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Decision science concerns understanding human decisionmaking and methods and tools to assist it. The first concern includes the distinction between descriptive and prescriptive: how humans actually decide, versus how they should decide. Much of the early literature prescribed rational-analytic methods, such as embodied in systems analysis and policy analysis. The descriptive literature, however, has long noted that humans use heuristics (cognitive shortcuts), which are usually quite valuable, but which sometimes introduce unintended biases. Efforts have been made to improve decision support by “debiasing” the presentation of information. A newer literature on “naturalistic” decisionmaking, however, emphasizes the strengths of intuitive decisionmaking based on heuristics and questions the desirability of debiasing. Our study contrasts the schools of thought and suggests steps toward a synthesis. Ultimately, decision support should appeal to both the rational-analytic and the intuitive capabilities of the decisionmaker, with a balance of “cold” and story-based presentation of analysis and recommendations. The particular balance should depend on characteristics of the decision, the decision environment, and the decisionmaker. Our study also discusses new tools emerging for decision support, which include increasingly realistic models and simulations, such as virtual worlds, and new methods to help in the creative and imaginative aspects of strategic planning. Most important, we note modern methods, such as exploratory analysis, to encourage decisions and strategies that are flexible, adaptive, and robust so as to deal well with uncertainty.

Table of Contents

  • Chapter One

    Introduction

  • Chapter Two

    Human Decisionmaking

  • Chapter Three

    Classic Analysis Concepts and Their Evolution

  • Chapter Four

    Advanced Decision Science for Analysis

  • Chapter Five

    The Research Frontier: Reconciling Analytic and Intuitive Considerations

  • Chapter Six

    Conclusions

  • Appendix A

    Debiasing an Air Campaign

  • Appendix B

    Rethinking Families of Models

  • Appendix C

    Further Discussion of Judgmental Bias and DSS

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The research reported here was sponsored by the United States Air Force and conducted by RAND Project AIR FORCE.

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