Publication Date



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.


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.

tr08-37a.pdf (107 kB)
Original file: UTEP-CS-08-37