  Groups of people or even robots often face problems they need to solve
together. Examples include collectively searching for resources, choosing when
and where to invest time and effort, and many more. Although a hierarchical
ordering of the relevance of the group members' inputs during collective
decision making is abundant, a quantitative demonstration of its origin and
advantages using a generic approach has not been described yet. Here we
introduce a family of models based on the most general features of group
decision making to show that the optimal distribution of competences is a
highly skewed function with a structured fat tail. Our results have been
obtained by optimizing the groups' compositions through identifying the best
performing distributions for both the competences and for the members'
flexibilities/pliancies. Potential applications include choosing the best
composition for a group intended to solve a given task.
