Intelligence Techniques Are Needed to Further Enhance the Advantage of Groups with Diversity in Problem Solving

Authors

Oscar Castillo
Patricia Melin
J. Esteban Gamez
Vladik Kreinovich, The University of Texas at El PasoFollow
Olga Kosheleva, The University of Texas at El PasoFollow

Publication Date

12-2008

Comments

Technical Report: UTEP-CS-08-37a

Published in the Proceedings of the 2009 IEEE Workshop on Hybrid Intelligent Models and Applications HIMA'2009, Nashville, Tennessee, March 30-April 2, 2009, pp. 48-55.

Abstract

In practice, there are many examples when the diversity in a group enhances the group's ability to solve problems -- and thus, leads to more efficient groups, firms, schools, etc. Several papers, starting with the pioneering research by Scott E. Page from the University of Michigan at Ann Arbor, provide a theoretical justification for this known empirical phenomenon. However, when the general advise of increasing diversity is transformed into simple-to-follow algorithmic rules (like quotas), the result is not always successful. In this paper, we prove that the problem of designing the most efficient group is computationally difficult (NP-hard). Thus, in general, it is not possible to come up with simple algorithmic rules for designing such groups: to design optimal groups, we need to combine standard optimization techniques with intelligent techniques that use expert knowledge.