisions based on a detailed rational analysis of all the relevant aspects. However, in reality – as Nobel Prize-winning research has shown – people have a limited ability to process information and, as a result, their decisions are not always optimal. Discussing the need for prediction-oriented statistical techniques, since many statistical methods currently used in economics focus more on model fitting and do not always lead to good predictions, the book is a valuable resource for researchers and students interested in the latest results and challenges and for practitioners wanting to learn how to use state-of-the-art techniques.

This book contains the topics of artificial intelligence and deep learning that do have much application in real-life problems. The concept of uncertainty has long been used in applied science, especially decision making and a logical decision must be made in the field of uncertainty or in the real-life environment that is formed and combined with vague concepts and data. The chapters of this book are connected to the new concepts and aspects of decision making with uncertainty. Besides, other chapters are involved with the concept of data mining and decision making under uncertain computations.

In the first approximation, decision making is nothing else but an optimization problem: We want to select the best alternative. This description, however, is not fully accurate: it implicitly assumes that we know the exact consequences of each decision, and that, once we have selected a decision, no constraints prevent us from implementing it. In reality, we usually know the consequences with some uncertainty, and there are also numerous constraints that needs to be taken into account. The presence of uncertainty and constraints makes decision making challenging. To resolve these challenges, we need to go beyond simple optimization, we also need to get a good understanding of how the corresponding systems and objects operate, a good understanding of why we observe what we observe – this will help us better predict what will be the consequences of different decisions. All these problems – in relation to different application areas – are the main focus of this book.

This book presents extended versions of selected papers from the annual International Workshops on Constraint Programming and Decision Making from 2016 to 2018. The papers address all stages of decision-making under constraints: (1) precisely formulating the problem of multi-criteria decision-making; (2) determining when the corresponding decision problem is algorithmically solvable; (3) finding the corresponding algorithms and making these algorithms as efficient as possible; and (4) taking into account interval, probabilistic, and fuzzy uncertainty inherent in the corresponding decision-making problems. In many application areas, it is necessary to make effective decisions under constraints, and there are several area-specific techniques for such decision problems. However, because they are area-specific, it is not easy to apply these techniques in other application areas. As such, the annual International Workshops on Constraint Programming and Decision Making focus on cross-fertilization between different areas, attracting researchers and practitioners from around the globe. The book includes numerous papers describing applications, in particular, applications to engineering, such as control of unmanned aerial vehicles, and vehicle protection against improvised explosion devices.

This volume is devoted to interactive and iterative processes of decision-making– I2 Fuzzy Decision Making, in brief. Decision-making is inherently interactive. Fuzzy sets help realize human-machine communication in an efficient way by facilitating a two-way interaction in a friendly and transparent manner. Human-centric interaction is of paramount relevance as a leading guiding design principle of decision support systems. The volume provides the reader with an updated and in-depth material on the conceptually appealing and practically sound methodology and practice of I2 Fuzzy Decision Making. The book engages a wealth of methods of fuzzy sets and Granular Computing, brings new concepts, architectures and practice of fuzzy decision-making providing the reader with various application studies. The book is aimed at a broad audience of researchers and practitioners in numerous disciplines in which decision-making processes play a pivotal role and serve as a vehicle to produce solutions to existing problems. Those involved in operations research, management, various branches of engineering, social sciences, logistics, and economics will benefit from the exposure to the subject matter. The book may serve as a useful and timely reference material for graduate students and senior undergraduate students in courses on decision-making, Computational Intelligence, operations research, pattern recognition, risk management, and knowledge-based systems.

Software is an essential enabler for science and the new economy. It creates new markets and directions for a more reliable, flexible and robust society and empowers the exploration of our world in ever more depth, but it often falls short of our expectations. Current software methodologies, tools, and techniques are still neither robust nor reliable enough for the constantly evolving market, and many promising approaches have so far failed to deliver the solutions required. This book presents the keynote ‘Engineering Cyber-Physical Systems’ and 64 peer-reviewed papers from the 16th International Conference on New Trends in Intelligent Software Methodology Tools, and Techniques, (SoMeT_17), held in Kitakyushu, Japan, in September 2017, which brought together researchers and practitioners to share original research results and practical development experience in software science and related new technologies. The aim of the SoMeT conferences is to capture the essence of the new state-of-the-art in software science and its supporting technology and to identify the challenges such technology will have to master. The book explores new trends and theories which illuminate the direction of developments in this field, and will be of interest to anyone whose work involves software science and its integration into tomorrow’s global information society.