Managing Responsibility and Information Flow in Dynamic Team Decision-Making

Abstract

We propose a method to allocate decision responsibility and arrange information flow dynamically within a team of decision-makers for command and control. A model of decision making which relates the decision load to decision accuracy is proposed and employed as an atomic building block to create predictive models of team decision making. An optimization problem is then proposed for a given set of forecast decision requirements in which the information flow between the atomic decision-making models is varied so as to maximize an aggregate measure of decision accuracy. A small-scale MATLAB Simulink implementation is presented as well as the outline of current work in which a genetic algorithm is employed to perform the optimization. Preliminary results indicate the technique improves the decision-making performance measure. We conclude with a discussion of implementation issues in a larger C2 context.

Document Details

• Document Type: Technical Report
• Publication Date: Sep 01, 2002
• Accession Number: ADA467635

Entities

People

• Alexander Kott
• Michael K. Martin
• Paul Hubbard

Tags

Communities of Interest

• C4I
• Human Systems

DTIC Thesaurus Topics

• Accuracy
• Algorithms
• Cognitive Systems Engineering
• Command And Control
• Command And Control Systems
• Control Systems
• Fire Fighting
• Genetic Algorithms
• Industrial Engineering
• Information Exchange
• Information Overload
• Information Transfer
• Military Operations
• Optimization
• Patent Applications
• Predictive Modeling
• Simulations

Fields of Study

• Computer science

Readers

• Computational Modeling and Simulation
• Operations Research
• Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

• AI & ML
• AI & ML - Bayesian Inference
• AI & ML - Machine Learning Algorithms
• Biotechnology
• Fully Networked C3
• Fully Networked C3 - Command and Control