Human Judgment and Decision Making: A Proposed Decision Model Using Sequential Processing

Abstract

This research effort developed, discussed and tested an alternative model of human judgment. Previous research has established the robustness and explanatory power of the linear compensatory decision model and documented evidence indicating a fundamental inability of the brain to process decision cue interactions. This research used policy capturing experiments to simulate human decision making in order to determine the explanatory power of a decision model based on processing decision cues in a sequential, cumulative fashion. This model is nonlinear and expands the limitations of previous decision models. A primary concern was the selection of weights used in either the compensatory or proposed decision models. Subjective weights supplied by the participants in two decision making exercises were compared with weights estimated for each model and with equally weighted cues. An analysis of variance was completed to determine the performance of the alternative models with each set of weights. It was concluded that the proposed decision model is a valid and innovative model of human judgment, particularly when equally weighted cues are used. The implications of a sequential model of judgment are discussed and applications to other research disciplines are presented.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1985
Accession Number
ADA174404

Entities

People

  • Wade H. Shaw Jr

Tags

Communities of Interest

  • C4I
  • Human Systems

DTIC Thesaurus Topics

  • Analysis Of Variance
  • Artificial Intelligence
  • Computational Science
  • Data Science
  • Databases
  • Decision Theory
  • Experimental Design
  • Information Processing
  • Information Science
  • Management Personnel
  • Mathematical Models
  • Motor Skills
  • Operations Research
  • Plastic Explosives
  • Psychology
  • Statistical Algorithms
  • Statistical Analysis

Fields of Study

  • Psychology

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.