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| decision modeling and analysis: Decision Modelling for Health Economic Evaluation Andrew Briggs, Mark Sculpher, Karl Claxton, 2006-08-17 In financially constrained health systems across the world, increasing emphasis is being placed on the ability to demonstrate that health care interventions are not only effective, but also cost-effective. This book deals with decision modelling techniques that can be used to estimate the value for money of various interventions including medical devices, surgical procedures, diagnostic technologies, and pharmaceuticals. Particular emphasis is placed on the importance of the appropriate representation of uncertainty in the evaluative process and the implication this uncertainty has for decision making and the need for future research. This highly practical guide takes the reader through the key principles and approaches of modelling techniques. It begins with the basics of constructing different forms of the model, the population of the model with input parameter estimates, analysis of the results, and progression to the holistic view of models as a valuable tool for informing future research exercises. Case studies and exercises are supported with online templates and solutions. This book will help analysts understand the contribution of decision-analytic modelling to the evaluation of health care programmes. ABOUT THE SERIES: Economic evaluation of health interventions is a growing specialist field, and this series of practical handbooks will tackle, in-depth, topics superficially addressed in more general health economics books. Each volume will include illustrative material, case histories and worked examples to encourage the reader to apply the methods discussed, with supporting material provided online. This series is aimed at health economists in academia, the pharmaceutical industry and the health sector, those on advanced health economics courses, and health researchers in associated fields. |
| decision modeling and analysis: Managerial Decision Modeling Nagraj (Raju) Balakrishnan, Barry Render, Ralph Stair, Charles Munson, 2017-08-07 This book fills a void for a balanced approach to spreadsheet-based decision modeling. In addition to using spreadsheets as a tool to quickly set up and solve decision models, the authors show how and why the methods work and combine the user's power to logically model and analyze diverse decision-making scenarios with software-based solutions. The book discusses the fundamental concepts, assumptions and limitations behind each decision modeling technique, shows how each decision model works, and illustrates the real-world usefulness of each technique with many applications from both profit and nonprofit organizations. The authors provide an introduction to managerial decision modeling, linear programming models, modeling applications and sensitivity analysis, transportation, assignment and network models, integer, goal, and nonlinear programming models, project management, decision theory, queuing models, simulation modeling, forecasting models and inventory control models. The additional material files Chapter 12 Excel files for each chapter Excel modules for Windows Excel modules for Mac 4th edition errata can be found at https://www.degruyter.com/view/product/486941 |
| decision modeling and analysis: Statistics, Data Analysis, and Decision Modeling James Robert Evans, 2007 This book covers basic concepts of business statistics, data analysis, and management science in a spreadsheet environment. Practical applications are emphasized throughout the book for business decision-making; a comprehensive database is developed, with marketing, financial, and production data already formatted on Excel worksheets. This shows how real data is used and decisions are made. Using Excel as the basic software, and including such add-ins as PHStat2, Crystal Ball, and TreePlan, this book covers a wide variety of topics related to business statistics: statistical thinking in business; displaying and summarizing data; random variables; sampling; regression analysis; forecasting; statistical quality control; risk analysis and Monte-Carlo simulation; systems simulation modeling and analysis; selection models and decision analysis; optimization modeling; and solving and analyzing optimization models. For those employed in the fields of quality control, management science, operations management, statistical science, and those who need to interpret data to make informed business decisions. |
| decision modeling and analysis: The Decision Model Barbara von Halle, Larry Goldberg, 2009-10-27 In the current fast-paced and constantly changing business environment, it is more important than ever for organizations to be agile, monitor business performance, and meet with increasingly stringent compliance requirements. Written by pioneering consultants and bestselling authors with track records of international success, The Decision Model: A |
| decision modeling and analysis: Handbook of Decision Analysis Gregory S. Parnell, Terry Bresnick, Steven N. Tani, Eric R. Johnson, 2013-01-24 A ONE-OF-A-KIND GUIDE TO THE BEST PRACTICES IN DECISION ANALYSIS Decision analysis provides powerful tools for addressing complex decisions that involve uncertainty and multiple objectives, yet most training materials on the subject overlook the soft skills that are essential for success in the field. This unique resource fills this gap in the decision analysis literature and features both soft personal/interpersonal skills and the hard technical skills involving mathematics and modeling. Readers will learn how to identify and overcome the numerous challenges of decision making, choose the appropriate decision process, lead and manage teams, and create value for their organization. Performing modeling analysis, assessing risk, and implementing decisions are also addressed throughout. Additional features include: Key insights gleaned from decision analysis applications and behavioral decision analysis research Integrated coverage of the techniques of single- and multiple-objective decision analysis Multiple qualitative and quantitative techniques presented for each key decision analysis task Three substantive real-world case studies illustrating diverse strategies for dealing with the challenges of decision making Extensive references for mathematical proofs and advanced topics The Handbook of Decision Analysis is an essential reference for academics and practitioners in various fields including business, operations research, engineering, and science. The book also serves as a supplement for courses at the upper-undergraduate and graduate levels. |
| decision modeling and analysis: Decision Models in Engineering and Management Patricia Guarnieri, 2015-01-05 Providing a comprehensive overview of various methods and applications in decision engineering, this book presents chapters written by a range experts in the field. It presents conceptual aspects of decision support applications in various areas including finance, vendor selection, construction, process management, water management and energy, agribusiness , production scheduling and control, and waste management. In addition to this, a special focus is given to methods of multi-criteria decision analysis. Decision making in organizations is a recurrent theme and is essential for business continuity. Managers from various fields including public, private, industrial, trading or service sectors are required to make decisions. Consequently managers need the support of these structured methods in order to engage in effective decision making. This book provides a valuable resource for graduate students, professors and researchers of decision analysis, multi-criteria decision analysis and group decision analysis. It is also intended for production engineers, civil engineers and engineering consultants. |
| decision modeling and analysis: Management Decision Making George E. Monahan, 2000-08-17 CD-ROM contains: Crystal Ball -- TreePlan -- AnimaLP -- Queue -- ExcelWorkbooks. |
| decision modeling and analysis: Decision Analysis, Location Models, and Scheduling Problems H. A. Eiselt, Carl-Louis Sandblom, 2004-01-12 The purpose of this book is to provide readers with an introduction to the fields of decision making, location analysis, and project and machine scheduling. The combination of these topics is not an accident: decision analysis can be used to investigate decision seenarios in general, location analysis is one of the prime examples of decision making on the strategic Ievel, project scheduling is typically concemed with decision making on the tactical Ievel, and machine scheduling deals with decision making on the operational Ievel. Some of the chapters were originally contributed by different authors, and we have made every attempt to unify the notation, style, and, most importantly, the Ievel of the exposition. Similar to our book on Integer Programming and Network Models (Eiselt and Sandblom, 2000), the emphasis of this volume is on models rather than solution methods. This is particularly important in a book that purports to promote the science of decision making. As such, advanced undergraduate and graduate students, as weil as practitioners, will find this volume beneficial. While different authors prefer different degrees of mathematical sophistication, we have made every possible attempt to unify the approaches, provide clear explanations, and make this volume accessible to as many readers as possible. |
| decision modeling and analysis: Statistics, Data Analysis, and Decision Modeling James Robert Evans, 2013 For undergraduate and graduate level courses that combines introductory statistics with data analysis or decision modeling. A pragmatic approach to statistics, data analysis and decision modeling. Statistics, Data Analysis & Decision Modeling focuses on the practical understanding of its topics, allowing readers to develop conceptual insight on fundamental techniques and theories. Evans' dedication to present material in a simple and straightforward fashion is ideal for student comprehension. |
| decision modeling and analysis: Real-World Decision Modeling with DMN James Taylor, Jan Purchase, 2023-07-24 Organizations make thousands of automated, operational decisions every week. How well they make these decisions drives profitability, reputation and customer satisfaction. Decision modeling helps them understand, automate and improve them |
| decision modeling and analysis: Policy Decision Modeling with Fuzzy Logic Ali Guidara, 2020-12-18 This book introduces the concept of policy decision emergence and its dynamics at the sub systemic level of the decision process. This level constitutes the breeding ground of the emergence of policy decisions but remains unexplored due to the absence of adequate tools. It is a nonlinear complex system made of several entities that interact dynamically. The behavior of such a system cannot be understood with linear and deterministic methods. The book presents an innovative multidisciplinary approach that results in the development of a Policy Decision Emergence Simulation Model (PODESIM). This computational model is a multi-level fuzzy inference system that allows the identification of the decision emergence levers. This development represents a major advancement in the field of public policy decision studies. It paves the way for decision emergence modeling and simulation by bridging complex systems theory, multiple streams theory, and fuzzy logic theory. |
| decision modeling and analysis: Hierarchical Decision Modeling Tugrul U. Daim, 2015-07-25 This volume, developed in honor of Dr. Dundar F. Kocaoglu, aims to demonstrate the applications of the Hierarchical Decision Model (HDM) in different sectors and its capacity in decision analysis. It is comprised of essays from noted scholars, academics and researchers of engineering and technology management around the world. This book is organized into five parts: Technology Policy Planning, Strategic Technology Planning, Technology Assessment, Application Extensions, and Methodology Extensions. Dr. Dundar F. Kocaoglu is one of the pioneers of multiple decision models using hierarchies, and creator of the HDM in decision analysis. HDM is a mission-oriented method for evaluation and/or selection among alternatives. A wide range of alternatives can be considered, including but not limited to, different technologies, projects, markets, jobs, products, cities to live in, houses to buy, apartments to rent, and schools to attend. Dr. Kocaoglu’s approach has been adopted for decision problems in many industrial sectors, including electronics research and development, education, government planning, agriculture, energy, technology transfer, semiconductor manufacturing, and has influenced policy locally, nationally, and internationally. Moreover, his students developed advanced tools and software applications to further improve and enhance the robustness of the HDM approach. Dr. Kocaoglu has made many contributions to the field of Engineering and Technology Management. During his tenure at Portland State University, he founded the Engineering and Technology Management program, where he served as Program Director and later, Department Chair. He also started the Portland International Conference on Management of Engineering and Technology (PICMET), which organizes an annual conference in international locations such as Korea, Turkey, South Africa, Thailand, and Japan. His teaching has won awards and resulted in a strong sense of student loyalty among his students even decades later. Through his academic work and research, Dr. Kocaoglu has strongly supported researchers of engineering management and has provided tremendous service to the field. This volume recognizes and celebrates Dr. Kocaoglu’s profound contributions to the field, and will serve as a resource for generations of researchers, practitioners and students. |
| decision modeling and analysis: Evaluation and Decision Models with Multiple Criteria Denis Bouyssou, Thierry Marchant, Marc Pirlot, Alexis Tsoukias, Philippe Vincke, 2006-06-07 Formal decision and evaluation models are so widespread that almost no one can pretend not to have used or suffered the consequences of one of them. This book is a guide aimed at helping the analyst to choose a model and use it consistently. A sound analysis of techniques is proposed and the presentation can be extended to most decision and evaluation models as a decision aiding methodology. |
| decision modeling and analysis: Handbook of Marketing Decision Models Berend Wierenga, 2008-09-05 Marketing models is a core component of the marketing discipline. The recent developments in marketing models have been incredibly fast with information technology (e.g., the Internet), online marketing (e-commerce) and customer relationship management (CRM) creating radical changes in the way companies interact with their customers. This has created completely new breeds of marketing models, but major progress has also taken place in existing types of marketing models. Handbook of Marketing Decision Models presents the state of the art in marketing decision models. The book deals with new modeling areas, such as customer relationship management, customer value and online marketing, as well as recent developments in other advertising, sales promotions, sales management, and competition are dealt with. New developments are in consumer decision models, models for return on marketing, marketing management support systems, and in special techniques such as time series and neural nets. |
| decision modeling and analysis: Spreadsheet Modeling and Decision Analysis Cliff T. Ragsdale, Lance Matheson, 1995 Valuable software, realistic examples, and fascinating topics . . . everything you need to master the most widely used management science techniques using Microsoft Excel is right here! Learning to make decisions in today's business world takes training and experience. Cliff Ragsdale--the respected innovator in the field of management science--is an outstanding guide to help you learn the skills you need, use Microsoft Excel for Windows to implement those skills, and gain the confidence to apply what you learn to real business situations. SPREADSHEET MODELING AND DECISION ANALYSIS gives you step-by-step instructions and annotated screen shots to make examples easy to follow. Plus, interesting sections called The World of Management Science show you how each topic has been applied in a real company. |
| decision modeling and analysis: Statistics, Data Analysis, and Decision Modeling James Robert Evans, David Louis Olson, 2003 This book covers basic concepts of business statistics, data analysis, and management science in a spreadsheet environment. Practical applications are emphasized throughout the book for business decision-making; a comprehensive database is developed, with marketing, financial, and production data already formatted on Excel worksheets. This shows how real data is used and decisions are made. Using Excel as the basic software, and including such add-ins as PHStat2, Crystal Ball, and TreePlan, this book covers a wide variety of topics related to business statistics: statistical thinking in business; displaying and summarizing data; random variables; sampling; regression analysis; forecasting; statistical quality control; risk analysis and Monte-Carlo simulation; systems simulation modeling and analysis; selection models and decision analysis; optimization modeling; and solving and analyzing optimization models. For those employed in the fields of quality control, management science, operations management, statistical science, and those who need to interpret data to make informed business decisions. |
| decision modeling and analysis: Metaheuristics for Business Analytics Abraham Duarte, Manuel Laguna, Rafael Marti, 2017-11-24 This essential metaheuristics tutorial provides descriptions and practical applications in the area of business analytics. It addresses key problems in predictive and prescriptive analysis, while also illustrating how problems that arise in business analytics can be modelled and how metaheuristics can be used to find high-quality solutions. Readers will be introduced to decision-making problems for which metaheuristics offer the most effective solution technique. The book not only shows business problem modelling on a spreadsheet but also how to design and create a Visual Basic for Applications code. Extra Material can be downloaded at http://extras.springer.com/978-3-319-68117-7. |
| decision modeling and analysis: Business Analytics for Decision Making Steven Orla Kimbrough, Hoong Chuin Lau, 2018-09-03 Business Analytics for Decision Making, the first complete text suitable for use in introductory Business Analytics courses, establishes a national syllabus for an emerging first course at an MBA or upper undergraduate level. This timely text is mainly about model analytics, particularly analytics for constrained optimization. It uses implementations that allow students to explore models and data for the sake of discovery, understanding, and decision making. Business analytics is about using data and models to solve various kinds of decision problems. There are three aspects for those who want to make the most of their analytics: encoding, solution design, and post-solution analysis. This textbook addresses all three. Emphasizing the use of constrained optimization models for decision making, the book concentrates on post-solution analysis of models. The text focuses on computationally challenging problems that commonly arise in business environments. Unique among business analytics texts, it emphasizes using heuristics for solving difficult optimization problems important in business practice by making best use of methods from Computer Science and Operations Research. Furthermore, case studies and examples illustrate the real-world applications of these methods. The authors supply examples in Excel®, GAMS, MATLAB®, and OPL. The metaheuristics code is also made available at the book's website in a documented library of Python modules, along with data and material for homework exercises. From the beginning, the authors emphasize analytics and de-emphasize representation and encoding so students will have plenty to sink their teeth into regardless of their computer programming experience. |
| decision modeling and analysis: Statistics, Data Analysis, and Decision Modeling James R. Evans, 2007 |
| decision modeling and analysis: Decision Modeling and Behavior in Complex and Uncertain Environments Tamar Kugler, J. Cole Smith, Terry Connolly, Young-Jun Son, 2008-07-20 This text examines new research at the interface of operations research, behavioral and cognitive sciences, and decision analysis. From the cognitive behaviorist who collects empirical evidence as to how people make decisions to the engineer and economist who are the consumers of such understanding, the reader encounters the familiar Traveling Salesman Problem and Prisoner's dilemma, how agricultural decisions are made in Argentina's Pampas region, and some social goals that come into play as an element of rational decision-making. In these 14 self-contained chapters, broad topics covered include the integration of decision analysis and behavioral models, innovations in behavioral models, exploring descriptive behavior models, and experimental studies. |
| decision modeling and analysis: Modeling in Medical Decision Making Giovanni Parmigiani, 2002-03 Describes Bayesian inference, Monte Carlo simulation, utility theory and gives case studies of their use. |
| decision modeling and analysis: Decision Modeling with Microsoft Excel Jeffrey H. Moore, Larry R. Weatherford, Lawrence R. Weatherford, 2001 CD-ROM contains: Premium Solver for Education -- Solver Table add-in software -- Extend LT 4.0 (simulation software) -- TreePlan -- GLP, a graphic visualization program -- Excel templates for in-text examples. |
| decision modeling and analysis: Multi-Criteria Decision Analysis in Management Behl, Abhishek, 2020-02-01 Multi-criteria decision making (MCDM) has been extensively used in diverse disciplines, with a variety of MCDM techniques used to solve complex problems. A primary challenge faced by research scholars is to decode these techniques using detailed step-by-step analysis with case studies and data sets. The scope of such work would help decision makers to understand the process of using MCDM techniques appropriately to solve complex issues without making mistakes. Multi-Criteria Decision Analysis in Management provides innovative insights into the rationale behind using MCDM techniques to solve decision-making problems and provides comprehensive discussions on these techniques from their inception, development, and growth to their advancements and applications. The content within this publication examines hybrid multicriteria models, value theory, and data envelopment. Ideal for researchers, management professionals, students, operations scholars, and academicians, this scholarly work supports and enhances the decision-making process. |
| decision modeling and analysis: Decision Analysis in Projects John R. Schuyler, 1996 |
| decision modeling and analysis: Business Process Management Mathias Weske, 2024 In this book, Mathias Weske details the complete business process lifecycle from process modeling to process enactment and process evaluation. After starting with the general foundations and abstractions in business process management, he introduces process modeling languages and process choreographies, as well as formal properties of processes and data. Eventually, he presents both traditional and advanced business process management architectures, covering, for example, workflow management systems, service-oriented architectures, and data-driven approaches. The 4th edition of his book contains significant updates, including a new section on directly follows graphs that play a crucial role in process mining. In addition, the core of declarative process modeling is introduced. The increasingly important role of data in business processes is addressed by a new section on data objects and data models in the data and decision chapter. To cover a recent trend in process automation, the enterprise systems architecture chapter now includes a section on robotic process automation. Mathias Weske argues that all communities involved need to have a common understanding of the different aspects of business process management. Hence his textbook is ideally suited for classes on business process management, information systems architecture, and workflow management alike. The accompanying website www.bpm-book.com contains further information and additional teaching material. |
| decision modeling and analysis: Community-Based Operations Research Michael P. Johnson, 2011-09-18 This edited volume is an introduction to diverse methods and applications in operations research focused on local populations and community-based organizations that have the potential to improve the lives of individuals and communities in tangible ways. The book's themes include: space, place and community; disadvantaged, underrepresented or underserved populations; international and transnational applications; multimethod, cross-disciplinary and comparative approaches and appropriate technology; and analytics. The book is comprised of eleven original submissions, a re-print of a 2007 article by Johnson and Smilowitz that introduces CBOR, and an introductory chapter that provides policy motivation, antecedents to CBOR in OR/MS, a theory of CBOR and a comprehensive review of the chapters. It is hoped that this book will provide a resource to academics and practitioners who seek to develop methods and applications that bridge the divide between traditional OR/MS rooted in mathematical models and newer streams in 'soft OR' that emphasize problem structuring methods, critical approaches to OR/MS and community engagement and capacity-building. |
| decision modeling and analysis: Decision Behaviour, Analysis and Support Simon French, John Maule, Nadia Papamichail, 2009-07-30 A multi-disciplinary exploration of how we can help decision makers to deliberate and make better decisions. |
| decision modeling and analysis: Multi-Criteria Decision Modelling Rahul Sindhwani, Punj Lata Singh, Bhawna Kumar, Varinder Kumar Mittal, J. Paulo Davim, 2021-08-01 This book examines Multi-Criteria Decision Modelling (MCDM) methodologies and facilitates diverse ways for strategic decision-making in a variety of practical applications. This book also provides a pragmatic foundation for solving real-life problems in different scenarios of emerging global markets. Multi-Criteria Decision Modelling: Applicational Techniques and Case Studies depicts the use of sensitivity analysis and modelling and includes case studies to understand and illustrate challenging concepts. It also offers step-by-step comprehensive methodologies for the utilization of MCDM to a variety of situations. The book deliberates ways for companies to use these methods to their advantage in order to achieve sustainability. Furthermore, it also presents an overview of the major streams of thought and provides a holistic view of the latest research and development trends in modelling and optimization. FEATURES Offers a stepwise comprehensive methodology for the application of MCDM to a variety of situations Presents an overview of the major streams of thought present in the MCDM technique Provides a holistic view of the latest research and development trends in the emerging markets in terms of modelling and optimization using MCDM for different industrial sectors Illuminates a practical foundation in order to provide a guide to address the problems of emerging markets Enlightens the ways for companies to use these methods to their advantage to be able to achieve sustainability This book is a guide for those performing decision analysis for academic purposes as well as for researchers aspiring to expand their knowledge on MCDM problem solving. |
| decision modeling and analysis: Ethnographic Decision Tree Modeling Christina H. Gladwin, 1989-09 Why do people in a certain group behave the way they do? And, more importantly, what specific criteria was used by the group in question? This book presents a method for answering these questions. |
| decision modeling and analysis: Multicriteria and Multiobjective Models for Risk, Reliability and Maintenance Decision Analysis Adiel Teixeira de Almeida, Cristiano Alexandre Virgínio Cavalcante, Marcelo Hazin Alencar, Rodrigo José Pires Ferreira, Adiel Teixeira de Almeida-Filho, Thalles Vitelli Garcez, 2015-07-01 This book integrates multiple criteria concepts and methods for problems within the Risk, Reliability and Maintenance (RRM) context. The concepts and foundations related to RRM are considered for this integration with multicriteria approaches. In the book, a general framework for building decision models is presented and this is illustrated in various chapters by discussing many different decision models related to the RRM context. The scope of the book is related to ways of how to integrate Applied Probability and Decision Making. In Applied Probability, this mainly includes: decision analysis and reliability theory, amongst other topics closely related to risk analysis and maintenance. In Decision Making, it includes a broad range of topics in MCDM (Multi-Criteria Decision Making) and MCDA (Multi-Criteria Decision Aiding; also known as Multi-Criteria Decision Analysis). In addition to decision analysis, some of the topics related to Mathematical Programming area are briefly considered, such as multiobjective optimization, since methods related to these topics have been applied to the context of RRM. The book addresses an innovative treatment for the decision making in RRM, thereby improving the integration of fundamental concepts from the areas of both RRM and decision making. This is accomplished by presenting an overview of the literature on decision making in RRM. Some pitfalls of decision models when applying them to RRM in practice are discussed and guidance on overcoming these drawbacks is offered. The procedure enables multicriteria models to be built for the RRM context, including guidance on choosing an appropriate multicriteria method for a particular problem faced in the RRM context. The book also includes many research advances in these topics. Most of the multicriteria decision models that are described are specific applications that have been influenced by this research and the advances in this field. Multicriteria and Multiobjective Models for Risk, Reliability and Maintenance Decision Analysis is implicitly structured in three parts, with 12 chapters. The first part deals with MCDM/A concepts methods and decision processes. The second part presents the main concepts and foundations of RRM. Finally the third part deals with specific decision problems in the RRM context approached with MCDM/A models. |
| decision modeling and analysis: Defense Modeling, Simulation, and Analysis National Research Council, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, Committee on Modeling and Simulation for Defense Transformation, 2006-09-22 Modeling, simulation, and analysis (MS&A) is a crucial tool for military affairs. MS&A is one of the announced pillars of a strategy for transforming the U.S. military. Yet changes in the enterprise of MS&A have not kept pace with the new demands arising from rapid changes in DOD processes and missions or with the rapid changes in the technology available to meet those demands. To help address those concerns, DOD asked the NRC to identify shortcomings in current practice of MS&A and suggest where and how they should be resolved. This report provides an assessment of the changing mission of DOD and environment in which it must operate, an identification of high-level opportunities for MS&A research to address the expanded mission, approaches for improving the interface between MS&A practitioners and decision makers, a discussion of training and continuing education of MS&A practitioners, and an examination of the need for coordinated military science research to support MS&A. |
| decision modeling and analysis: Decision Making Under Uncertainty Mykel J. Kochenderfer, 2015-07-24 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. |
| decision modeling and analysis: Network Meta-Analysis for Decision-Making Sofia Dias, A. E. Ades, Nicky J. Welton, Jeroen P. Jansen, Alexander J. Sutton, 2018-03-19 A practical guide to network meta-analysis with examples and code In the evaluation of healthcare, rigorous methods of quantitative assessment are necessary to establish which interventions are effective and cost-effective. Often a single study will not provide the answers and it is desirable to synthesise evidence from multiple sources, usually randomised controlled trials. This book takes an approach to evidence synthesis that is specifically intended for decision making when there are two or more treatment alternatives being evaluated, and assumes that the purpose of every synthesis is to answer the question for this pre-identified population of patients, which treatment is 'best'? A comprehensive, coherent framework for network meta-analysis (mixed treatment comparisons) is adopted and estimated using Bayesian Markov Chain Monte Carlo methods implemented in the freely available software WinBUGS. Each chapter contains worked examples, exercises, solutions and code that may be adapted by readers to apply to their own analyses. This book can be used as an introduction to evidence synthesis and network meta-analysis, its key properties and policy implications. Examples and advanced methods are also presented for the more experienced reader. Methods used throughout this book can be applied consistently: model critique and checking for evidence consistency are emphasised. Methods are based on technical support documents produced for NICE Decision Support Unit, which support the NICE Methods of Technology Appraisal. Code presented is also the basis for the code used by the ISPOR Task Force on Indirect Comparisons. Includes extensive carefully worked examples, with thorough explanations of how to set out data for use in WinBUGS and how to interpret the output. Network Meta-Analysis for Decision Making will be of interest to decision makers, medical statisticians, health economists, and anyone involved in Health Technology Assessment including the pharmaceutical industry. |
| decision modeling and analysis: Modeling Markets Peter S.H. Leeflang, Jaap E. Wieringa, Tammo H.A. Bijmolt, Koen H. Pauwels, 2014-11-12 This book is about how models can be developed to represent demand and supply on markets, where the emphasis is on demand models. Its primary focus is on models that can be used by managers to support marketing decisions. Modeling Markets presents a comprehensive overview of the tools and methodologies that managers can use in decision making. It has long been known that even simple models outperform judgments in predicting outcomes in a wide variety of contexts. More complex models potentially provide insights about structural relations not available from casual observations. In this book, the authors present a wealth of insights developed at the forefront of the field, covering all key aspects of specification, estimation, validation and use of models. The most current insights and innovations in quantitative marketing are presented, including in-depth discussion of Bayesian estimation methods. Throughout the book, the authors provide examples and illustrations. This book will be of interest to researchers, analysts, managers and students who want to understand, develop or use models of marketing phenomena. |
| decision modeling and analysis: Tools and Techniques for Economic Decision Analysis Stankovi?, Jelena, Delias, Pavlos, Marinkovi?, Sr?an, Rochhia, Sylvie, 2016-10-31 The success of any business relies heavily on the evaluation and improvement on current strategies and processes. Such progress can be facilitated by implementing more effective decision-making systems. Tools and Techniques for Economic Decision Analysis provides a thorough overview of decision models and methodologies in the context of business economics. Highlighting a variety of relevant issues on finance, economic policy, and firms and networks, this book is an ideal reference source for managers, professionals, students, and academics interested in emerging developments for decision analysis. |
| decision modeling and analysis: Introduction to Decision Analysis David C. Skinner, 2009 This book is the most practical and thought-provoking step-by-step guide to making better decisions that is available today! Proven techniques and solid experience are the foundation for this classic text, which was written for the manager and for the decision analysis practitioner!-- |
| decision modeling and analysis: Modeling Human and Organizational Behavior Panel on Modeling Human Behavior and Command Decision Making: Representations for Military Simulations, Board on Human-Systems Integration, Division of Behavioral and Social Sciences and Education, National Research Council, 1998-08-14 Simulations are widely used in the military for training personnel, analyzing proposed equipment, and rehearsing missions, and these simulations need realistic models of human behavior. This book draws together a wide variety of theoretical and applied research in human behavior modeling that can be considered for use in those simulations. It covers behavior at the individual, unit, and command level. At the individual soldier level, the topics covered include attention, learning, memory, decisionmaking, perception, situation awareness, and planning. At the unit level, the focus is on command and control. The book provides short-, medium-, and long-term goals for research and development of more realistic models of human behavior. |
| decision modeling and analysis: Value of Information in the Earth Sciences Jo Eidsvik, Tapan Mukerji, Debarun Bhattacharjya, 2015-11-19 Gathering the right kind and the right amount of information is crucial for any decision-making process. This book presents a unified framework for assessing the value of potential data gathering schemes by integrating spatial modelling and decision analysis, with a focus on the Earth sciences. The authors discuss the value of imperfect versus perfect information, and the value of total versus partial information, where only subsets of the data are acquired. Concepts are illustrated using a suite of quantitative tools from decision analysis, such as decision trees and influence diagrams, as well as models for continuous and discrete dependent spatial variables, including Bayesian networks, Markov random fields, Gaussian processes, and multiple-point geostatistics. Unique in scope, this book is of interest to students, researchers and industry professionals in the Earth and environmental sciences, who use applied statistics and decision analysis techniques, and particularly to those working in petroleum, mining, and environmental geoscience. |
| decision modeling and analysis: Portfolio Decision Analysis Ahti Salo, Jeffrey Keisler, Alec Morton, 2011-08-12 Portfolio Decision Analysis: Improved Methods for Resource Allocation provides an extensive, up-to-date coverage of decision analytic methods which help firms and public organizations allocate resources to 'lumpy' investment opportunities while explicitly recognizing relevant financial and non-financial evaluation criteria and the presence of alternative investment opportunities. In particular, it discusses the evolution of these methods, presents new methodological advances and illustrates their use across several application domains. The book offers a many-faceted treatment of portfolio decision analysis (PDA). Among other things, it (i) synthesizes the state-of-play in PDA, (ii) describes novel methodologies, (iii) fosters the deployment of these methodologies, and (iv) contributes to the strengthening of research on PDA. Portfolio problems are widely regarded as the single most important application context of decision analysis, and, with its extensive and unique coverage of these problems, this book is a much-needed addition to the literature. The book also presents innovative treatments of new methodological approaches and their uses in applications. The intended audience consists of practitioners and researchers who wish to gain a good understanding of portfolio decision analysis and insights into how PDA methods can be leveraged in different application contexts. The book can also be employed in courses at the post-graduate level. |
| decision modeling and analysis: The Chess Mysteries of Sherlock Holmes Raymond M. Smullyan, 2012 Join Holmes and Watson as they examine interrupted games to deduce prior moves. A series of increasingly complex chess mysteries culminates in a double murder perpetrated by Professor Moriarty. The master sleuth instructs his companion (and us) in the intricacies of retrograde analysis; readers need only a knowledge of how the pieces move. |
DECISION Definition & Meaning - Merriam-Webster
The meaning of DECISION is the act or process of deciding. How to use decision in a sentence.
DECISION | English meaning - Cambridge Dictionary
DECISION definition: 1. a choice that you make about something after thinking about several possibilities: …
DECISION Definition & Meaning | Dictionary.com
Decision definition: the act or process of deciding; deciding; determination, as of a question or doubt, by making a …
decision noun - Definition, pictures, pronunciation and u…
Definition of decision noun in Oxford Advanced American Dictionary. Meaning, pronunciation, picture, …
Decision - definition of decision by The Free Dictionary
1. the act or process of deciding. 2. the act of making up one's mind: a difficult decision. 3. something that is …
DECISION Definition & Meaning - Merriam-Webster
The meaning of DECISION is the act or process of deciding. How to use decision in a sentence.
DECISION | English meaning - Cambridge Dictionary
DECISION definition: 1. a choice that you make about something after thinking about several possibilities: 2. the…. Learn more.
DECISION Definition & Meaning | Dictionary.com
Decision definition: the act or process of deciding; deciding; determination, as of a question or doubt, by making a judgment.. See examples of DECISION used in a sentence.
decision noun - Definition, pictures, pronunciation and usage …
Definition of decision noun in Oxford Advanced American Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
Decision - definition of decision by The Free Dictionary
1. the act or process of deciding. 2. the act of making up one's mind: a difficult decision. 3. something that is decided; resolution. 4. a judgment, as one pronounced by a court. 5. the …
What does Decision mean? - Definitions.net
What does Decision mean? This dictionary definitions page includes all the possible meanings, example usage and translations of the word Decision. A choice or judgement. Firmness of …
decision - Wiktionary, the free dictionary
Jun 7, 2025 · (choice or judgment): Most often, to decide something is to make a decision; however, other possibilities exist as well. Many verbs used with destination or conclusion, such …
SUPREME COURT OF THE UNITED STATES
3 days ago · judgment” rule articulated by the Eighth Circuit in its 1982 decision in Monahan, in which the Eighth Circuit reasoned that to prove dis-crimination under the Rehabilitation Act in …
Decision-making - Wikipedia
In psychology, decision-making (also spelled decision making and decisionmaking) is regarded as the cognitive process resulting in the selection of a belief or a course of action among several …
Decision - Definition, Meaning & Synonyms - Vocabulary.com
To make a decision is to make up your mind about something. To act with decision is to proceed with determination, which might be a natural character trait.
