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| ey 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. |
| ey decision modeling and analysis: Applied Decision Support with Soft Computing Xinghuo Yu, 2012-12-06 Soft computing has provided sophisticated methodologies for the development of intelligent decision support systems. Fast advances in soft computing technologies, such as fuzzy logic and systems, artificial neural networks and evolutionary computation, have made available powerful problem representation and modelling paradigms, and learning and optimisation mechanisms for addressing modern decision making issues. This book provides a comprehensive coverage of up-to-date conceptual frameworks in broadly perceived decision support systems and successful applications. Different from other existing books, this volume predominately focuses on applied decision support with soft computing. Areas covered include planning, management finance and administration in both the private and public sectors. |
| ey decision modeling and analysis: Advances in Configural Frequency Analysis Alexander von Eye, Patrick Mair, Eun-Young Mun, 2010-04-20 Using real-world data examples, this authoritative book shows how to use the latest configural frequency analysis (CFA) techniques to analyze categorical data. Some of the techniques are presented here for the first time. In contrast to methods that focus on relationships among variables, such as log-linear modeling, CFA allows researchers to evaluate differences and change at the level of individual cells in a table. Illustrated are ways to identify and test for cell configurations that are either consistent with or contrary to hypothesized patterns (the types and antitypes of CFA); control for potential covariates that might influence observed results; develop innovative prediction models; address questions of moderation and mediation; and analyze intensive longitudinal data. The book also describes free software applications for executing CFA. |
| ey decision modeling and analysis: Management , 1979 |
| ey decision modeling and analysis: Modeling Individual Differences in Perceptual Decision Making Joseph W. Houpt, Cheng-Ta Yang, James T. Townsend, 2017-01-18 To deal with the abundant amount of information in the environment in order to achieve our goals, human beings adopt a strategy to accumulate some information and filter out other information to ultimately make decisions. Since the development of cognitive science in the 1960s, researchers have been interested in understanding how human beings process and accumulate information for decision-making. Researchers have conducted extensive behavioral studies and applied a wide range of modeling tools to study human behavior in simple-detection tasks and two-choice decision tasks (e.g., discrimination, classification). In general, researchers often assume that the manner in which information is processed for decision-making is invariant across individuals given a particular experimental context. Independent variables, including speed-accuracy instructions, stimulus properties (i.e., intensity), and characteristics of the participants (i.e., aging, cognitive ability) are assumed to affect the parameters in a model (i.e., speed of information accumulation, response bias) but not the way that participants process information (e.g., the order of information processing). Given these assumptions, much modeling has been accomplished based on the grouped data, rather than the individual data. However, a growing number of studies have demonstrated that there were individual differences in the perceptual decision process. In the same task context, different groups of the participants may process information in different manners. The capacity and architecture of the decision mechanism were found to vary across individuals, implying that humans’ decision strategies can vary depending on the context to maximize their performance. In this special issue, we focused on a particular subset of cognitive models, particularly accumulator models, multinomial processing trees and systems factorial technology (SFT) as applied to perceptual decision making. The motivation for the focus on perceptual decision-making is threefold. Empirical studies of perception have grown out of a history of making a large number of observations for each individual so as to achieve precise estimates of each individual’s performance. This type of data, rather than a small number of observations per individual, is most amenable to achieving precision in individual-level and group-level cognitive modeling. Second, the interaction between the acquisition of perceptual information and the decisions based on that information (to the extent that those processes are distinguishable) offers rich data for scientific exploration. Finally, there is an increasing interest in the practical application of individual variation in perceptual ability, whether to inform perceptual training and expertise, or to guide personnel decisions. Although these practical applications are beyond the scope of this issue, we hope that the research presented herein may serve as the foundation for future endeavors in that domain. |
| ey decision modeling and analysis: NASA Tech Briefs , 2007 |
| ey decision modeling and analysis: Energy Research Abstracts , 1981 |
| ey decision modeling and analysis: Cloud Data Centers and Cost Modeling Caesar Wu, Rajkumar Buyya, 2015-02-27 Cloud Data Centers and Cost Modeling establishes a framework for strategic decision-makers to facilitate the development of cloud data centers. Just as building a house requires a clear understanding of the blueprints, architecture, and costs of the project; building a cloud-based data center requires similar knowledge. The authors take a theoretical and practical approach, starting with the key questions to help uncover needs and clarify project scope. They then demonstrate probability tools to test and support decisions, and provide processes that resolve key issues. After laying a foundation of cloud concepts and definitions, the book addresses data center creation, infrastructure development, cost modeling, and simulations in decision-making, each part building on the previous. In this way the authors bridge technology, management, and infrastructure as a service, in one complete guide to data centers that facilitates educated decision making. - Explains how to balance cloud computing functionality with data center efficiency - Covers key requirements for power management, cooling, server planning, virtualization, and storage management - Describes advanced methods for modeling cloud computing cost including Real Option Theory and Monte Carlo Simulations - Blends theoretical and practical discussions with insights for developers, consultants, and analysts considering data center development |
| ey decision modeling and analysis: Decision Making and Forecasting Kneale T. Marshall, Robert M. Oliver, 1995 This book discusses how to design the most important features of realistic decision problems into analytical models that reveal their structure and give insight. Emphasis is on model formulation using graphical techniques with influence diagrams and decision trees. Decision Making and Forecasting shows how forecasting must be integrated with decision making in a coherent manner and makes frequent use of the economic value of forecasts. |
| ey decision modeling and analysis: Risk Modeling for Hazards and Disasters Gero Michel, 2017-08-29 Risk Modeling for Hazards and Disasters covers all major aspects of catastrophe risk modeling, from hazards through to financial analysis. It explores relevant new science in risk modeling, indirect losses, assessment of impact and consequences to insurance losses, and current changes in risk modeling practice, along with case studies. It also provides further insight into the shortcomings of current models and examines model risk and ideas to diversify risk assessment. Risk Modeling for Hazards and Disasters instructs readers on how to assess, price and then hedge the losses from natural and manmade catastrophes. This book reviews current model development and science and explains recent changes in the catastrophe modeling space, including new initiatives covering uncertainty and big data in the assessment of risk for insurance pricing and portfolio management. Edited by a leading expert in both hazards and risk, this book is authored by a global panel including major modeling vendors, modeling consulting firms, and well-known catastrophe modeling scientists. Risk Modeling for Hazards and Disasters provides important insight into how models are used to price and manage risk. - Includes high profile case studies such as the Newcastle earthquake, Hurricane Andrew and Hurricane Katrina - Provides crucial information on new ideas and platforms that will help address the new demands for risk management and catastrophe risk reporting - Presents the theory and practice needed to know how models are created and what is and what is not important in the modeling process - Covers relevant new science in risk modeling, indirect losses, assessment of impact and consequences to insurance losses, and current changes in risk modeling practice, along with case studies |
| ey decision modeling and analysis: Methods in Comparative Effectiveness Research Constantine Gatsonis, Sally C. Morton, 2017-02-24 Comparative effectiveness research (CER) is the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care (IOM 2009). CER is conducted to develop evidence that will aid patients, clinicians, purchasers, and health policy makers in making informed decisions at both the individual and population levels. CER encompasses a very broad range of types of studies—experimental, observational, prospective, retrospective, and research synthesis. This volume covers the main areas of quantitative methodology for the design and analysis of CER studies. The volume has four major sections—causal inference; clinical trials; research synthesis; and specialized topics. The audience includes CER methodologists, quantitative-trained researchers interested in CER, and graduate students in statistics, epidemiology, and health services and outcomes research. The book assumes a masters-level course in regression analysis and familiarity with clinical research. |
| ey decision modeling and analysis: Simulation Modeling and Analysis with Expertfit Software Averill Law, 2006-07-21 Since the publication of the first edition in 1982, the goal of Simulation Modeling and Analysis has always been to provide a comprehensive, state-of-the-art, and technically correct treatment of all important aspects of a simulation study. The book strives to make this material understandable by the use of intuition and numerous figures, examples, and problems. It is equally well suited for use in university courses, simulation practice, and self study. The book is widely regarded as the “bible” of simulation and now has more than 100,000 copies in print. The book can serve as the primary text for a variety of courses; for example: • A first course in simulation at the junior, senior, or beginning-graduate-student level in engineering, manufacturing, business, or computer science (Chaps. 1 through 4, and parts of Chaps. 5 through 9). At the end of such a course, the students will be prepared to carry out complete and effective simulation studies, and to take advanced simulation courses. • A second course in simulation for graduate students in any of the above disciplines (most of Chaps. 5 through 12). After completing this course, the student should be familiar with the more advanced methodological issues involved in a simulation study, and should be prepared to understand and conduct simulation research. • An introduction to simulation as part of a general course in operations research or management science (part of Chaps. 1, 3, 5, 6, and 9). |
| ey decision modeling and analysis: Management Strategies to Survive in a Competitive Environment Hasan Dincer, Serhat Yüksel, 2021-04-27 Competition is present for almost every sector nowadays. Therefore, it is vital for companies to develop a set of strategies in order to survive in the competitive environment of a globalized world. This book discusses how and why not every strategy is appropriate for every sector. The volume offers a qualified and comprehensive analysis to determine effective competitive strategies taking into account the many different factors that affect company performance. |
| ey decision modeling and analysis: Energy , 1978 |
| ey decision modeling and analysis: Selected Water Resources Abstracts , |
| ey decision modeling and analysis: Energy Abstracts for Policy Analysis , 1981 |
| ey decision modeling and analysis: Pharmacoeconomics Renee J. G. Arnold, 2016-04-19 The pharmaceutical industry is almost boundless in its ability to supply new drug therapies, but how does one decide which are the best medicines to use within restricted budgets? With particular emphasis on modeling, methodologies, data sources, and application to real-world dilemmas, Pharmacoeconomics: From Theory to Practice provides an introduc |
| ey decision modeling and analysis: Probability, Statistics, And Decision Making In The Atmospheric Sciences Allan Murphy, Richard W. Katz, 2019-07-11 Methodology drawn from the fields of probability. statistics and decision making plays an increasingly important role in the atmosphericsciences. both in basic and applied research and in experimental and operational studies. Applications of such methodology can be found in almost every facet of the discipline. from the most theoretical and global (e.g., atmospheric predictability. global climate modeling) to the most practical and local (e.g., crop-weather modeling forecast evaluation). Almost every issue of the multitude of journals published by the atmospheric sciences community now contain some or more papers involving applications of concepts and/or methodology from the fields of probability and statistics. Despite the increasingly pervasive nature of such applications. very few book length treatments of probabilistic and statistical topics of particular interest to atmospheric scientists have appeared (especially inEnglish) since the publication of the pioneering works of Brooks andCarruthers (Handbook of Statistical Methods in Meteorology) in 1953 and Panofsky and Brier-(some Applications of)statistics to Meteor) in 1958. As a result. many relatively recent developments in probability and statistics are not well known to atmospheric scientists and recent work in active areas of meteorological research involving significant applications of probabilistic and statistical methods are not familiar to the meteorological community as a whole. |
| ey decision modeling and analysis: Personalized Anaesthesia Pedro L. Gambús, Jan F. A. Hendrickx, 2020-02-06 Presents a modern vision of anaesthesia, integrating technology and knowledge, to change how anaesthesia is taught and practised. |
| ey decision modeling and analysis: Sustainable Supply Chains: Strategies, Issues, and Models Usha Ramanathan, Ramakrishnan Ramanathan, 2020-07-17 This book discusses supply chain issues and models with examples from actual case studies. Recent advances in sustainability, supply chains and technologies have brought promising potential for the management of sustainable global and local supply chains. While most of the current literature seem to consider developments in the field of sustainable supply chains and in the field of Industry 4.0 as two distinct entities, this book attempts to explore the synergy in bringing these two distinct fields together. The book features chapters on management of sustainability and industry 4.0 on supply chains as a whole, with several case studies on issues related to the application of sustainable supply chains in specific application sectors. They employ mathematical modeling and statistical analyses, as well as descriptive qualitative studies. They cover a range of application areas including multiple sectors (restaurant, manufacturing, logistics, furniture, food and insurance), domains (supply chains, logistics, marketing, and reverse logistics) and multiple country contexts (UK and India). The potential links between sustainability and the recent technological innovations from Industry 4.0 have been explored in detail. The book offers a valuable tool for managerial decision-making on the current practice and future potential on the use of Industry 4.0 tools for sustainable supply chains to facilitate competitive advantage with case studies in various industry sectors. In addition, some intriguing mathematical models will appeal to students and researchers interested in modeling the logistics process and the application of evolutionary game theory for integrating the social and economic aspects of sustainable supply chains. Some of these supply chain issues have been addressed in a previous book by the Editors. |
| ey decision modeling and analysis: Inventory of Federal Energy-related Environment and Safety Research for FY 1978: Project listings and indexes , 1979 |
| ey decision modeling and analysis: Research Awards Index , 1978 |
| ey decision modeling and analysis: Research Grants Index National Institutes of Health (U.S.). Division of Research Grants, 1971 |
| ey decision modeling and analysis: Index Medicus , 2004 Vols. for 1963- include as pt. 2 of the Jan. issue: Medical subject headings. |
| ey decision modeling and analysis: Cumulated Index Medicus , 1995 |
| ey decision modeling and analysis: Artificial Intelligence and Image Processing in Medical Imaging Walid A. Zgallai, Dilber Uzun Ozsahin, 2024-01-18 Artificial Intelligence and Image Processing in Medical Imaging deals with the applications of processing medical images with a view of improving the quality of the data in order to facilitate better decision- making. The book covers the basics of medical imaging and the fundamentals of image processing. It explains spatial and frequency domain applications of image processing, introduces image compression techniques and their applications, and covers image segmentation techniques and their applications. The book includes object detection and classification applications and provides an overall background to statistical analysis in biomedical systems. The role of Machine Learning, including Neural Networks, Deep Learning, and the implications of the expansion of artificial intelligence is also covered. With contributions from prominent researchers worldwide, this book provides up-to-date and comprehensive coverage of AI applications in image processing where readers will find the latest information with clear examples and illustrations. - Provides the latest comprehensive coverage of the developments of AI techniques and the principles of medical imaging - Covers all aspects of medical imaging, from acquisition, the use of hardware and software, image analysis and implementation of AI in problem solving - Provides examples of medical imaging and how they're processed, including segmentation, classification, and detection |
| ey decision modeling and analysis: Scientific and Technical Aerospace Reports , 1992 |
| ey decision modeling and analysis: Digital Transformation of the Design, Construction and Management Processes of the Built Environment Bruno Daniotti, Marco Gianinetto, Stefano Della Torre, 2019-12-30 This open access book focuses on the development of methods, interoperable and integrated ICT tools, and survey techniques for optimal management of the building process. The construction sector is facing an increasing demand for major innovations in terms of digital dematerialization and technologies such as the Internet of Things, big data, advanced manufacturing, robotics, 3D printing, blockchain technologies and artificial intelligence. The demand for simplification and transparency in information management and for the rationalization and optimization of very fragmented and splintered processes is a key driver for digitization. The book describes the contribution of the ABC Department of the Polytechnic University of Milan (Politecnico di Milano) to R&D activities regarding methods and ICT tools for the interoperable management of the different phases of the building process, including design, construction, and management. Informative case studies complement the theoretical discussion. The book will be of interest to all stakeholders in the building process – owners, designers, constructors, and faculty managers – as well as the research sector. |
| ey decision modeling and analysis: Hydrology and Water Resources in Arizona and the Southwest , 2006 |
| ey decision modeling and analysis: Principles of Marketology, Volume 2 Hashem Aghazadeh, 2017-04-28 Principles of Marketology, Volume 2 focuses on the practical aspect and demonstrates the applications of marketology referring to market orientation, internal marketing, business, market and competitive analysis concepts and techniques. Then the modern marketology and its developments in the future are discussed. At the of this volume as the appendix, a handbook of marketology is presented in which a practical manual including simple and summarized descriptions of different needed parts and worksheets for executing marketology in an organization is depicted. |
| ey decision modeling and analysis: Inventory of Federal Energy-related Environment and Safety Research for ... , 1979 |
| ey decision modeling and analysis: Peopling the Landscape of Çatalhöyük Ian Hodder, 2020-11-01 This volume reports on the ways in which humans engaged in their material and biotic environments at Çatalhöyük, using a wide range of archaeological evidence. This volume also summarizes work on the skeletal remains recovered from the site, as well as analytical research on isotopes and aDNA. |
| ey decision modeling and analysis: Simulation Modeling and Analysis with ARENA Tayfur Altiok, Benjamin Melamed, 2010-07-26 Simulation Modeling and Analysis with Arena is a highly readable textbook which treats the essentials of the Monte Carlo discrete-event simulation methodology, and does so in the context of a popular Arena simulation environment. It treats simulation modeling as an in-vitro laboratory that facilitates the understanding of complex systems and experimentation with what-if scenarios in order to estimate their performance metrics. The book contains chapters on the simulation modeling methodology and the underpinnings of discrete-event systems, as well as the relevant underlying probability, statistics, stochastic processes, input analysis, model validation and output analysis. All simulation-related concepts are illustrated in numerous Arena examples, encompassing production lines, manufacturing and inventory systems, transportation systems, and computer information systems in networked settings. - Introduces the concept of discrete event Monte Carlo simulation, the most commonly used methodology for modeling and analysis of complex systems - Covers essential workings of the popular animated simulation language, ARENA, including set-up, design parameters, input data, and output analysis, along with a wide variety of sample model applications from production lines to transportation systems - Reviews elements of statistics, probability, and stochastic processes relevant to simulation modeling |
| ey decision modeling and analysis: Interpretable Machine Learning Christoph Molnar, 2020 This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project. |
| ey decision modeling and analysis: Integrated Science in Digital Age 2020 Tatiana Antipova, 2020-05-26 This book presents the proceedings of the 2020 International Conference on Integrated Science in Digital Age, which was jointly supported by the Institute of Certified Specialists (Russia) and Springer, and was held on May 1–3, 2020. The conference provided an international forum for researchers and practitioners to present and discuss the latest innovations, trends, results, experiences and concerns in the various areas of integrated science in the digital age. The main goal of the conference was to efficiently disseminate original findings in the natural and social sciences, covering topics such as blockchain & cryptocurrency; computer law & security; digital accounting & auditing; digital business & finance; digital economics; digital education; digital engineering; machine learning; smart cities in the digital age; health policy & management; and information management. |
| ey decision modeling and analysis: ERDA Energy Research Abstracts United States. Energy Research and Development Administration, 1977 |
| ey decision modeling and analysis: ERDA Energy Research Abstracts United States. Energy Research and Development Administration. Technical Information Center, 1977 |
| ey decision modeling and analysis: An Introduction to Stochastic Modeling Howard M. Taylor, Samuel Karlin, 2014-05-10 An Introduction to Stochastic Modeling provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance. This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful. |
| ey decision modeling and analysis: Inventory of Federal Energy-related Environment and Safety Research for FY 1978 , 1979 |
| ey decision modeling and analysis: Energy , 1980 A selection of annotated references to unclassified reports and journal articles that were introduced into the NASA scientific and technical information system and announced in Scientific and technical aerospace reports (STAR) and International aerospace abstracts (IAA). |
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EY, [6] [7] previously known as Ernst & Young, is a multinational professional services network based in London, United Kingdom. [8] Along with Deloitte, KPMG and PwC, it is one of the Big …
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EY helps clients create long-term value for all stakeholders. Enabled by data and technology, our services and solutions provide trust through assurance and help clients transform, grow and …
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At EY, we develop you with future-focused skills, and equip you with world-class experiences. We empower you in a flexible environment, and fuel you and your extraordinary talents in a …
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At EY, we empower our people with the right mindsets and skills to navigate what’s next, become the transformative leaders the world needs, pursue careers as unique as they are, and build …
Ernst & Young - Wikipedia
EY, [6] [7] previously known as Ernst & Young, is a multinational professional services network based in London, United Kingdom. [8] Along with Deloitte, KPMG and PwC, it is one of the Big …
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