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.

最近，理论家推测，与同质的群体相反，不同的群体可能具有相对优势[SE Page，The Diversity Bonus（2019）]-所有这些都导致解决复杂问题取得了更大的成功。这样一来，对复杂，相互交织的环境和社会问题的理解可能会受益于各种本地专业知识的整合。但是，经常通过基于实验室或计算的实验来支持这一假设，目前尚不清楚这些发现是否普遍适用于现实世界的复杂性。为了弥合这种鸿沟，我们将基于Internet的知识启发技术与集体智慧的理论原理相结合，以设计与当地利益相关者的实验。以马萨诸塞州的条纹鲈鱼渔业为例，我们汇集了以图形化认知图表示的资源利益相关者的本地知识，以产生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目评论显示，不同群体的模型优于同类群体的模型。通过随机网络分析进行的评估还表明，与同质组相比，多样化的组更能充分地建模复杂的反馈和相互依赖性。然后，我们使用我们的数据运行蒙特卡洛实验，其中利益相关者驱动的认知图的分布被随机复制并生成虚拟组。随机实验还预测，知识多样性可以提高小组的成功率，这是通过将小组模型与基于生态系统的渔业管理模型进行基准比较来衡量的。我们还强调指出，必须通过适当的聚合过程来缓和多样性，从而导致更加复杂而简约的模型。