Payam Aminpour, Steven A. Gray, Antonie J. Jetter, Joshua E. Introne, Alison Singer and Robert Arlinghaus
Sustainable management of natural resources requires adequate scientific knowledge about complex relationships between
human and natural systems. Such understanding is difficult to achieve in many contexts due to data scarcity and knowledge limitations.
We explore the potential of harnessing the collective intelligence of resource stakeholders to overcome this challenge.
Using a fisheries example, we show that by aggregating the system knowledge held by stakeholders through graphical mental
models, a crowd of diverse resource users produces a system model of social–ecological relationships that is comparable to the
best scientific understanding. We show that the averaged model from a crowd of diverse resource users outperforms those of
more homogeneous groups. Importantly, however, we find that the averaged model from a larger sample of individuals can perform
worse than one constructed from a smaller sample. However, when averaging mental models within stakeholder-specific
subgroups and subsequently aggregating across subgroup models, the effect is reversed. Our work identifies an inexpensive,
yet robust way to develop scientific understanding of complex social–ecological systems by leveraging the collective wisdom of