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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.
List of contents
Preface
Single-Variable Decision-Making with Very Few Data PointsIrrevocable Commitment with Incomplete Data
Decision-Making with Very Few Data Points
Single-Variable Decision-Making with Very Many Data PointsDecision-Making with Very Many Data Points
Null Hypothesis Foundations of Standard Statistical Tables
Multiple-Variable Decision-Making with Very Many Data PointsPairwise and Groupwise Togetherness
Dynamic Replication of Reality
Single- and Multiple Variable Decision-Making with Very Few Data Points--Single Decision-MakersImproving the Initial Guess with New Data
Firm Decisions on Fuzzy Foundations
Single and Multiple Variable Decision-Making with Very Few Data Points--Multiple Decision-MakersAttitude Toward Outcomes--Single and Multiple Decision-Makers
Aggregate Action-Outcome Anticipations of Multiple Decision-Makers
Answers to QuestionsAppendix: Standard Statistical Tables
Bibliography
Index
About the author
GEORGE K. CHACKO is Professor of Systems Science at the University of Southern California and Director of the Doctoral Program in Technology Management at National Chengchi University in Taipei. A member of the World Future Society and Fellow of the American Association for the Advancement of Science and the American Astronautical Society, Dr. Chacko is the author of 39 books, including Technology Management (Praeger, 1988) and Dynamic Program Management (Praeger, 1989).
