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            Systems Mapping

            How to build and use causal models of systems

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            Author(s)
            Barbrook-Johnson, Pete
            Penn, Alexandra S.
            Language
            English
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            Abstract
            This open access book explores a range of new and older systems mapping methods focused on representing causal relationships in systems. In a practical manner, it describes the methods and considers the differences between them; describes how to use them yourself; describes how to choose between and combine them; considers the role of data, evidence, and stakeholder opinion; and describes how they can be useful in a range of policy and research settings. This book provides a key starting point and general-purpose resource for understanding complex adaptive systems in practical, actionable, and participatory ways. The book successfully meets the growing need in a range of social, environmental, and policy challenges for a richer more nuanced, yet actionable and participatory understanding of the world. The authors provide a clear framework to alleviate any confusion about the use of appropriate terms and methods, enhance the appreciation of the value they can bring, and clearly explain the differences between approaches and the resulting outputs of mapping processes and analysis.
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/176977
            Keywords
            systems mapping; complexity methods; participatory modelling; modelling; complex systems; social science research methods; social science; participatory systems mapping
            DOI
            10.1007/978-3-031-01919-7
            ISBN
            9783031019197
            Publisher
            Springer Nature
            Publisher website
            http://www.springernature.com/oabooks
            Publication date and place
            Cham, 2022
            Imprint
            Palgrave Macmillan
            Pages
            186
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            Credits


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            • logo EUEuropean Union
              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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