Centre for Environmental Policy, Imperial College London, Weeks Hall, 16-18 Prince's Gardens, South Kensington, SW7 1NE, U.K.
Conserv Biol. 2019 Dec;33(6):1247-1255. doi: 10.1111/cobi.13335. Epub 2019 Apr 30.
Environmental decisions are often deferred to groups of experts, committees, or panels to develop climate policy, plan protected areas, or negotiate trade-offs for biodiversity conservation. There is, however, surprisingly little empirical research on the performance of group decision making related to the environment. We examined examples from a range of different disciplines, demonstrating the emergence of collective intelligence (CI) in the elicitation of quantitative estimates, crowdsourcing applications, and small-group problem solving. We explored the extent to which similar tools are used in environmental decision making. This revealed important gaps (e.g., a lack of integration of fundamental research in decision-making practice, absence of systematic evaluation frameworks) that obstruct mainstreaming of CI. By making judicious use of interdisciplinary learning opportunities, CI can be harnessed effectively to improve decision making in conservation and environmental management. To elicit reliable quantitative estimates an understanding of cognitive psychology and to optimize crowdsourcing artificial intelligence tools may need to be incorporated. The business literature offers insights into the importance of soft skills and diversity in team effectiveness. Environmental problems set a challenging and rich testing ground for collective-intelligence tools and frameworks. We argue this creates an opportunity for significant advancement in decision-making research and practice.
环境决策通常会推迟给专家组、委员会或专家组来制定气候政策、规划保护区或协商生物多样性保护的权衡取舍。然而,与环境相关的群体决策绩效的实证研究却少之又少。我们从一系列不同学科的例子中,展示了在定量估计的启发、众包应用和小团体解决问题中集体智慧(CI)的出现。我们探讨了类似工具在环境决策中的使用程度。这揭示了一些重要的差距(例如,在决策实践中缺乏基础研究的整合,缺乏系统的评估框架),这些差距阻碍了集体智慧的主流化。通过明智地利用跨学科的学习机会,可以有效地利用集体智慧来改善保护和环境管理中的决策。为了得出可靠的定量估计,需要了解认知心理学,并优化众包人工智能工具。商业文献深入探讨了软技能和团队多样性在团队有效性中的重要性。环境问题为集体智能工具和框架提供了富有挑战性和丰富的测试场。我们认为,这为决策研究和实践的重大进展创造了机会。