Department of Community Sustainability, Michigan State University, East Lansing, MI 48824;
Department of Community Sustainability, Michigan State University, East Lansing, MI 48824.
Proc Natl Acad Sci U S A. 2021 Feb 2;118(5). doi: 10.1073/pnas.2016887118.
Recently, theoreticians have hypothesized that diverse groups, as opposed to groups that are homogeneous, may have relative merits [S. E. Page, (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.
最近，理论家假设，与同质群体相比，多样化的群体可能具有相对优势[ S. E. Page，（2019）]——所有这些都能使解决复杂问题取得更大的成功。因此，理解复杂的、相互交织的环境和社会问题可能会受益于整合各种类型的本地专业知识。然而，支持这一假设的努力经常是通过基于实验室或计算的实验来进行的，并且不清楚这些发现是否可以推广到现实世界的复杂性中。为了弥合这一差距，我们结合基于互联网的知识 elicitation 技术和集体智慧的理论原则，设计了一个与本地利益相关者合作的实验。我们使用马萨诸塞州条纹鲈鱼渔业的案例，将图形认知图表示的资源利益相关者的本地知识汇集起来，生成与渔业生态系统相关的复杂社会-生态相互依存关系的因果模型。来自科学专家小组的盲审表明，不同群体的模型优于同质群体的模型。通过随机网络分析的评估也表明，与同质群体相比，多样化群体更能充分模拟复杂的反馈和相互依存关系。然后，我们使用我们的数据进行蒙特卡罗实验，其中随机复制利益相关者驱动的认知图的分布，并生成虚拟群体。随机实验还预测，知识多样性可以提高群体的成功，这是通过将群体模型与基于生态系统的渔业管理模型进行基准测试来衡量的。我们还强调，多样性必须通过适当的聚合过程来调节，从而产生更复杂但更简洁的模型。
