COMPARISON OF BAYESIAN AND REGRESSION APPROACHES TO THE STUDY OF INFORMATION PROCESSING IN JUDGMENT,

Abstract

Since 1960 there have been more than 600 studies within the rather narrowly defined topic of information utilization in judgment and decision making. Much of this work has been accomplished within two basic schools of research, which has been labeled the 'regression' and the 'Bayesian' approaches. For the most part, researchers have tended to work strictly within a single approach and there has been minimal communication between the resultant subgroups of workers. The objective is to present a comparative analysis of these two broad methods of approach, examining (a) the models that have been developed for prescribing and describing the use of information in decision making; (b) the major experimental paradigms, including the types of judgment, prediction, and decision tasks and the kinds of information that have been available to the decision maker in these tasks; (c) the key independent variables that have been manipulated in experimental studies; and (d) the major empirical results and conclusions. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1970
Accession Number
AD0709976

Entities

People

  • Paul Slovic
  • Sarah Lichtenstein

Organizations

  • Oregon Research Institute

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Cognition
  • Information Processing
  • Judgment
  • Mental Processes

Fields of Study

  • Psychology

Readers

  • Computational Modeling and Simulation
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Information Retrieval