Managing Uncertainties In Networks

Despite sophisticated technology and knowledge, the strategic networks and games required to solve uncertainties becomes more complex and more important than ever before.

Managing Knowledge in Strategic Alliances is a volume in the book series Research in Strategic Alliances that will focus on providing a robust and comprehensive forum for new scholarship in the field of strategic alliances. In particular, the books in the series will cover new views of interdisciplinary theoretical frameworks and models, significant practical problems of alliance organization and management, and emerging areas of inquiry. The series will also include comprehensive empirical studies of selected segments of business, economic, industrial, government, and non-profit activities with wide prevalence of strategic alliances. Through the ongoing release of focused topical titles, this book series will seek to disseminate theoretical insights and practical management information that will enable interested professionals to gain a rigorous and comprehensive understanding of the field of strategic alliances. Managing Knowledge in Strategic Alliances contains contributions by leading scholars in the field of strategic alliance research. The 11 chapters in this volume cover a number of significant topics that speak to the critical issues in managing knowledge in strategic alliances. The chapter topics cover both the broader issues, such as managing uncertainty in alliances, collaborative know-how, novelty in interpartner knowledge, coopetition in knowledge integration, and dynamic knowledge capabilities, and the more focused problems of innovation and partner selection, partner responsiveness and knowledge in supply chain networks, the effect of knowledge flows on the decision to cooperate, and interpartner learning dynamics in an alliance constellation. The chapters include empirical as well as conceptual treatments of the selected topics, and collectively present a wide-ranging review of the noteworthy research perspectives on knowledge management in strategic alliances.

Managing uncertainties in industrial systems is a daily challenge to ensure improved design, robust operation, accountable performance and responsive risk control. Authored by a leading European network of experts representing a cross section of industries, Uncertainty in Industrial Practice aims to provide a reference for the dissemination of uncertainty treatment in any type of industry. It is concerned with the quantification of uncertainties in the presence of data, model(s) and knowledge about the system, and offers a technical contribution to decision-making processes whilst acknowledging industrial constraints. The approach presented can be applied to a range of different business contexts, from research or early design through to certification or in-service processes. The authors aim to foster optimal trade-offs between literature-referenced methodologies and the simplified approaches often inevitable in practice, owing to data, time or budget limitations of technical decision-makers. Uncertainty in Industrial Practice: Features recent uncertainty case studies carried out in the nuclear, air & space, oil, mechanical and civil engineering industries set in a common methodological framework. Presents methods for organizing and treating uncertainties in a generic and prioritized perspective. Illustrates practical difficulties and solutions encountered according to the level of complexity, information available and regulatory and financial constraints. Discusses best practice in uncertainty modeling, propagation and sensitivity analysis through a variety of statistical and numerical methods. Reviews recent standards, references and available software, providing an essential resource for engineers and risk analysts in a wide variety of industries. This book provides a guide to dealing with quantitative uncertainty in engineering and modelling and is aimed at practitioners, including risk-industry regulators and academics wishing to develop industry-realistic methodologies.

Communication between man and machine is vital to completing projects in the current day and age. Without this constant connectiveness as we enter an era of big data, project completion will result in utter failure. Agile Approaches for Successfully Managing and Executing Projects in the Fourth Industrial Revolution addresses changes wrought by Industry 4.0 and its effects on project management as well as adaptations and adjustments that will need to be made within project life cycles and project risk management. Highlighting such topics as agile planning, cloud projects, and organization structure, it is designed for project managers, executive management, students, and academicians.

Uncertainties in Modern Power Systems combines several aspects of uncertainty management in power systems at the planning and operation stages within an integrated framework. This book provides the state-of-the-art in electric network planning, including time-scales, reliability, quality, optimal allocation of compensators and distributed generators, mathematical formulation, and search algorithms. The book introduces innovative research outcomes, programs, algorithms, and approaches that consolidate the present status and future opportunities and challenges of power systems. The book also offers a comprehensive description of the overall process in terms of understanding, creating, data gathering, and managing complex electrical engineering applications with uncertainties. This reference is useful for researchers, engineers, and operators in power distribution systems. Includes innovative research outcomes, programs, algorithms, and approaches that consolidate current status and future of modern power systems Discusses how uncertainties will impact on the performance of power systems Offers solutions to significant challenges in power systems planning to achieve the best operational performance of the different electric power sectors

"Wicked" problems are large-scale, long-term policy dilemmas in which multiple and compounding risks and uncertainties combine with sharply divergent public values to generate contentious political stalemates; wicked problems in the environmental arena typically emerge from entrenched conflicts over natural resource management and over the prioritization of economic and conservation goals more generally. This new book examines past experience and future directions in the management of wicked environmental problems and describes new strategies for mitigating the conflicts inherent in these seemingly intractable situations. The book: reviews the history of the concept of wicked problems examines the principles and processes that managers have applied explores the practical limitations of various approaches Most important, the book reviews current thinking on the way forward, focusing on the implementation of "learning networks," in which public managers, technical experts, and public stakeholders collaborate in decision-making processes that are analytic, iterative, and deliberative. Case studies of forest management in the Sierra Nevada, restoration of the Florida Everglades, carbon trading in the European Union, and management of the Ngorongoro Conservation Area in Tanzania are used to explain concepts and demonstrate practical applications. Wicked Environmental Problems offers new approaches for managing environmental conflicts and shows how managers could apply these approaches within common, real-world statutory decision-making frameworks. It is essential reading for anyone concerned with managing environmental problems.

Governance Networks in the Public Sector presents a comprehensive study of governance networks and the management of complexities in network settings. Public, private and non-profit organizations are increasingly faced with complex, wicked problems when making decisions, developing policies or delivering services in the public sector. These activities take place in networks of interdependent actors guided by diverging and sometimes conflicting perceptions and strategies. As a result these networks are dominated by cognitive, strategic and institutional complexities. Dealing with these complexities requires sophisticated forms of coordination: network governance. This book presents the most recent theoretical and empirical insights into governance networks. It provides a conceptual framework and analytical tools to study the complexities involved in handling wicked problems in governance networks in the public sector. The book also discusses strategies and management recommendations for governments, business and third sector organisations operating in and governing networks. Governance Networks in the Public Sector is an essential text for advanced students of public management, public administration, public policy and political science, and for public managers and policymakers.

An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.