Assessing and Improving Team Decision Making

Abstract

This project employed analytical and experimental techniques from signal detection theory to (a) assess the accuracy of team performance, (b) identify sources of inefficiency in team decision making, (c) specify how team members utilize information received from sources having different statistical properties, and (d) model the team deliberation process. The team's task was to decide whether signal-plus-noise or noise-alone had occurred, based on individual graphical displays presented to the team members. Team performance was shown to depend on (1) the signal-to-noise level of members' displays and the efficiency of individual member detection (compared to the statistical optimal), (2) the correlation (common noise) among members' displays, (3) constraints on member communication and interaction, and (4) how efficiently the team combined member judgments to form the team's decision (including mandatory voting rules). The internal correlation, expertise, and bias of different information sources influenced the decision weights that team members gave to these sources, sometimes in a non-optimal fashion. Using a Bayesian network model of team deliberation, the project began to quantify important aspects of the deliberation process such as the interaction protocols used by members of a networked team.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2002
Accession Number
ADA399024

Entities

People

  • Robert D. Sorkin

Organizations

  • University of Florida

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Bayesian Networks
  • Conformity
  • Consistency
  • Detection
  • Efficiency
  • False Alarms
  • Judgment
  • Models
  • Noise
  • Observers
  • Probability
  • Psychological Phenomena And Processes
  • Psychology
  • Signal Detection
  • Students

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Organizational Process Management (OPM).
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference