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The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
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Maintaining Transient Diversity Is a General Principle for Improving Collective Problem Solving.

Paul E Smaldino1,2, Cody Moser1, Alejandro Pérez Velilla1

  • 1Department of Cognitive & Information Sciences, University of California, Merced.

Perspectives on Psychological Science : a Journal of the Association for Psychological Science
|June 27, 2023
PubMed
Summary

Cooperative teams improve problem-solving by increasing transient diversity, which explores more solutions before consensus. This enhances collective intelligence but may increase the time to reach a final decision.

Keywords:
allied field: computer scienceallied field: sociologycognitioncollective intelligenceculture/diversitydiversityformal modelsintragroup processesnetworkssocial cognition

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Area of Science:

  • Cognitive Science
  • Social Psychology
  • Evolutionary Biology

Background:

  • Human teams excel at complex problem-solving, with consensus-building mechanisms enhancing solution quality.
  • Many consensus mechanisms function by temporarily increasing solution diversity within the group.

Approach:

  • This study argues that mechanisms increasing transient diversity, operating at individual, interpersonal, and group levels, improve collective problem-solving.
  • Evidence is synthesized from empirical studies and formal models (e.g., multiarmed bandits, NK landscapes, evolutionary-transmission models).

Key Points:

  • Transient diversity is boosted by expanding the solution search space or slowing information diffusion, delaying consensus.
  • Increased transient diversity enhances solution quality at the expense of time.
  • Exceptions arise in simple problems solvable by trial-and-error or when team incentives are misaligned.

Conclusions:

  • Mechanisms promoting transient diversity are crucial for collective intelligence and innovation.
  • Understanding these dynamics has implications for problem-solving, innovation, and cumulative cultural evolution.
  • This framework offers insights into optimizing group performance in complex tasks.