The diversity bonus in pooling local knowledge about complex problems.

Affiliation

Aminpour P(1), Gray SA(2)(3), Singer A(4), Scyphers SB(5), Jetter AJ(6), Jordan R(2), Murphy R Jr(7), Grabowski JH(5).
Author information:
(1)Department of Community Sustainability, Michigan State University, East Lansing, MI 48824; [Email]
(2)Department of Community Sustainability, Michigan State University, East Lansing, MI 48824.
(3)Collective Intelligence Research Group, The IT University of Copenhagen, 2300 Copenhagens, Denmark.
(4)Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ 86011.
(5)Department of Marine and Environmental Sciences, Coastal Sustainability Institute, Northeastern University, Nahant, MA 01908.
(6)Department of Engineering and Technology Management, Portland State University, Portland, OR 97201.
(7)Fisheries, Aquatic Science, and Technology Laboratory, Alaska Pacific University, Anchorage, AK 99508.

Abstract

Recently, theoreticians have hypothesized that diverse groups, as opposed to groups that are homogeneous, may have relative merits [S. E. Page, The Diversity Bonus (2019)]-all of which lead to more success in solving complex problems. As such, understanding complex, intertwined environmental and social issues may benefit from the integration of diverse types of local expertise. However, efforts to support this hypothesis have been frequently made through laboratory-based or computational experiments, and it is unclear whether these discoveries generalize to real-world complexities. To bridge this divide, we combine an Internet-based knowledge elicitation technique with theoretical principles of collective intelligence to design an experiment with local stakeholders. Using a case of striped bass fisheries in Massachusetts, we pool the local knowledge of resource stakeholders represented by graphical cognitive maps to produce a causal model of complex social-ecological interdependencies associated with fisheries ecosystems. Blinded reviews from a scientific expert panel revealed that the models of diverse groups outranked those from homogeneous groups. Evaluation via stochastic network analysis also indicated that a diverse group more adequately modeled complex feedbacks and interdependencies than homogeneous groups. We then used our data to run Monte Carlo experiments wherein the distributions of stakeholder-driven cognitive maps were randomly reproduced and virtual groups were generated. Random experiments also predicted that knowledge diversity improves group success, which was measured by benchmarking group models against an ecosystem-based fishery management model. We also highlight that diversity must be moderated through a proper aggregation process, leading to more complex yet parsimonious models.