A collection of papers that explore of the use of modeling in military planning. Explores how modeling applies to a variety forms of military engagement including ground, sea, air and nuclear warfare.

A selective review of modern decision science and implications for decision-support systems. The study suggests ways to synthesize lessons from research on heuristics and biases with those from "naturalistic research." It also discusses modern tools, such as increasingly realistic simulations, multiresolution modeling, and exploratory analysis, which can assist decisionmakers in choosing strategies that are flexible, adaptive, and robust.

Information operations involve the use of military information and how it is gathered, manipulated, and fused. It includes such critical functions as intelligence, surveillance, reconnaissance, command and control, communications, and precision navigation. Separating myth from reality, this authoritative resource provides military professionals with a current and comprehensive understanding of information warfare operations planning, including offensive, defensive, and influence operations. The book identifies the features of information operations that differ from traditional military operations and reveals why this discipline is more important now than ever before. Professionals discover new planning tools that have been brought together under a single platform to become the next Information Operations Planning Tool for the U.S. Department of Defence. Additionally, the book defines and identifies new threats and opportunities, and explains why the U.S. is not yet winning the war for the minds.

The author discusses fundamental principles and techniques governing the design and use of war games.

This volume develops and describes an ontology of modern conflict. Modern conflict is a complex adaptive system. As such, it exhibits emergent properties, or properties that are not predictable from simple descriptions of the system. The Modern Conflict Ontology (MCO) creates a structure for collecting and analyzing information regarding both conventional and unconventional conflict in the face of uncertainty. The first three chapters of the book begin the discussion of the MCO. The first chapter introduces the foundational concepts. The second chapter discusses modern conflict in detail. The third chapter provides an overview of ontologies in sufficient detail to make the rest of the book understandable, but without covering the minutia of the subject. The next ten chapters describe the parts of the MCO. Each part is a sub-ontology and is discussed in detail, including connections to the other parts. Instances are used very liberally to ensure that the concepts are made concrete. The final chapter consolidates the descriptions of the ontology into a discussion of “what we can know.” It describes the implementation history and changes from the predecessor Unconventional Conflict Ontology (UCO) to the MCO, plus some uses of the ontology and potential future enhancements. Providing an ontology that describes the entire modern conflict domain, this volume is appropriate for military professionals and academics and professionals in political science, computer science, and operations research.

The only how-to guide offering a unified, systemic approach to acquiring, cleaning, and managing data in R Every experienced practitioner knows that preparing data for modeling is a painstaking, time-consuming process. Adding to the difficulty is that most modelers learn the steps involved in cleaning and managing data piecemeal, often on the fly, or they develop their own ad hoc methods. This book helps simplify their task by providing a unified, systematic approach to acquiring, modeling, manipulating, cleaning, and maintaining data in R. Starting with the very basics, data scientists Samuel E. Buttrey and Lyn R. Whitaker walk readers through the entire process. From what data looks like and what it should look like, they progress through all the steps involved in getting data ready for modeling. They describe best practices for acquiring data from numerous sources; explore key issues in data handling, including text/regular expressions, big data, parallel processing, merging, matching, and checking for duplicates; and outline highly efficient and reliable techniques for documenting data and recordkeeping, including audit trails, getting data back out of R, and more. The only single-source guide to R data and its preparation, it describes best practices for acquiring, manipulating, cleaning, and maintaining data Begins with the basics and walks readers through all the steps necessary to get data ready for the modeling process Provides expert guidance on how to document the processes described so that they are reproducible Written by seasoned professionals, it provides both introductory and advanced techniques Features case studies with supporting data and R code, hosted on a companion website A Data Scientist's Guide to Acquiring, Cleaning and Managing Data in R is a valuable working resource/bench manual for practitioners who collect and analyze data, lab scientists and research associates of all levels of experience, and graduate-level data mining students.