Download or read book Managing Uncertainties in Networks written by Johannes Franciscus Maria Koppenjan and published by Psychology Press. This book was released on 2004 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Despite sophisticated technology and knowledge, the strategic networks and games required to solve uncertainties becomes more complex and more important than ever before.

Download or read book Managing Uncertainties in Networks written by Johannes Franciscus Maria Koppenjan and published by . This book was released on 2004 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Uncertainty in Industrial Practice written by Etienne de Rocquigny and published by John Wiley & Sons. This book was released on 2008-09-15 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Learning Under Uncertainty written by Donald P. Moynihan and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper examines the nature of learning in networks dealing with conditions of high uncertainty. I take Koppenjan and Klijn's (2004) framework for understanding network uncertainty and apply it to an extreme example of uncertainty, an interorganizational crisis taskforce dealing with an exotic animal disease. The paper identifies the basic difficulties involved in learning under crisis conditions. The taskforce had to learn most of the elements taken for granted in more mature structural forms - the nature of the structural framework in which it was working, how to adapt that framework, the role and actions appropriate for each individual, and how to deal with unanticipated problems. The network pursued this learning in a variety of ways. Most critically, the taskforce used standard operating procedures to provide a form of network memory.

Download or read book Agile Approaches for Successfully Managing and Executing Projects in the Fourth Industrial Revolution written by Bolat, Hür Bersam and published by IGI Global. This book was released on 2019-03-15 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Uncertainties in Modern Power Systems written by Ahmed F. Zobaa and published by Academic Press. This book was released on 2020-10-26 with total page 718 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Wicked Environmental Problems written by Peter J. Balint and published by Island Press. This book was released on 2012-06-22 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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.

Download or read book Managing in Uncertainty Theory and Practice written by Constantin Zopounidis and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a new point of view on the subject of the management of uncertainty. It covers a wide variety of both theoretical and practical issues involving the analysis and management of uncertainty in the fields of finance, management and marketing. Audience: Researchers and professionals from operations research, management science and economics.

Download or read book Optimization of Temporal Networks under Uncertainty written by Wolfram Wiesemann and published by Springer Science & Business Media. This book was released on 2012-01-04 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many decision problems in Operations Research are defined on temporal networks, that is, workflows of time-consuming tasks whose processing order is constrained by precedence relations. For example, temporal networks are used to model projects, computer applications, digital circuits and production processes. Optimization problems arise in temporal networks when a decision maker wishes to determine a temporal arrangement of the tasks and/or a resource assignment that optimizes some network characteristic (e.g. the time required to complete all tasks). The parameters of these optimization problems (e.g. the task durations) are typically unknown at the time the decision problem arises. This monograph investigates solution techniques for optimization problems in temporal networks that explicitly account for this parameter uncertainty. We study several formulations, each of which requires different information about the uncertain problem parameters.

Download or read book Managing Knowledge in Strategic Alliances written by T. K. Das and published by IAP. This book was released on 2013-04-01 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Governance Networks in the Public Sector written by Erik Hans Klijn and published by Routledge. This book was released on 2015-08-28 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Management of Uncertainty written by Gudela Grote and published by Springer Science & Business Media. This book was released on 2009-09-17 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: As I write, the financial systems of the world are collapsing with still no clear indication of what the consequences will be and which measures should be taken to avoid such a crisis in the future. There seems to be agreement though, that the financial instruments introduced in the past few decades entailed far too much complexity and uncertainty and that there was too little regulatory control over the use of these instruments. Management of uncertainty with the aim of achieving self-control is the core concern of this book. It was not written with a focus on financial systems, but many concepts developed in this book are applicable to this field as well. The - neric principles of reducing, maintaining or increasing uncertainties in view of the different contingencies an organization is faced with, the fundamental issue of how much control is possible and who should be in control, and the question of how much and what kind of regulation is necessary with the overall aim of finding an appropriate balance between system stability and flexibility are at the centre of heated debates on the future of finance.

Download or read book Decision Making Under Uncertainty written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2015-07-24 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Managing Uncertainties Using a Knowledge Management Approach written by S. C. L. Koh and published by . This book was released on 2000 with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Managing Uncertainty in Crisis written by Xiaoli Lu and published by Springer. This book was released on 2018-12-11 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book studies the variety of organizational strategies selected to cope with critical uncertainties during crises. This research formulates and applies an institutional sense-making model to explain the selection of strategies for coping with uncertainties during crises to answer the question why some organizations select a rule-based strategy to cope with uncertainties, whereas others pursue a more ad hoc-based strategy. It finds that the level of institutionalization does not affect strategy selection in the initial phase of responding to crises; that three rigidity effects can be identified in the selection of sense-making strategies once organizations have faced the failure of their selected strategies; that discontinuities in the feedback loop of sense-making do not necessarily move organizations to switch their sense-making strategies, but interact with institutionalization to contribute to switching sense-making strategies. This book bridges the gap between institutional thinking and crisis management theorizing. A major step forward in the world of crisis management studies! ——Professor Arjen Boin, Leiden University, the Netherlands In a world of increasingly complex, sociotechnical systems interacting in high-risk environments, Professor Lu’s analysis of how organizations manage uncertainty is both timely and profound. ——Professor Louise K. Comfort, Director, Center for Disaster Management, University of Pittsburgh, USA Prof. Lu greatly enhances our understanding of how organizations cope with uncertainty and make sense of their challenges under the pressures of catastrophe. ——Dr. Arnold M. Howitt, Faculty Co-Director, Program on Crisis Leadership, Harvard Kennedy School, USA This book provides not only a theory of crisis management but also a key concept around which research and practice can be conducted. ——Professor Naim Kapucu, Director of School of Public Administration, University of Central Florida, USA A generic institutional model for analyzing and managing hazards, disasters and crises worldwide. ——Professor Joop Koppenjan, Erasmus University Rotterdam, the Netherlands This book has done an excellent job in opening the black box of how organizations make sense of the crisis situations they face and develop strategies to respond. It should be read by all of us who wish for a peaceful and safe world. ——Professor Lan Xue, Dean of School of Public Policy and Management, Tsinghua University, China

Download or read book Uncertainties in Neural Networks written by Magnus Malmström and published by Linköping University Electronic Press. This book was released on 2021-04-06 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: In science, technology, and engineering, creating models of the environment to predict future events has always been a key component. The models could be everything from how the friction of a tire depends on the wheels slip to how a pathogen is spread throughout society. As more data becomes available, the use of data-driven black-box models becomes more attractive. In many areas they have shown promising results, but for them to be used widespread in safety-critical applications such as autonomous driving some notion of uncertainty in the prediction is required. An example of such a black-box model is neural networks (NNs). This thesis aims to increase the usefulness of NNs by presenting an method where uncertainty in the prediction is obtained by linearization of the model. In system identification and sensor fusion, under the condition that the model structure is identifiable, this is a commonly used approach to get uncertainty in the prediction from a nonlinear model. If the model structure is not identifiable, such as for NNs, the ambiguities that cause this have to be taken care of in order to make the approach applicable. This is handled in the first part of the thesis where NNs are analyzed from a system identification perspective, and sources of uncertainty are discussed. Another problem with data-driven black-box models is that it is difficult to know how flexible the model needs to be in order to correctly model the true system. One solution to this problem is to use a model that is more flexible than necessary to make sure that the model is flexible enough. But how would that extra flexibility affect the uncertainty in the prediction? This is handled in the later part of the thesis where it is shown that the uncertainty in the prediction is bounded from below by the uncertainty in the prediction of the model with lowest flexibility required for representing true system accurately. In the literature, many other approaches to handle the uncertainty in predictions by NNs have been suggested, of which some are summarized in this work. Furthermore, a simulation and an experimental studies inspired by autonomous driving are conducted. In the simulation study, different sources of uncertainty are investigated, as well as how large the uncertainty in the predictions by NNs are in areas without training data. In the experimental study, the uncertainty in predictions done by different models are investigated. The results show that, compared to existing methods, the linearization method produces similar results for the uncertainty in predictions by NNs. An introduction video is available at https://youtu.be/O4ZcUTGXFN0 Inom forskning och utveckling har det har alltid varit centralt att skapa modeller av verkligheten. Dessa modeller har bland annat använts till att förutspå framtida händelser eller för att styra ett system till att bete sig som man önskar. Modellerna kan beskriva allt från hur friktionen hos ett bildäck påverkas av hur mycket hjulen glider till hur ett virus kan sprida sig i ett samhälle. I takt med att mer och mer data blir tillgänglig ökar potentialen för datadrivna black-box modeller. Dessa modeller är universella approximationer vilka ska kunna representera vilken godtycklig funktion som helst. Användningen av dessa modeller har haft stor framgång inom många områden men för att verkligen kunna etablera sig inom säkerhetskritiska områden såsom självkörande farkoster behövs en förståelse för osäkerhet i prediktionen från modellen. Neuronnät är ett exempel på en sådan black-box modell. I denna avhandling kommer olika sätt att tillförskaffa sig kunskap om osäkerhet i prediktionen av neuronnät undersökas. En metod som bygger på linjärisering av modellen för att tillförskaffa sig osäkerhet i prediktionen av neuronnätet kommer att presenteras. Denna metod är välbeprövad inom systemidentifiering och sensorfusion under antagandet att modellen är identifierbar. För modeller såsom neuronnät, vilka inte är identifierbara behövs det att det tas hänsyn till tvetydigheterna i modellen. En annan utmaning med datadrivna black-box modeller, är att veta om den valda modellmängden är tillräckligt generell för att kunna modellera det sanna systemet. En lösning på detta problem är att använda modeller som har mer flexibilitet än vad som behövs, det vill säga en överparameteriserad modell. Men hur påverkas osäkerheten i prediktionen av detta? Detta är något som undersöks i denna avhandling, vilken visar att osäkerheten i den överparameteriserad modellen kommer att vara begränsad underifrån av modellen med minst flexibilitet som ändå är tillräckligt generell för att modellera det sanna systemet. Som avslutning kommer dessa resultat att demonstreras i både en simuleringsstudie och en experimentstudie inspirerad av självkörande farkoster. Fokuset i simuleringsstudien är hur osäkerheten hos modellen är i områden med och utan tillgång till träningsdata medan experimentstudien fokuserar på jämförelsen mellan osäkerheten i olika typer av modeller.Resultaten från dessa studier visar att metoden som bygger på linjärisering ger liknande resultat för skattningen av osäkerheten i prediktionen av neuronnät, jämfört med existerande metoder.

Download or read book Uncertainty in Complex Dynamical Networks written by Mengran Xue and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: As modern infrastructure networks become more and more complex and intricate, new research problems concerning network dynamics are arising (both in particular in application domains, and more broadly in systems and control theory). One keystone challenge is that many of these networks' operations are subject to internal and environmental uncertainty, both of which play a critical role in shaping the networks' dynamics and determining their performances. The impacts of these uncertainties on network dynamics and performance needs to be studied. The research in this thesis is primarily focused on developing new advanced control and design tools for complex dynamical networks subject to uncertainty. The presented analyses and methodologies are well motivated from an infrastructure perspective, and calibrated with requirements in numerous real applications.