气候变化领域集成服务门户(CCPortal): The diversity bonus in pooling local knowledge about complex problems

CCPortal

DOI	10.1073/pnas.2016887118
	The diversity bonus in pooling local knowledge about complex problems
	Aminpour P.; Gray S.A.; Singer A.; Scyphers S.B.; Jetter A.J.; Jordan R.; Robert Murphy R.M.; Grabowski J.H.
发表日期	2021
ISSN	00278424
卷号	118 期号:5
英文摘要	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. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Collective intelligence; Diversity; Local knowledge; Social-ecological systems; Sustainability
语种	英语
scopus关键词	article; benchmarking; causal model; cognitive map; ecosystem; fishery management; human; intelligence; Internet; Massachusetts; network analysis; nonhuman; stochastic model; striped bass; theoretical study
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/180796
作者单位	Department of Community Sustainability, Michigan State University, East Lansing, MI 48824, United States; Collective Intelligence Research Group, IT University of Copenhagen, Copenhagens, 2300, Denmark; Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ 86011, United States; Department of Marine and Environmental Sciences, Coastal Sustainability Institute, Northeastern University, Nahant, MA 01908, United States; Department of Engineering and Technology Management, Portland State University, Portland, OR 97201, United States; Fisheries, Aquatic Science, and Technology Laboratory, Alaska Pacific University, Anchorage, AK 99508, United States
推荐引用方式 GB/T 7714	Aminpour P.,Gray S.A.,Singer A.,et al. The diversity bonus in pooling local knowledge about complex problems[J],2021,118(5).
APA	Aminpour P..,Gray S.A..,Singer A..,Scyphers S.B..,Jetter A.J..,...&Grabowski J.H..(2021).The diversity bonus in pooling local knowledge about complex problems.Proceedings of the National Academy of Sciences of the United States of America,118(5).
MLA	Aminpour P.,et al."The diversity bonus in pooling local knowledge about complex problems".Proceedings of the National Academy of Sciences of the United States of America 118.5(2021).