Intelligence techniques are needed to further enhance the advantage of groups with diversity in problem solving

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.