Uncertainty modeling decision making (65 résultats)

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Paperback. Etat : new. Paperback. Research and development organizations operate in environments characterized by technical complexity, uncertainty, limited resources, demanding schedules, and rapidly changing strategic priorities. Their success depends not only on scientific knowledge and engineering capability but also on the ability of multidisciplinary teams to integrate specialized expertise, communicate effectively, manage uncertainty, and make sound decisions. These challenges become particularly significant when projects involve emerging technologies, distributed teams, and objectives that may evolve throughout the development process.This book was written to provide an integrated perspective on the management and performance of multidisciplinary R&D teams and organizations. It brings together concepts from engineering management, organizational behavior, systems thinking, probability, stochastic modeling, computational simulation, and decision science. Its central premise is that uncertainty should not be treated solely as an obstacle. When it is identified, modeled, and interpreted systematically, uncertainty can provide valuable information for planning, resource allocation, risk mitigation, and organizational learning.Particular attention is given to the relationships among individual competence, team cohesion, communication, technical uncertainty, organizational structure, and project performance. These factors do not operate independently. They form an interconnected system in which decisions and behaviors generate feedback that can either strengthen or weaken organizational performance. Accordingly, the book approaches R&D management as a dynamic process requiring continuous observation, evaluation, adaptation, and learning.The book also examines how stochastic models and computational methods can support management decisions. Monte Carlo simulation, sensitivity analysis, network modeling, risk assessment, and related techniques provide a structured means of exploring possible outcomes when complete information is unavailable. These methods do not eliminate uncertainty or replace managerial judgment. Instead, they help decision-makers understand the range of plausible outcomes, identify influential variables, compare alternative strategies, and select actions that are more robust under changing conditions.The material is intended for engineering managers, R&D leaders, project managers, systems engineers, researchers, graduate students, and professionals working in technology-intensive organizations. Readers with management backgrounds may use the book to develop a better understanding of quantitative modeling and simulation. Readers with engineering or scientific backgrounds may use it to examine the organizational and behavioral dimensions of technical performance. The interdisciplinary presentation is designed to encourage communication between these communities and support a more unified approach to R&D management.This book does not propose that a single model can fully represent the complexity of an R&D organization. Human behavior, technical development, and organizational change cannot be predicted with absolute precision. Nevertheless, carefully constructed models can clarify assumptions, reveal dependencies, test alternative scenarios, and improve the quality and transparency of management decisions. Their greatest value lies not in producing a single definitive forecast but in supporting informed reasoning under uncertainty.Ultimately, the book seeks to contribute to the development of R&D organizations that are more collaborative, adaptive, resilient, and capable of sustained innovation. By connecting human and organizational considerations with stochastic modeling and computational analysis, it offers a framework through which uncertainty can be transformed from an unmanaged source o Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Paperback. Etat : new. Paperback. Research and development organizations operate in environments characterized by technical complexity, uncertainty, limited resources, demanding schedules, and rapidly changing strategic priorities. Their success depends not only on scientific knowledge and engineering capability but also on the ability of multidisciplinary teams to integrate specialized expertise, communicate effectively, manage uncertainty, and make sound decisions. These challenges become particularly significant when projects involve emerging technologies, distributed teams, and objectives that may evolve throughout the development process.This book was written to provide an integrated perspective on the management and performance of multidisciplinary R&D teams and organizations. It brings together concepts from engineering management, organizational behavior, systems thinking, probability, stochastic modeling, computational simulation, and decision science. Its central premise is that uncertainty should not be treated solely as an obstacle. When it is identified, modeled, and interpreted systematically, uncertainty can provide valuable information for planning, resource allocation, risk mitigation, and organizational learning.Particular attention is given to the relationships among individual competence, team cohesion, communication, technical uncertainty, organizational structure, and project performance. These factors do not operate independently. They form an interconnected system in which decisions and behaviors generate feedback that can either strengthen or weaken organizational performance. Accordingly, the book approaches R&D management as a dynamic process requiring continuous observation, evaluation, adaptation, and learning.The book also examines how stochastic models and computational methods can support management decisions. Monte Carlo simulation, sensitivity analysis, network modeling, risk assessment, and related techniques provide a structured means of exploring possible outcomes when complete information is unavailable. These methods do not eliminate uncertainty or replace managerial judgment. Instead, they help decision-makers understand the range of plausible outcomes, identify influential variables, compare alternative strategies, and select actions that are more robust under changing conditions.The material is intended for engineering managers, R&D leaders, project managers, systems engineers, researchers, graduate students, and professionals working in technology-intensive organizations. Readers with management backgrounds may use the book to develop a better understanding of quantitative modeling and simulation. Readers with engineering or scientific backgrounds may use it to examine the organizational and behavioral dimensions of technical performance. The interdisciplinary presentation is designed to encourage communication between these communities and support a more unified approach to R&D management.This book does not propose that a single model can fully represent the complexity of an R&D organization. Human behavior, technical development, and organizational change cannot be predicted with absolute precision. Nevertheless, carefully constructed models can clarify assumptions, reveal dependencies, test alternative scenarios, and improve the quality and transparency of management decisions. Their greatest value lies not in producing a single definitive forecast but in supporting informed reasoning under uncertainty.Ultimately, the book seeks to contribute to the development of R&D organizations that are more collaborative, adaptive, resilient, and capable of sustained innovation. By connecting human and organizational considerations with stochastic modeling and computational analysis, it offers a framework through which uncertainty can be transformed from an unman Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

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Taschenbuch. Etat : Neu. Neuware - Reactive PublishingUnlock the power of Bayesian methods and probabilistic programming with this clear, practical guide designed for data scientists, statisticians, and analysts working in R.This book bridges the gap between theory and real-world application by teaching you how to build, fit, and interpret hierarchical Bayesian models using Stan, the leading platform for probabilistic programming. Through hands-on examples and intuitive explanations, you'll learn how to effectively quantify uncertainty, make robust inferences, and support better decision-making under complexity.What You'll Learn: - The fundamentals of Bayesian modeling and why it outperforms traditional frequentist approaches in many modern applications- How to construct and diagnose hierarchical models for grouped, nested, and multilevel data- Practical workflows for probabilistic programming with Stan and R- Techniques for uncertainty quantification and propagation through complex models- Model comparison, validation, and communication of results for stakeholdersWritten for intermediate to advanced R users, this guide emphasizes code you can immediately apply to your own projects, whether in research, industry, or academia. Each chapter combines conceptual clarity with reproducible examples, helping you move confidently from basic Bayesian concepts to sophisticated modeling techniques.If you want to move beyond point estimates and p-values toward a more principled, uncertainty-aware approach to data analysis and decision making, this book provides the practical foundation you need.Perfect for: Data scientists, quantitative researchers, statisticians, and R programmers looking to master modern Bayesian workflows.…

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Taschenbuch. Etat : Neu. Neuware - Reactive PublishingIn an era where financial markets are driven by noise, complexity, and uncertainty, Bayesian Methods for Quantitative Finance delivers a decisive framework for navigating volatility with confidence. This authoritative guide redefines modern financial modeling through the Bayesian lens, empowering traders, analysts, and quants to make smarter, adaptive decisions in uncertain environments.Unravel the mechanics behind belief updating, posterior inference, and probabilistic modeling as you explore hands-on applications in risk modeling, asset pricing, portfolio optimization, and algorithmic trading. Whether you're recalibrating macroeconomic assumptions or building real-time market prediction engines, Bayesian thinking provides a mathematically rigorous yet intuitive path to clarity.From hierarchical models to Monte Carlo simulations, this book bridges theory and practice with advanced Python examples and real-world use cases. Equip yourself with the tools to not just analyze data-but learn from it.…

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Taschenbuch. Etat : Neu. Optimal Decision-Making for Catastrophic Earthquakes | Seismic hazard modeling, loss estimation, and decision-making under uncertainty | Katsuichiro Goda | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639138962 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

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paperback. Etat : New. Language:Chinese.Paperback. Pub Date: 2023-06 Pages: 172 Publisher: Science Press Granular computing simulates the cognitive thinking rules of the human brain to improve the efficiency of solving complex problems. and has played an important role in the field of intelligent information processing. This book focuses on studying uncertainty problems from a multi-granularity perspective. From the perspectives of multi-granularity modeling and decision-making. the structural characteristics of mu.…

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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book systematically explores theories related to linguistic computational models and group decision making methods under uncertainty. It introduces innovative linguistic computational models capable of fusing complex linguistic information, including multi-granular linguistic information, unbalanced linguistic information and hesitant fuzzy linguistic information. Building upon the linguistic computational models, this book presents methods tailored to various types of group decision making problems under uncertainty. Additionally, it delves into group decision making problems where the personalized individual semantics of experts are considered. The book also showcases practical applications of the proposed group decision making methods, ranging from ERP system supplier selection to talent recruitment, subway line selection, and location selection for electric vehicle charging stations. By shedding light on novel models for modeling complex linguistic information and introducing new approaches to addressing linguistic group decision making challenges, this book offers valuable insights for engineers, researchers, and postgraduates interested in decision analysis, operations research, computational intelligence, management science and engineering, and related fields.…

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book systematically explores theories related to linguistic computational models and group decision making methods under uncertainty. It introduces innovative linguistic computational models capable of fusing complex linguistic information, including multi-granular linguistic information, unbalanced linguistic information and hesitant fuzzy linguistic information. Building upon the linguistic computational models, this book presents methods tailored to various types of group decision making problems under uncertainty. Additionally, it delves into group decision making problems where the personalized individual semantics of experts are considered. The book also showcases practical applications of the proposed group decision making methods, ranging from ERP system supplier selection to talent recruitment, subway line selection, and location selection for electric vehicle charging stations. By shedding light on novel models for modeling complex linguistic information and introducing new approaches to addressing linguistic group decision making challenges, this book offers valuable insights for engineers, researchers, and postgraduates interested in decision analysis, operations research, computational intelligence, management science and engineering, and related fields.…

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