Groups of Diverse Problem Solvers Can Outperform Groups of High-Ability Problem Solvers

We introduce a general framework for modeling functionally diverse problem solving agents. In this framework, problem solving agents possess representations of problems and algorithms that they use to locate solutions. We use this framework to establish a result relevant to group composition. We find that when selecting a problem solving team from a diverse population of intelligent agents, a team of randomly selected agents outperforms a team comprised of the best performing agents. This result relies on the intuition that as the initial pool of problem solvers becomes large, the best performing agents necessarily become similar in the space of problem solvers. Their relatively greater ability is more than offset by their lack of problem solving diversity