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Edited by William J. Baumol, New York University, New York, NY, and approved September 17, 2004 (received for review May 25, 2004)
Abstract
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.
Footnotes
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↵ § To whom correspondence should be addressed. E-mail: luhong{at}umich.edu.
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This paper was submitted directly (Track II) to the PNAS office.
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↵ ∥ In another set of computational experiments where a different problem was being solved, we consider agents with the same heuristics but whose perspectives vary. Similar results were found {Hong, L. & Page, S. E. (2002) Working paper, Diversity and Optimality [Loyola University (Chicago) and Univ. of Michigan (Ann Arbor)]}.
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↵ ** Mathematically, the expected diversity of two randomly selected agents equals 11/12 = 0.9183333.
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↵ †† Hong, L. & Page, S. E. (2002) Working paper, Diversity and Optimality [Loyola Univ. (Chicago) and Univ. of Michigan (Ann Arbor)].
- Copyright © 2004, The National Academy of Sciences
Groups of diverse problem solvers can outperform groups of high-ability problem solvers
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