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Author: Stephen R. Watson
Publisher: Cambridge University Press
Published: 1987
Total Pages: 332
ISBN-13: 9780521310789
DOWNLOAD EBOOK →Offers a comprehensive overview of the theory of decision making and its practical application in decision analysis.
Author: Victor Tang
Publisher: Springer
Published: 2018-09-03
Total Pages: 652
ISBN-13: 3319630261
DOWNLOAD EBOOK →This book provides a practice-driven, yet rigorous approach to executive management decision-making that performs well even under unpredictable conditions. It explains how executives can employ prescribed engineering design methods to arrive at robust outcomes even when faced with uncontrollable uncertainty. The book presents the paradigm and its main principles in Part I; in Part II it illustrates how to frame a decision situation and how to design the decision so that it will produce its intended behavior. In turn, Part III discusses in detail in situ case studies on executive management decisions. Lastly, Part IV summarizes the book and formulates the key lessons learned.
Author: Nicky J. Welton
Publisher: John Wiley & Sons
Published: 2012-04-12
Total Pages: 296
ISBN-13: 111830540X
DOWNLOAD EBOOK →In the evaluation of healthcare, rigorous methods of quantitative assessment are necessary to establish interventions that are both effective and cost-effective. Usually a single study will not fully address these issues and it is desirable to synthesize evidence from multiple sources. This book aims to provide a practical guide to evidence synthesis for the purpose of decision making, starting with a simple single parameter model, where all studies estimate the same quantity (pairwise meta-analysis) and progressing to more complex multi-parameter structures (including meta-regression, mixed treatment comparisons, Markov models of disease progression, and epidemiology models). A comprehensive, coherent framework is adopted and estimated using Bayesian methods. Key features: A coherent approach to evidence synthesis from multiple sources. Focus is given to Bayesian methods for evidence synthesis that can be integrated within cost-effectiveness analyses in a probabilistic framework using Markov Chain Monte Carlo simulation. Provides methods to statistically combine evidence from a range of evidence structures. Emphasizes the importance of model critique and checking for evidence consistency. Presents numerous worked examples, exercises and solutions drawn from a variety of medical disciplines throughout the book. WinBUGS code is provided for all examples. Evidence Synthesis for Decision Making in Healthcare is intended for health economists, decision modelers, statisticians and others involved in evidence synthesis, health technology assessment, and economic evaluation of health technologies.
Author: Howard Rachlin
Publisher:
Published: 1989
Total Pages: 312
ISBN-13:
DOWNLOAD EBOOK →This book views both cognitive and behavioural theories and experiments in an historical and philosophical context. Current theory and practice are presented as part of an ongoing effort to understand voluntary human behaviour with roots as deep as those of western civilization. Cognitive and behavioural approaches are viewed as complementary (rather than competing) descriptions of judgement, decision and choice.
Author: Mausam Natarajan
Publisher: Springer Nature
Published: 2022-06-01
Total Pages: 194
ISBN-13: 3031015592
DOWNLOAD EBOOK →Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. On the other hand, reinforcement learning additionally learns these models based on the feedback the agent gets from the environment. This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature. These include determinization-based approaches, sampling techniques, heuristic functions, dimensionality reduction, and hierarchical representations. Finally, we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems. Table of Contents: Introduction / MDPs / Fundamental Algorithms / Heuristic Search Algorithms / Symbolic Algorithms / Approximation Algorithms / Advanced Notes
Author: Robert A. Powell PhD
Publisher: Berrett-Koehler Publishers
Published: 2008-12
Total Pages: 230
ISBN-13: 1523096764
DOWNLOAD EBOOK →Make Better Decisions While Managing Projects! Decision-making is critical in project management. Lack of decision-making knowledge, avoidable mistakes, and improper definitions can negatively impact your company's ability to generate profit. The Project Manager's Guide to Making Successful Decisions is a practical handbook that focuses on the significance of project decision-making skills that will all you to reach workable and effective results. This valuable resource highlights numerous decisions necessary to support the project management life cycle, presents various techniques that facilitate the decision-making process, provides an overview of decision analysis as it relates to project management, and much more! + Understand different types of decision-making processes and cycles + Recognize how to frame the decision and gather better information + Define alternatives and assessments to make the right decision + Analyze short case studies demonstrating project decision making success
Author: Terry Bresnick
Publisher: Springer Nature
Published: 2022-09-21
Total Pages: 159
ISBN-13: 3031112806
DOWNLOAD EBOOK →This book provides a blend of quantitative and qualitative approaches to decision making, while also bridging the gap between the theory of how to make good decisions versus how people actually make decisions. The authors present the tools and techniques of decision analysis to learn how to become a FOCCUSSED decision maker: Identify and properly Frame the decision or problem at hand Specify the goals, Objectives, and values that you are trying to achieve Develop creative, meaningful Choices from among which you can choose Evaluate the Consequences of selecting each alternative using your goals, objectives, and values Think about the key Uncertainties that could impact the decision Understand the Swaps and trade-offs that you are willing to make Develop an approach for implementing your Solution Elicit the data you’ll need from a variety of sources and Disseminate and communicate your decisions to others. The authors define a decision as the choice among alternatives, based on how we value and trade-off their pros and cons, made in the face of uncertainty about what will actually happen. The decision-making process is presented as having three pillars to support the decision maker: Preferences–what we prefer, what meets our goals and objectives, and the recognition that preferences are personal to the one making the decision; Alternatives–the choices, options, or courses of action that we have, and over which we have some degree of control; and Information–what we know about the situation, what we don’t know, how we connect choices to outcomes, and how we deal with uncertainty. Key components of good decision-making include how to define your goals and objectives, how to incorporate uncertainties that we all face, and how to develop better alternatives, all of which are discussed. Sophisticated techniques are presented in a way that is accessible to the average decision maker. Probability theory is utilized to improve decisions, and uncertainties are captured in decision trees. Risk avoidance, risk transfer, and risk mitigation are also discussed. Readers will gain a clear understanding of how to articulate the goals and objectives that should be the focal point of any decision.
Author: Diana B. Petitti
Publisher: OUP USA
Published: 2000
Total Pages: 319
ISBN-13: 0195133641
DOWNLOAD EBOOK →Public health and in health policy courses at the undergraduate and graduate level.
Author: Vincent A. W. J. Marchau
Publisher: Springer
Published: 2019-04-04
Total Pages: 408
ISBN-13: 3030052524
DOWNLOAD EBOOK →This open access book focuses on both the theory and practice associated with the tools and approaches for decisionmaking in the face of deep uncertainty. It explores approaches and tools supporting the design of strategic plans under deep uncertainty, and their testing in the real world, including barriers and enablers for their use in practice. The book broadens traditional approaches and tools to include the analysis of actors and networks related to the problem at hand. It also shows how lessons learned in the application process can be used to improve the approaches and tools used in the design process. The book offers guidance in identifying and applying appropriate approaches and tools to design plans, as well as advice on implementing these plans in the real world. For decisionmakers and practitioners, the book includes realistic examples and practical guidelines that should help them understand what decisionmaking under deep uncertainty is and how it may be of assistance to them. Decision Making under Deep Uncertainty: From Theory to Practice is divided into four parts. Part I presents five approaches for designing strategic plans under deep uncertainty: Robust Decision Making, Dynamic Adaptive Planning, Dynamic Adaptive Policy Pathways, Info-Gap Decision Theory, and Engineering Options Analysis. Each approach is worked out in terms of its theoretical foundations, methodological steps to follow when using the approach, latest methodological insights, and challenges for improvement. In Part II, applications of each of these approaches are presented. Based on recent case studies, the practical implications of applying each approach are discussed in depth. Part III focuses on using the approaches and tools in real-world contexts, based on insights from real-world cases. Part IV contains conclusions and a synthesis of the lessons that can be drawn for designing, applying, and implementing strategic plans under deep uncertainty, as well as recommendations for future work. The publication of this book has been funded by the Radboud University, the RAND Corporation, Delft University of Technology, and Deltares.
