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Distributed decision making (DDM) has become of increasing importance in quantitative decision analysis. In applications like supply chain management, service operations, or managerial accounting, DDM has led to a paradigm shift. The book provides a unified approach to such seemingly diverse fields as multi-level stochastic programming, hierarchical production planning, principal agent theory, negotiations or contract theory. Different settings like multi-level one-person decision problems, multi-person antagonistic planning, and leadership situations are covered. Numerous examples and real-life planning cases illustrate the concepts. The new edition has been considerably expanded by additional chapters on supply chain management, service operations and multi-agent systems.
Frequently (and often inappropriately) decision making in the work environment has been analyzed and modeled in terms of isolated decisions made by one person. In reality, decision making is a continuous, interpersonal process usually involving several ``decision makers'' aiming at dynamic and cooperative control of the state of affairs at work. Based on original contributions from researchers and research teams, this book provides an urgently needed cognitive approach to models of distributed decision making, exploring the basis for design of decision support systems in various complex, collective, modern work environments. It identifies the state of the art of modeling distributed decision making and the problems imposed by modern high-tech systems. A also formulates promising research avenues.
Explores decision making in organizations, highlighting the roles of incentive, conflict, power and politics.
Decision making in today's organizations is often distributed widely and usually supported by such technologies as satellite communications, electronic messaging, teleconferencing, and shared data bases. Distributed Decision Making outlines the process and problems involved in dispersed decision making, draws on current academic and case history information, and highlights the need for better theories, improved research methods and more interdisciplinary studies on the individual and organizational issues associated with distributed decision making. An appendix provides additional background reading on this socially and economically important problem area.
Distributed Decision Making and Control is a mathematical treatment of relevant problems in distributed control, decision and multiagent systems, The research reported was prompted by the recent rapid development in large-scale networked and embedded systems and communications. One of the main reasons for the growing complexity in such systems is the dynamics introduced by computation and communication delays. Reliability, predictability, and efficient utilization of processing power and network resources are central issues and the new theory and design methods presented here are needed to analyze and optimize the complex interactions that arise between controllers, plants and networks. The text also helps to meet requirements arising from industrial practice for a more systematic approach to the design of distributed control structures and corresponding information interfaces Theory for coordination of many different control units is closely related to economics and game theory network uses being dictated by congestion-based pricing of a given pathway. The text extends existing methods which represent pricing mechanisms as Lagrange multipliers to distributed optimization in a dynamic setting. In Distributed Decision Making and Control, the main theme is distributed decision making and control with contributions to a general theory and methodology for control of complex engineering systems in engineering, economics and logistics. This includes scalable methods and tools for modeling, analysis and control synthesis, as well as reliable implementations using networked embedded systems. Academic researchers and graduate students in control science, system theory, and mathematical economics and logistics will find mcu to interest them in this collection, first presented orally by the contributors during a sequence of workshops organized in Spring 2010 by the Lund Center for Control of Complex Engineering Systems, a Linnaeus Center at Lund University, Sweden.>
This book constitutes the thoroughly refereed post-conference proceedings of the 21st International Workshop on Multi-Agent-Based Simulation, MABS 2021, held in May 2021 as part of AAMAS 2021. The conference was held virtually due to COVID 19 pandemic. The 14 revised full papers included in this volume were carefully selected from 23 submissions. The workshop focused on finding efficient solutions to model complex social systems, in such areas as economics, management, organizational and social sciences in general. In all these areas, agent theories, metaphors, models, analysis, experimental designs, empirical studies, and methodological principles, all converge into simulation as a way of achieving explanations and predictions, exploration and testing of hypotheses, better designs and systems and providing decision-support in a wide range of applications.
Distributed decision making is described in this book from a hierarchical perspective. A unified approach allows to treat such seemingly diverse fields as multi-level decision making, hierarchical production planning, principal agent theory, hierarchical negotiations, and dynamic games within the framework of a general pair of functional equations. In doing so, the book covers the range from a multi-level one-person decision problem to a multi-person antagonistic planning and leadership situation. These general ideas are illustrated with numerous examples and real-life planning situations. In addition, the treatise provides a theoretical foundation for important problem areas in business administration such as hierarchical production planning, the problems of design and implementation, modern concepts in managerial accounting, and supply chain management.
This book provides an introductory treatment of the fundamentals of decision-making in a distributed framework. Classical detection theory assumes that complete observations are available at a central processor for decision-making. More recently, many applications have been identified in which observations are processed in a distributed manner and decisions are made at the distributed processors, or processed data (compressed observations) are conveyed to a fusion center that makes the global decision. Conventional detection theory has been extended so that it can deal with such distributed detection problems. A unified treatment of recent advances in this new branch of statistical decision theory is presented. Distributed detection under different formulations and for a variety of detection network topologies is discussed. This material is not available in any other book and has appeared relatively recently in technical journals. The level of presentation is such that the hook can be used as a graduate-level textbook. Numerous examples are presented throughout the book. It is assumed that the reader has been exposed to detection theory. The book will also serve as a useful reference for practicing engineers and researchers. I have actively pursued research on distributed detection and data fusion over the last decade, which ultimately interested me in writing this book. Many individuals have played a key role in the completion of this book.
Advances in digital innovations continue to dramatically change daily life, work and the economy. Transforming to a Digital Society and Economy provides deep insights for advancing economic growth in our developing digital world, explaining the underlying techno-economics and our ability to make informed decisions. Using empirical analyses derived from data-driven modeling, Transforming to a Digital Society and Economy explores the impacts of digital innovation on socio-economic phenomena, resilience, and governance. It examines the limitation of using GDP as a measure of economic growth in digital societies, stressing how the Internet promotes a "free" culture that cannot be captured through GDP data. The book synthesizes multi-dimensional research consisting of digital platform ecosystems observations, theoretical appraisals, statistical methods development, in-depth empirical analysis, and database construction for analysis and outcomes compilation. Utilizing analysis from more than 500 global ICT leaders, Transforming to a Digital Society and Economy identifies potential challenges and solutions for academic analysis, economic planning, and policy-making.
The governance of health care in Ontario has long provided opportunities for citizens and stakeholders to participate, deliberate, and influence health care policy and investment decisions. Yet, despite providing opportunities for deliberation and influence amongst citizens, we don’t know how democratic the system actually is. Distributed Democracy advances an original analytical framework to guide an investigation of democracy and accountability relationships in complex policy making environments. Applying the analytical framework in the context of health care governance in Ontario from 2004–2019, Carey Doberstein shows that the popular criticisms of health care governance in Ontario are misplaced. The democratic system of local health care governance is often plagued by severed connections among the various layers of deliberation and policy-making. An incisive analysis with considerable relevance for policy-makers and across academic disciplines, Distributed Democracy makes an important contribution to our understanding of policy development and decision-making as well as the limitations and potential of distributed democratic accountability.