Reinforcement and Systemic Machine Learning for Decision Making

Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems. This book describes the latest RL and ADP techniques for decision and control in human engineered systems, covering both single player decision and control and multi-player games. Edited by the pioneers of RL and ADP research, the book brings together ideas and methods from many fields and provides an important and timely guidance on controlling a wide variety of systems, such as robots, industrial processes, and economic decision-making.

Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations. Key Features: Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques. Starts from basic principles up to advanced concepts. Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software. Gives practical tips for data mining implementation to solve real world problems. Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring. Supported by an accompanying website hosting datasets and user analysis. Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.

Making a decision, of any importance, is never simple. On the one hand, specialists in decision theory do not come within the reach of most policy makers and, secondly, there are very few books on pragmatic decision that are not purely anecdotal. In addition, there is virtually no book that provides a link between decision-making and action. This book provides a bridge between the latest results in artificial intelligence, neurobiology, psychology and decision-making for action. What is the role of intuition or emotion? What are the main psychological biases of which we must be wary? How can we avoid being manipulated? What is the proper use of planning? How can we remain rational even if one is not an expert in probabilities? Perhaps more importantly for managers, how does one go from decision to action? So many questions fundamental to the practice of decision-making are addressed. This book dissects all issues that arise almost daily for decision-makers, at least for major decisions. Drawing on numerous examples, this book answers, in plain language and imagery, all your questions. The final chapter takes the form of a brief reminder – everything you have to remember to be a good decision-maker.

Decision-making has evolved recently thanks to the introduction of information and communication technologies in many organizations, which has led to new kinds of decision-making processes, called “collaborative decision-making”, at the organizational and cognitive levels. This book looks at the development of the decision-making process in organizations. Decision-aiding and its paradigm of problem solving are defined, showing how decision-makers now need to work in a cooperative way. Definitions of cooperation and associated concepts such as collaboration and coordination are given and a framework of cooperative decision support systems is presented, including intelligent DSS, cooperative knowledge-based systems, workflow, group support systems, collaborative engineering, integrating with a collaborative decision-making model in part or being part of global projects. Several models and experimental studies are also included showing that these new processes have to be supported by new types of tools, several of which are described in order to calculate or simulate solutions or global solutions for decision-making modification. Definitions and new trends for these models are given, along with types of systems. Contents 1. Alteration of Decision-Making Processes in Organizations. 2. New Decision-Making Processes. 3. The Need to Cooperate. 4. Cooperative Decision-Making. 5. Activity Support Systems. 6. Cooperative Decision Support Systems: CDSSS. About the Authors Pascale Zarat? is Professor at Toulouse 1 Capitole University, France. She conducts her research at the IRIT Laboratory and is the Editor-in-Chief of the International Journal of Decision Support Systems Technologies. She is co-chair of the European Working Group on DSS and has published several studies and books.

Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all factors that affect a business, such as customers, competitors, business partners, economic environment, and internal operations, therefore enabling optimal decisions to be made. Business Intelligence provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence. This book: Combines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence. Covers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation. Is made accessible to readers through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies. Explains how to utilise mathematical models and analysis models to make effective and good quality business decisions. This book is aimed at postgraduate students following data analysis and data mining courses. Researchers looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide.

IIE/Joint Publishers Book of the Year Award 2016! Awarded for ‘an outstanding published book that focuses on a facet of industrial engineering, improves education, or furthers the profession’. Engineering Decision Making and Risk Management emphasizes practical issues and examples of decision making with applications in engineering design and management Featuring a blend of theoretical and analytical aspects, this book presents multiple perspectives on decision making to better understand and improve risk management processes and decision-making systems. Engineering Decision Making and Risk Management uniquely presents and discusses three perspectives on decision making: problem solving, the decision-making process, and decision-making systems. The author highlights formal techniques for group decision making and game theory and includes numerical examples to compare and contrast different quantitative techniques. The importance of initially selecting the most appropriate decision-making process is emphasized through practical examples and applications that illustrate a variety of useful processes. Presenting an approach for modeling and improving decision-making systems, Engineering Decision Making and Risk Management also features: Theoretically sound and practical tools for decision making under uncertainty, multi-criteria decision making, group decision making, the value of information, and risk management Practical examples from both historical and current events that illustrate both good and bad decision making and risk management processes End-of-chapter exercises for readers to apply specific learning objectives and practice relevant skills A supplementary website with instructional support material, including worked solutions to the exercises, lesson plans, in-class activities, slides, and spreadsheets An excellent textbook for upper-undergraduate and graduate students, Engineering Decision Making and Risk Management is appropriate for courses on decision analysis, decision making, and risk management within the fields of engineering design, operations research, business and management science, and industrial and systems engineering. The book is also an ideal reference for academics and practitioners in business and management science, operations research, engineering design, systems engineering, applied mathematics, and statistics.

A comprehensive exposition of the theory and techniques of fault identification and decision theory when applied to complex systems shows how modern computer analysis and diagnostic methods might be applied to launch vehicle design, checkout, and launch the space checkout system is a specialized area which is rarely explored in terms of the intelligent techniques and approaches involved an original view combining modern theory with well-established research material, inviting a contemporary approach to launch dynamics highlights the advanced research works in the field of testing, control and decision-making for space launch presented in a very well organized way and the technical level is very high

The only book to combine emergency management principLEs with proven military concepts Good disaster plans do not guarantee a good response. Any disaster plan rarely survives the first rain bands of a hurricane or the first tremors of an earthquake. While developing plans is essential, there must be systems in place to adapt these plans to the ever-changing operational environment of a disaster. Currently there is no set of standard disaster response principles to guide a community. The National Incident Management System (NIMS) and the Incident Command System (ICS) provide the framework to implement operational decisions, but they were never designed as operational concepts. The military has developed just such concepts and many of them can be adapted for civilian use. Disaster Operations and Decision Making adapts those military concepts and combines them with disaster lessons learned to create a new opera-tional paradigm. Emphasizing team building, Emergency Operations Center operational systems, and situational awareness, the book details easily adopted methods. All of these methods are designed to be incorporated into the NIMS and ICS framework to enhance a community's response to any type of disaster. Disaster Operations and Decision Making is an essential resource for emergency managers, fire chiefs, law enforcement officers, homeland security professionals, public health officials, and anyone else involved or interested in crisis management.

An increasing number of business executives, managers and political leaders are using the concept of “weak signals” nowadays. There are also an increasing number of people trying to find out exactly what this concept means, as well as when and how to use it. Such questions arise particularly when it comes down to making strategic decisions. To help with these questions, this book defines the concept of the “weak signal” and then demonstrates how it would be useful in relation to strategic decisions, and more precisely in relation to three moments of the strategic decision, i.e.: – The trigger of the strategic decision (what is the trigger, why and when) – The development of the strategic decision – The implementation of the strategic decision