The diversity bonus in pooling local knowledge about complex problems
The diversity bonus in pooling local knowledge about complex problems
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DOI：
10.1073/pnas.2016887118
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发表时间：
2021-02-02
影响因子：
11.1
通讯作者：
Grabowski, Jonathan H.
中科院分区：
文献类型：
--
作者：
Aminpour, Payam;Gray, Steven A.;Grabowski, Jonathan H.
最近，理论家们假设不同的群体，相对于同质的群体，可能有相对的优点[S。E. Page，The Diversity Bonus（2019）]-所有这些都导致在解决复杂问题方面取得更多成功。因此，了解复杂的、相互交织的环境和社会问题可能受益于整合各种类型的当地专门知识。然而，支持这一假设的努力经常是通过实验室或计算实验进行的，目前还不清楚这些发现是否适用于现实世界的复杂性。为了弥合这一鸿沟，我们结合联合收割机基于互联网的知识启发技术与集体智慧的理论原则，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，我们汇集了由图形认知地图所代表的资源利益相关者的当地知识，以产生与渔业生态系统相关的复杂的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法审查显示，不同群体的模型排名高于同质群体。通过随机网络分析的评估也表明，一个不同的群体更充分地模拟复杂的反馈和相互依赖性比同质群体。然后，我们使用我们的数据来运行蒙特卡罗实验，其中随机复制了由持有者驱动的认知地图的分布，并生成了虚拟组。随机实验还预测，知识多样性提高了群体的成功，这是衡量基准组模型对基于生态系统的渔业管理模式。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约的模型。
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.