The diversity bonus in pooling local knowledge about complex problems.

Area of Science:

• Environmental science
• Social-ecological systems
• Collective intelligence

Background:

• Theoretical work suggests diverse groups outperform homogeneous ones in complex problem-solving.
• Previous studies often relied on lab or computational experiments, limiting real-world applicability.
• Understanding complex environmental and social issues requires integrating diverse local expertise.

Purpose of the Study:

• To test the hypothesis that diverse local stakeholder groups generate superior models of social-ecological systems.
• To bridge the gap between theoretical findings and real-world complexities in environmental management.
• To develop and apply an experimental approach combining knowledge elicitation and collective intelligence principles.

Main Methods:

• An Internet-based knowledge elicitation technique was used with local stakeholders in Massachusetts.
• Graphical cognitive maps were created by diverse and homogeneous groups of striped bass fishery stakeholders.
• Stochastic network analysis and Monte Carlo simulations were employed to evaluate model performance.

Main Results:

• Models generated by diverse groups were rated higher by scientific experts than those from homogeneous groups.
• Diverse groups produced models that better captured complex feedbacks and interdependencies in social-ecological systems.
• Simulations predicted that knowledge diversity enhances group success in modeling fisheries ecosystems.

Conclusions:

• Diverse local stakeholder engagement is crucial for effectively modeling complex social-ecological interdependencies.
• The study validates the benefits of diversity in real-world environmental problem-solving.
• Effective aggregation processes are necessary to moderate diversity for creating complex yet parsimonious models.