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

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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.

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Authors and Affiliations

  1. Krannert Graduate School of Management, Purdue University, West Lafayette, IN, 47907-1310, USA

    Jackie Rees

  2. Decision and Information Sciences, Warrington College of Business, University of Florida, Gainesville, FL, 32611, USA

    Gary Koehler

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  1. Jackie Rees
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  2. Gary Koehler
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Correspondence to Jackie Rees.

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Rees, J., Koehler, G. Evolution in Groups: A Genetic Algorithm Approach to Group Decision Support Systems. Information Technology and Management 3, 213–227 (2002). https://doi.org/10.1023/A:1015566711710

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