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This book describes how a confused decision maker, who wishes to make a reasonable and responsible choice among alternatives, can systematically probe their thoughts and feelings in order to make the critically important trade-offs between incommensurable objectives.
The axiomatic foundations of the Bayesian approach to decision making assurne precision in the decision maker's judgements. In practicc, dccision makers often provide only partial and/or doubtful information. We unify and expand results to deal with those cases introducing a general framework for sensitivity analysis in multi-objective decision making. We study first decision making problems under partial information. We provide axioms leading to modelling preferences by families of value functions, in problems under certainty, and moJelling beliefs by families of probability distributions and preferences by familics of utility functions, in problems under uncertainty. Both problems are treated in parallel with the same parametric model. Alternatives are ordered in a Pareto sense, the solution of the problem being the set of non dominated alternatives. Potentially optimal solutions also seem acceptable, from an intuitive point of view and due to their relation with the nondominated ones. Algorithms are provided to compute these solutions in general problems and in cases typical in practice: linear and bilinear problems. Other solution concepts are criticised on the grounds of being ad hoc. In summary, we have a more ro bust theory of decision making based on a weaker set ofaxioms, but embodying coherence, since it essentially implies carrying out a family of coherent dccision anitlyses.
Whether managing strategy, operations or products, knowing how to make the best decision in a complex, uncertain business environment is difficult. You might be faced with multiple, competing objectives, which means making trade-offs. To complicate matters, any uncertainty makes it hard to explicitly understand how different objectives will impact potential outcomes. This book will help you face these problems. It provides a decision analysis framework implemented as a simple spreadsheet tool. This multi-objective decision analysis framework helps you to measure trade-offs among objectives and incorporate uncertainties and risk preferences. With this book, you will be able to identify what information is needed to make a decision, define how that information should be combined, and, finally, provide quantifiable evidence to clearly communicate and justify the decision. The process involves minimal overhead and is perfect for busy professionals who need a simple, structured process for making, tracking, and communicating decisions. This process makes decision making more efficient by focusing only on information and factors that are well-defined, measureable, and relevant to the decision at hand. The framework requires clear characterization of a decision, ensuring that it can be traced and is consistent with the intended objectives and organizational values. Using this structured decision-making framework, anyone can consistently make better decisions to gain competitive and strategic advantage.
An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
This work on strategic decision making focuses on multi-objective decision analysis with spreadsheets
Accompanying CD-ROM contains ... "[u]sers manual and software for NCHRP Report 590: Multi-objective optimization for bridge management systems."--CD-ROM label.
From the individual to the largest organization, everyone today has to make investments in IT. Making a smart investment that will best satisfy all the necessary decision-making criteria requires careful and inclusive analysis. This textbook provides an up-to-date, in-depth understanding of the methodologies available to aid in this complex process of multi-criteria decision-making. It guides readers on the process of technology acquisition — what methods to use to make IT investment decisions, how to choose the technology and justify its selection, and how the decision will impact the organization.Unique to this textbook are both financial investment models and more complex decision-making models from the field of management science so that readers can extend the analysis benefits to enhance and confirm their IT investment choices. The wide range of methodologies featured in the book gives readers the opportunity to customize their best-fit solutions for their unique IT decision situation. This textbook is especially ideal for educators and students involved in programs dealing with technology management, operations management, applied finance, operations research, and industrial engineering.A complimentary copy of the ‘Instructor's Manual and Test Bank’ and the PowerPoint presentations of the text materials are available for all instructors who adopt this book as a course text. Please send your request to [email protected].