Decision-making under uncertainty : an applied statistics approach
著者
書誌事項
Decision-making under uncertainty : an applied statistics approach
Praeger, 1991
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注記
Bibliography: p. [247]-249
Includes index
内容説明・目次
内容説明
In real-life decision-making situations it is necessary to make decisions with incomplete information, for oftentimes uncertain results. In Decision-Making Under Uncertainty, Dr. Chacko applies his years of statistical research and experience to the analysis of twenty-four real-life decision-making situations, both those with few data points (eg: Cuban Missile Crisis), and many data points (eg: aspirin for heart attack prevention). These situations encompass decision-making in a variety of business, social and political, physical and biological, and military environments. Though different, all of these have one characteristic in common: their outcomes are uncertain/unkown, and unknowable. Chacko Demonstrates how the decision-maker can reduce uncertainty by choosing probable outcomes using the statistical methods he introduces.
This detailed volume develops standard statistical concepts (t, x2, normal distribution, ANOVA), and the less familiar concepts (logical probability, subjective probability, Bayesian Inference, Penalty for Non-Fulfillment, Bluff-Threats Matrix, etc.). Chacko also offers a thorough discussion of the underlying theoretical principles. The end of each chapter contains a set of questions, three quarters of which focus on concepts, formulation, conclusion, resource commitments, and caveats; only one quarter with computations. Ideal for the practitioner, the work is also designed to serve as the primary text for graduate or advanced undergraduate courses in statistics and decision science.
目次
Preface Single-Variable Decision-Making with Very Few Data Points Irrevocable Commitment with Incomplete Data Decision-Making with Very Few Data Points Single-Variable Decision-Making with Very Many Data Points Decision-Making with Very Many Data Points Null Hypothesis Foundations of Standard Statistical Tables Multiple-Variable Decision-Making with Very Many Data Points Pairwise and Groupwise Togetherness Dynamic Replication of Reality Single- and Multiple Variable Decision-Making with Very Few Data Points--Single Decision-Makers Improving the Initial Guess with New Data Firm Decisions on Fuzzy Foundations Single and Multiple Variable Decision-Making with Very Few Data Points--Multiple Decision-Makers Attitude Toward Outcomes--Single and Multiple Decision-Makers Aggregate Action-Outcome Anticipations of Multiple Decision-Makers Answers to Questions Appendix: Standard Statistical Tables Bibliography Index
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