Presents information to create a trade-off analysis framework for use in government and commercial acquisition environments This book presents a decision management process based on decision theory and cost analysis best practices aligned with the ISO/IEC 15288, the Systems Engineering Handbook, and the Systems Engineering Body of Knowledge. It provides a sound trade-off analysis framework to generate the tradespace and evaluate value and risk to support system decision-making throughout the life cycle. Trade-off analysis and risk analysis techniques are examined. The authors present an integrated value trade-off and risk analysis framework based on decision theory. These trade-off analysis concepts are illustrated in the different life cycle stages using multiple examples from defense and commercial domains. Provides techniques to identify and structure stakeholder objectives and creative, doable alternatives Presents the advantages and disadvantages of tradespace creation and exploration techniques for trade-off analysis of concepts, architectures, design, operations, and retirement Covers the sources of uncertainty in the system life cycle and examines how to identify, assess, and model uncertainty using probability Illustrates how to perform a trade-off analysis using the INCOSE Decision Management Process using both deterministic and probabilistic techniques Trade-off Analytics: Creating and Exploring the System Tradespace is written for upper undergraduate students and graduate students studying systems design, systems engineering, industrial engineering and engineering management. This book also serves as a resource for practicing systems designers, systems engineers, project managers, and engineering managers. Gregory S. Parnell, PhD, is a Research Professor in the Department of Industrial Engineering at the University of Arkansas. He is also a senior principal with Innovative Decisions, Inc., a decision and risk analysis firm and has served as Chairman of the Board. Dr. Parnell has published more than 100 papers and book chapters and was lead editor of Decision Making for Systems Engineering and Management, Wiley Series in Systems Engineering (2nd Ed, Wiley 2011) and lead author of the Handbook of Decision Analysis (Wiley 2013). He is a fellow of INFORMS, the INCOSE, MORS, and the Society for Decision Professionals.

Presents information to create a trade-off analysis framework for use in government and commercial acquisition environments This book presents a decision management process based on decision theory and cost analysis best practices aligned with the ISO/IEC 15288, the Systems Engineering Handbook, and the Systems Engineering Body of Knowledge. It provides a sound trade-off analysis framework to generate the tradespace and evaluate value and risk to support system decision-making throughout the life cycle. Trade-off analysis and risk analysis techniques are examined. The authors present an integrated value trade-off and risk analysis framework based on decision theory. These trade-off analysis concepts are illustrated in the different life cycle stages using multiple examples from defense and commercial domains. Provides techniques to identify and structure stakeholder objectives and creative, doable alternatives Presents the advantages and disadvantages of tradespace creation and exploration techniques for trade-off analysis of concepts, architectures, design, operations, and retirement Covers the sources of uncertainty in the system life cycle and examines how to identify, assess, and model uncertainty using probability Illustrates how to perform a trade-off analysis using the INCOSE Decision Management Process using both deterministic and probabilistic techniques Trade-off Analytics: Creating and Exploring the System Tradespace is written for upper undergraduate students and graduate students studying systems design, systems engineering, industrial engineering and engineering management. This book also serves as a resource for practicing systems designers, systems engineers, project managers, and engineering managers. Gregory S. Parnell, PhD, is a Research Professor in the Department of Industrial Engineering at the University of Arkansas. He is also a senior principal with Innovative Decisions, Inc., a decision and risk analysis firm and has served as Chairman of the Board. Dr. Parnell has published more than 100 papers and book chapters and was lead editor of Decision Making for Systems Engineering and Management, Wiley Series in Systems Engineering (2nd Ed, Wiley 2011) and lead author of the Handbook of Decision Analysis (Wiley 2013). He is a fellow of INFORMS, the INCOSE, MORS, and the Society for Decision Professionals.

This study assesses the potential of new technology to reduce logistics support requirements for future Army combat systems. It describes and recommends areas of research and technology development in which the Army should invest now to field systems that will reduce logistics burdens and provide desired capabilities for an "Army After Next (AAN) battle force" in 2025.

Algorithms have made our lives more efficient and entertaining--but not without a significant cost. Can we design a better future, one in which societial gains brought about by technology are balanced with the rights of citizens? The Ethical Algorithm offers a set of principled solutions based on the emerging and exciting science of socially aware algorithm design.

Health inequalities blight lives, generate enormous costs, and exist everywhere. This book is the definitive all-in-one guide for anyone who wishes to learn about, commission, and use distributional cost-effectiveness analysis to promote both equity and efficiency in health and healthcare.

Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.

Optimize the decisions that define your code by exploring the common mistakes and intentional tradeoffs made by expert developers. In Software Mistakes and Tradeoffs you will learn how to: Reason about your systems to make intuitive and better design decisions Understand consequences and how to balance tradeoffs Pick the right library for your problem Thoroughly analyze all of your service’s dependencies Understand delivery semantics and how they influence distributed architecture Design and execute performance tests to detect code hot paths and validate a system’s SLA Detect and optimize hot paths in your code to focus optimization efforts on root causes Decide on a suitable data model for date/time handling to avoid common (but subtle) mistakes Reason about compatibility and versioning to prevent unexpected problems for API clients Understand tight/loose coupling and how it influences coordination of work between teams Clarify requirements until they are precise, easily implemented, and easily tested Optimize your APIs for friendly user experience Code performance versus simplicity. Delivery speed versus duplication. Flexibility versus maintainability—every decision you make in software engineering involves balancing tradeoffs. In Software Mistakes and Tradeoffs you’ll learn from costly mistakes that Tomasz Lelek and Jon Skeet have encountered over their impressive careers. You’ll explore real-world scenarios where poor understanding of tradeoffs lead to major problems down the road, so you can pre-empt your own mistakes with a more thoughtful approach to decision making. Learn how code duplication impacts the coupling and evolution speed of your systems, and how simple-sounding requirements can have hidden nuances with respect to date and time information. Discover how to efficiently narrow your optimization scope according to 80/20 Pareto principles, and ensure consistency in your distributed systems. You’ll soon have built up the kind of knowledge base that only comes from years of experience. About the technology Every step in a software project involves making tradeoffs. When you’re balancing speed, security, cost, delivery time, features, and more, reasonable design choices may prove problematic in production. The expert insights and relatable war stories in this book will help you make good choices as you design and build applications. About the book Software Mistakes and Tradeoffs explores real-world scenarios where the wrong tradeoff decisions were made and illuminates what could have been done differently. In it, authors Tomasz Lelek and Jon Skeet share wisdom based on decades of software engineering experience, including some delightfully instructive mistakes. You’ll appreciate the specific tips and practical techniques that accompany each example, along with evergreen patterns that will change the way you approach your next projects. What's inside How to reason about your software systematically How to pick tools, libraries, and frameworks How tight and loose coupling affect team coordination Requirements that are precise, easy to implement, and easy to test About the reader For mid- and senior-level developers and architects who make decisions about software design and implementation. About the author Tomasz Lelek works daily with a wide range of production services, architectures, and JVM languages. A Google engineer and author of C# in Depth, Jon Skeet is famous for his many practical contributions to Stack Overflow.