Directions for downloading student versions of the DecisionTools Suite and Logical Decisions software can be found in the appendices. Web Resource The book’s website at offers tutorials of Logical Decisions software for multi-objective decisions and Precision Tree software for probabilistic decisions. Each chapter also includes discussion questions and references. Numerous activities interspersed throughout the text highlight real-world situations, helping readers see how the concepts presented can be used in their own work environment or personal life. The book also covers more complex decisions arising in negotiations, strategy, and ethics that involve multiple dimensions simultaneously. The decisions include buying a car, picking a supplier or home contractor, selecting a technology, picking a location for a manufacturing plant or sports stadium, hiring an employee or selecting among job offers, deciding on the size of a sales force, making a late design change, and sourcing to emerging markets. The core of the text addresses decisions that involve selecting the best alternative from diverse choices. The authors analyze strengths and weaknesses of the best alternatives, enabling decision makers to improve on these alternatives by adding value and reducing risk. The tradeoffs between accuracy and computation speed for the mixture distribution approach compare favorably with those for discretization and other approaches in a variety of problems, especially ones that call for extensions of powerful Gaussian models such as the Kalman filter.Download Value Added Decision Making for Managers Book in PDF, Epub and Kindleĭeveloped from the authors’ longstanding course on decision and risk analysis, Value-Added Decision Making for Managers explores the important interaction between decisions and management action and clarifies the barriers to rational decision making. Influence diagrams, which represent decision and inference problems graphically, are used to represent problems formulated with mixtures, and to solve them efficiently in the case of Gaussian mixtures, exploiting the tractability of the multivariate Gaussian distribution. Common statistical methods for estimating mixtures, such as the EM algorithm, are adapted for fitting artificial mixtures, and a simple objective that balances accuracy and computational cost is used to select the number of continuous components. Unlike most of the mixture literature, this dissertation emphasizes constructing artificial mixtures in order to approximate arbitrary continuous distributions in a tractable form. It generalizes both discrete and Gaussian distributions and can combine advantages of each for analysis. A Gaussian mixture becomes Gaussian when conditioned on the outcome of an unobserved discrete variable. This dissertation develops the use of mixture distributions, especially Gaussian mixtures (normal mixtures), for this purpose. An alternative approximation is to fit tractable continuous probability distributions to the continuous random variables, allowing calculations in closed form. To simplify assessments and computations, practitioners of decision analysis discretize these to a few points. Decision problems often involve continuous random variables and continuous decision variables.
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