Wisdom of stakeholder crowds in complex social-ecological systems
Wisdom of stakeholder crowds in complex social-ecological systems
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DOI：
10.1038/s41893-019-0467-z
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发表时间：
2020-01-13
影响因子：
27.6
通讯作者：
Arlinghaus, Robert
中科院分区：
文献类型：
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作者：
Aminpour, Payam;Gray, Steven A.;Arlinghaus, Robert
自然资源管理涉及受数据和知识限制影响的复杂关系。心智模型可以利用一群利益相关者的智慧。自然资源的可持续管理需要对人类和自然系统之间的复杂关系有足够的科学知识。由于数据稀缺和知识限制，这种理解在许多情况下很难实现。我们探索利用资源利益相关者的集体智慧来克服这一挑战的潜力。以渔业为例，我们展示了通过图形化心智模型将利益相关者持有的系统知识聚合起来，一群不同的资源使用者产生了一个与最佳科学理解相媲美的社会-生态关系系统模型。我们表明，来自不同资源用户群体的平均模型优于那些更同质的群体。然而，重要的是，我们发现来自较大个体样本的平均模型可能比来自较小样本的平均模型表现更差。然而，当在特定利益相关者的子组中平均心理模型并随后在子组模型中聚集时，效果是相反的。通过利用非科学家利益相关者的集体智慧，我们的工作确定了一种廉价但稳健的方法，以发展对复杂社会生态系统的科学理解。
Natural resource management involves complex relationships that are affected by data and knowledge limitations. Mental modelling can harness the wisdom of a crowd of stakeholders.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 non-scientist stakeholders.