Fuzzy Cognitive Maps of Social-Ecological Complexity: Applying Mental Modeler to the Bonneville Salt Flats

IF 3.1 3区 环境科学与生态学 Q2 ECOLOGY
Michael P. Blacketer , Matthew T.J. Brownlee , Elizabeth D. Baldwin , Brenda B. Bowen
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引用次数: 4

Abstract

Although often limited in terms of extent or accuracy, mental models—i.e., explanations of the surrounding world and how things work within it—provide confidence and frameworks to navigate life's uncertainties. Unfortunately, differing and yet similar mental models held collectively by groups can lead to problematic behavior, misunderstandings, and conflict on large scales. Such challenges are likely familiar to natural resource managers who, in the course of their work, must consider issues that are neither simple nor exclusively ecological or social in nature. Building mental models of various groups’ understanding of a complex natural resource may help managers address the impacts of resource-related behaviors but can be a difficult task when collecting modeling data from large and diverse user groups. Using a sequential, exploratory approach, our study addresses the utility of surrogate mental modeling to explore (a) mental models held by key players from six stakeholder groups associated with Utah's Bonneville Salt Flats (US), and (b) whether these key players were confident that their personal subjective models represented their own group's thinking about Bonneville. We sought to illuminate and compare stakeholder groups’ mental models of subjectively important social and ecological concepts related to Bonneville through the use of fuzzy cognitive maps (FCMs; i.e., semi-quantitative representations of mental models) constructed in Mental Modeler. Analysis revealed differences among groups’ FCMs and levels of perceived complexity, as well as areas of agreement regarding the strength, direction, and character of certain social-ecological relationships. Intersections and divergences in stakeholder mental models may provide logical starting points for communal knowledge-building that can perhaps lessen tension among groups attributable to conceptual misunderstandings of resource-specific complexity.

社会-生态复杂性的模糊认知图:应用心理建模器到Bonneville盐滩
尽管在范围和准确性方面经常受到限制,心智模型——即:对周围世界的解释以及事物是如何在其中运作的——为驾驭生活的不确定性提供了信心和框架。不幸的是,群体共同持有的不同而又相似的心理模型可能导致问题行为、误解和大规模冲突。自然资源管理人员很可能熟悉这些挑战,他们在工作过程中必须考虑既不简单也不完全是生态或社会性质的问题。建立不同群体对复杂自然资源的理解的心理模型可能有助于管理人员解决与资源相关的行为的影响,但当从大型和不同的用户群体收集建模数据时,这可能是一项艰巨的任务。使用顺序的探索性方法,我们的研究解决了替代心理模型的效用,以探索(a)与犹他州邦纳维尔盐滩(美国)相关的六个利益相关者群体的关键参与者所持有的心理模型,以及(b)这些关键参与者是否相信他们的个人主观模型代表了他们自己群体对邦纳维尔的看法。我们试图阐明和比较利益相关者群体的心理模型,通过使用模糊认知图(fcm;在mental Modeler中构建的心智模型(即心智模型的半定量表征)。分析揭示了不同群体的fcm和感知复杂性水平之间的差异,以及关于某些社会生态关系的强度、方向和特征的一致领域。利益相关者心智模型中的交集和分歧可能为公共知识建设提供逻辑起点,这可能会减轻由于对资源特定复杂性的概念误解而导致的群体之间的紧张关系。
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来源期刊
Ecological Complexity
Ecological Complexity 环境科学-生态学
CiteScore
7.10
自引率
0.00%
发文量
24
审稿时长
3 months
期刊介绍: Ecological Complexity is an international journal devoted to the publication of high quality, peer-reviewed articles on all aspects of biocomplexity in the environment, theoretical ecology, and special issues on topics of current interest. The scope of the journal is wide and interdisciplinary with an integrated and quantitative approach. The journal particularly encourages submission of papers that integrate natural and social processes at appropriately broad spatio-temporal scales. Ecological Complexity will publish research into the following areas: • All aspects of biocomplexity in the environment and theoretical ecology • Ecosystems and biospheres as complex adaptive systems • Self-organization of spatially extended ecosystems • Emergent properties and structures of complex ecosystems • Ecological pattern formation in space and time • The role of biophysical constraints and evolutionary attractors on species assemblages • Ecological scaling (scale invariance, scale covariance and across scale dynamics), allometry, and hierarchy theory • Ecological topology and networks • Studies towards an ecology of complex systems • Complex systems approaches for the study of dynamic human-environment interactions • Using knowledge of nonlinear phenomena to better guide policy development for adaptation strategies and mitigation to environmental change • New tools and methods for studying ecological complexity
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