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.
Hong
Lu
L
Michigan Business School and Complex Systems, University of Michigan, Ann Arbor, MI 48109-1234, USA. luhong@umich.edu
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Scott E
SE