Response Models for Detection of Change

1. Front Matter

   Pages i-viii

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2. Introduction

   • Amnon Rapoport, William E. Stein, Graham J. Burkheimer

   Pages 1-15

3. The Optimal Policy

   • Amnon Rapoport, William E. Stein, Graham J. Burkheimer

   Pages 16-31

4. A Response Model with a Fixed Probability Boundary

   • Amnon Rapoport, William E. Stein, Graham J. Burkheimer

   Pages 32-63

5. A Response Model with a Fixed Number of Observations

   • Amnon Rapoport, William E. Stein, Graham J. Burkheimer

   Pages 64-80

6. A Response Model with a Fixed Number of Successive Observations

   • Amnon Rapoport, William E. Stein, Graham J. Burkheimer

   Pages 81-102

7. Sensitivity Analysis

   • Amnon Rapoport, William E. Stein, Graham J. Burkheimer

   Pages 103-118

8. Multi-State Detection of Change

   • Amnon Rapoport, William E. Stein, Graham J. Burkheimer

   Pages 119-147

9. Experimental Research

   • Amnon Rapoport, William E. Stein, Graham J. Burkheimer

   Pages 147-167

10. Extensions

    • Amnon Rapoport, William E. Stein, Graham J. Burkheimer

    Pages 168-179

11. Back Matter

    Pages 180-201

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This book reports our research on detection of change processes that underlie psychophysical, learning, medical diagnosis, military, and pro­ duction control situations, and share three major features. First, the states of the process are not directly observable but become gradually known with the sequential acquisition of fallible information over time. Second, the mechanism that generates the fallible information is not stationary; rather, it is subjected to a sudden and irrevocable change. Thirdly, in­ complete, probabilistic information about the time of change is available when the process commences. The purpose of the book is to characterize this class of detection of change processes, to derive the optimal policy that minimizes total expected loss, and, most importantly, to develop testable response models, based on simple decision rules, for describing detection of change behavior. The book is theoretical in the sense that it offers mathematical models of multi-stage decision behavior and solutions to optimization problems. However, it is not anti-empirical, as it aims to stimulate new experimental research and to generate applications. Throughout the book, questions of experimental verification are briefly considered, and existing data from two studies are brought to bear on the validity of the models. The work is not complete; it only provides a starting point for investigating how people detect a change in an uncertain environment, balancing between the cost of delay in detecting the change and the cost of making an incor­ rect terminal decision.

• decision theory
• evaluation
• experiment
• information
• nature
• research
• state
• statistics

• University of North Carolina, Chapel Hill, USA

  Amnon Rapoport

• Texas Christian University, USA

  William E. Stein

• Research Triangle Institute, USA

  Graham J. Burkheimer

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