Evolution in Groups: A Genetic Algorithm Approach to Group Decision Support Systems

Authors:
Jackie Rees;Gary Koehler
Affiliations:
Krannert Graduate School of Management, Purdue University, West Lafayette, IN 47907-1310, USA jrees@mgmt.purdue.edu;Decision and Information Sciences, Warrington College of Business, University of Florida, Gainesville, FL 32611, USA koehler@ufl.edu
Venue:
Information Technology and Management
Year:
2002

Citing 8
Cited 1

A foundation for the study of group decision support systems

Management Science
Electronic meeting systems

Communications of the ACM - Special issue on computer graphics: state of the arts
An introduction to genetic algorithms

An introduction to genetic algorithms
The Simple Genetic Algorithm: Foundations and Theory

The Simple Genetic Algorithm: Foundations and Theory
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
The New Science of Management Decision

The New Science of Management Decision
A mathematical model of performance of computer-mediated groups during idea generation

Journal of Management Information Systems
The global network organization of the future: information management opportunities and challenges

Journal of Management Information Systems - Special issue: Information technology and organization design

A decision support system for substage-zoning filling design of rock-fill dams based on particle swarm optimization

Information Technology and Management

Quantified Score

Hi-index	0.00

Visualization

Abstract

Certain tasks undertaken by groups using Group Decision Support Systems (GDSS) can be viewed as search problems. These tasks involve arriving at a solution or decision where the problem is complex enough to warrant the use of computerized decision support tools. For these types of GDSS tasks, we propose to model the information exchange and convergence toward a solution by the group as a simple genetic algorithm. The simple genetic algorithm is a generalized search technique that is based on the principles of evolution and natural selection. Simply put, the best points in the current population are more likely to be selected and combined through genetic operators to determine new points. We propose that groups using GDSS to address certain tasks behave like a simple genetic algorithm in the manner in which possible solutions are generated, enhanced and altered in attempting to reach a decision or consensus.