Eliciting and analyzing expert judgement : a practical guide

著者

    • Meyer, Mary A.
    • Booker, Jane M.

書誌事項

Eliciting and analyzing expert judgement : a practical guide

Mary A. Meyer, Jane M. Booker

（Knowledge-based systems）

Academic Press, c1991

大学図書館所蔵 件 / 全7件

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注記

Bibliography: p. 433-441

Includes index

内容説明・目次

内容説明

Expert judgement is used in response to an enormous diversity of technical problems. The expert is often required to perform a role when other sources, such as measurement, observations, experimentation, or simulation, are unavailable or not widely agreed upon. However, many problems are faced in translating expert judgement into reliable and unbiased solutions. With the correct elicitation and analysis techniques, Meyer and Booker show that using expert judgement can be infinately more reliable and efficient. The subject of this book is analyzing and eliciting expert judgement for practical applications. The authors provide guidelines for formal elicitation and analysis, with particular reference to methods developed in the field of human cognition and communication. They also outline the principle which proscribes that elicitation and analysis techniques should not be dependent on the experts and their domain and on the way humans actually think. The book will allow even novice readers to design appropriate methods for their own particular application according to this principle.

目次

  • Part 1 Introduction to expert judgement: common questions and pitfalls concerning expert judgement
  • background on human problem solving and bias. Part 2 Elicitation procedures: selecting the question areas
  • refining the questions
  • selecting and motivating the experts
  • selecting the components of elicitation
  • designing and tailoring the elicitation
  • practicing the elicitation and training the project personnel
  • conducting the elicitation. Part 3 Analysis procedures: introducing the techniques for analysis of expert judgement data
  • initial look at the data - the first analyses
  • understanding the data base structure
  • correlation and bias detection
  • model formation
  • combining responses - aggregation
  • characterizing uncertainties
  • making inferences.

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