Applied Decision Making

This book gathers a collection of the latest research, applications, and proposals, introducing readers to innovations and concepts from diverse environments and systems. As such, it will provide students and professionals alike with not only cutting-edge information, but also new inspirations and potential research directions. Each chapter focuses on a specific aspect of applied decision making, e.g. in complex systems, computational intelligence, security, and ubiquitous computing.

This Fifth Edition reflects the ever-increasing changes in the tools and technology available today. Duane Davis teaches students and managers how to develop ways to efficiently and effectively plan, collect, organize, and assimilate information to make informed business decisions. This book covers the fundamentals of conducting research as well as the recent advancements in the field of business research such as the use of the Internet, qualitative research, and modern analytical tools (SPSS and Excel). The new edition is available packaged with the SPSS Student Version Software.

Human Judgment and Decision Processes in Applied Settings is the second to two volumes that attempt to define the areas of progress in the understanding of human decision making processes. The first volume, Human Judgment and Decision Processes (Academic Press, 1975) was concerned with formal and mathematical approaches to the problems of judgment and decision making. The major theoretical orientations (information integration theory, signal detection theory, portfolio theory, and multiattribute-utility measurement) were presented and their rationales discussed. The present volume is concerned with the application of these theories, and the various techniques derived from them, to the problems of decision making in the everyday world. The chapters reflect the many modifications and adjustments that must be made to mathematical rules in order to apply decision theory models in the real world. The tools described serve a broad variety of interests: those of the urban health or social planner, the organizational manager, the researcher, the educator, and, in fact, all of those who must weight evidence to reach decisions. Planner, manager, researcher, teacher, policymaker—all will find assistance in overcoming the commonly encountered roadblocks when one must choose between alternatives in what remains an uncertain world.

In Decision Making and Problem Solving: A Practical Guide for Applied Research, the author utilizes traditional approaches, tools, and techniques adopted to solve current day-to-day, real-life problems. The book offers guidance in identifying and applying accurate methods for designing a strategy as well as implementing these strategies in the real world. The book includes realistic case studies and practical approaches that should help readers understand how the decision making occurs and can be applied to problem solving under deep uncertainty.

The objective of this edited book is to share the outcomes from various research domains to develop efficient, adaptive, and intelligent models to handle the challenges related to decision making. It incorporates the advances in machine intelligent techniques such as data streaming, classification, clustering, pattern matching, feature selection, and deep learning in the decision-making process for several diversified applications such as agriculture, character recognition, landslide susceptibility, recommendation systems, forecasting air quality, healthcare, exchange rate prediction, and image dehazing. It also provides a premier interdisciplinary platform for scientists, researchers, practitioners, and educators to share their thoughts in the context of recent innovations, trends, developments, practical challenges, and advancements in the field of data mining, machine learning, soft computing, and decision science. It also focuses on the usefulness of applied intelligent techniques in the decision-making process in several aspects. To address these objectives, this edited book includes a dozen chapters contributed by authors from around the globe. The authors attempt to solve these complex problems using several intelligent machine-learning techniques. This allows researchers to understand the mechanism needed to harness the decision-making process using machine-learning techniques for their own respective endeavors.

Multi-Criteria Decision Making (MCDM) has been one of the fastest growing problem areas in many disciplines. The central problem is how to evaluate a set of alternatives in terms of a number of criteria. Although this problem is very relevant in practice, there are few methods available and their quality is hard to determine. Thus, the question `Which is the best method for a given problem?' has become one of the most important and challenging ones. This is exactly what this book has as its focus and why it is important. The author extensively compares, both theoretically and empirically, real-life MCDM issues and makes the reader aware of quite a number of surprising `abnormalities' with some of these methods. What makes this book so valuable and different is that even though the analyses are rigorous, the results can be understood even by the non-specialist. Audience: Researchers, practitioners, and students; it can be used as a textbook for senior undergraduate or graduate courses in business and engineering.

Employees are constantly making decisions and judgments that have the potential to affect themselves, their families, their work organizations, and on some occasion even the broader societies in which they live. A few examples include: deciding which job applicant to hire, setting a production goal, judging one’s level of job satisfaction, deciding to steal from the cash register, agreeing to help organize the company’s holiday party, forecasting corporate tax rates two years later, deciding to report a coworker for sexual harassment, and predicting the level of risk inherent in a new business venture. In other words, a great many topics of interest to organizational researchers ultimately reduce to decisions made by employees. Yet, numerous entreaties notwithstanding, industrial and organizational psychologists typically have not incorporated a judgment and decision-making perspective in their research. The current book begins to remedy the situation by facilitating cross-pollination between the disciplines of organizational psychology and decision-making. The book describes both laboratory and more “naturalistic” field research on judgment and decision-making, and applies it to core topics of interest to industrial and organizational psychologists: performance appraisal, employee selection, individual differences, goals, leadership, teams, and stress, among others. The book also suggests ways in which industrial and organizational psychology research can benefit the discipline of judgment and decision-making. The authors of the chapters in this book conduct research at the intersection of organizational psychology and decision-making, and consequently are uniquely positioned to bridging the divide between the two disciplines.