An evolutionary approach to group decision making

We propose modeling Group Support System (GSS) search tasks with Genetic Algorithms. Using explicit mathematical models for Genetic Algorithms (GAs), we show how to estimate the underlying GA parameters from an observed GSS solution path. Once these parameters are estimated, they may be related to GSS variables such as group composition and membership, leadership presence, the specific GSS tools available, incentive structure, and organizational culture. The estimated Genetic Algorithm parameters can be used with the mathematical models for GAs to compute or simulate expected GSS process outcomes.