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This book presents research on recent developments in collective decision-making. With contributions from leading scholars from a variety of disciplines, it provides an up-to-date overview of applications in social choice theory, welfare economics, and industrial organization. The contributions address, amongst others, topics such as measuring power, the manipulability of collective decisions, and experimental approaches. Applications range from analysis of the complicated institutional rules of the European Union to responsibility-based allocation of cartel damages or the design of webpage rankings. With its interdisciplinary focus, the book seeks to bridge the gap between different disciplinary approaches by pointing to open questions that can only be resolved through collaborative efforts.
This book presents research on recent developments in collective decision-making. With contributions from leading scholars from a variety of disciplines, it provides an up-to-date overview of applications in social choice theory, welfare economics, and industrial organization. The contributions address, amongst others, topics such as measuring power, the manipulability of collective decisions, and experimental approaches. Applications range from analysis of the complicated institutional rules of the European Union to responsibility-based allocation of cartel damages or the design of webpage rankings. With its interdisciplinary focus, the book seeks to bridge the gap between different disciplinary approaches by pointing to open questions that can only be resolved through collaborative efforts.
The rapidly evolving business and technology landscape demands sophisticated decision-making tools to stay ahead of the curve. Advances in Complex Decision Making: Using Machine Learning and Tools for Service-Oriented Computing is a cutting-edge technical guide exploring the latest decision-making technology advancements. This book provides a comprehensive overview of machine learning algorithms and examines their applications in complex decision-making systems in a service-oriented framework. The authors also delve into service-oriented computing and how it can be used to build complex systems that support decision making. Many real-world examples are discussed in this book to provide a practical insight into how discussed techniques can be applied in various domains, including distributed computing, cloud computing, IoT and other online platforms. For researchers, students, data scientists and technical practitioners, this book offers a deep dive into the current developments of machine learning algorithms and their applications in service-oriented computing. This book discusses various topics, including Fuzzy Decisions, ELICIT, OWA aggregation, Directed Acyclic Graph, RNN, LSTM, GRU, Type-2 Fuzzy Decision, Evidential Reasoning algorithm and robust optimisation algorithms. This book is essential for anyone interested in the intersection of machine learning and service computing in complex decision-making systems.
Volume 37 brings together papers related to a variety of topics in small groups and organizational research. The volume includes papers that address theoretical and empirical issues related to consumer social privilege, group processes and disrupted environments, the use of time as a construct and the affective bases of self.
This title looks at decision making from the manager’s viewpoint and aims to help you to improve your decision making. It also recognises that, in business today, decision making is everyone’s responsibility. Decision making for staff, who are not managers, is often through involvement in team decisions and the module also explores the benefits and limitations of team decision making.
This book is focused on fuzzy group decision making, offering technical details including methodology, collaboration and aggregation mechanisms, system architectures, and applications. It explores the two categories of fuzzy-group decision making - anterior-aggregation and posterior-aggregation – and highlights how imperative collaboration amongst decision makers is for both forms. Advances in Fuzzy Group Decision Making will be of interest to researchers in decision making, artificial intelligence, soft computing, operations management, and information management, as well as practicing managers and engineers.
Communication and Group Decision-Making takes stock of recent group communication research - with an explicit focus on communication processes. This book is recommended for academics professionals and researchers in communication and organization.
"This book explores the world of Decision Making Support Systems (DMSS), which encompasses Decision Support Systems (DSS), Executive Information Systems (EIS), Expert Systems (ES), Knowledge Based Systems (KBS), Creativity Enhancing Systems (CES), and more"--Provided by publisher.
The long-awaited second edition of Communication and Group Decision Making advances a unique perspective on group decisionmaking, complementing approaches taken in management, psychology, and sociology. Group communication processes are extremely important, yet they have proven to be elusive and difficult to understand, and the type of theory necessary to make sense of the processes differs from those commonly found in the social sciences. This exceptional book gathers together and discusses a number of strong theoretical frameworks that have developed over the past 15 years. Providing important empirical evidence, the authors take stock of recent developments in group communication research. The essays are distinctive, both in their explicit focus on communication processes and in their location in a unique intellectual tradition.
This book reviews and presents several approaches to advanced decision-making models for safety and risk assessment. Each introduced model provides case studies indicating a high level of efficiency, robustness, and applicability, which allow readers to utilize them in their understudy risk-based assessment applications. The book begins by introducing a novel dynamic DEMATEL for improving safety management systems. It then progresses logically, dedicating a chapter to each approach, including advanced FMEA with probabilistic linguistic preference relations, Bayesian Network approach and interval type-2 fuzzy set, advanced TOPSIS with spherical fuzzy set, and advanced BWM with neutrosophic fuzzy set and evidence theory. This book will be of interest to professionals and researchers working in the field of system safety and reliability and postgraduate and undergraduate students studying applications of decision-making tools and expert systems.