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2018 | OriginalPaper | Buchkapitel

Navigating Complex Systems for Policymaking Using Simple Software Tools

verfasst von : Philippe J. Giabbanelli, Magda Baniukiewicz

Erschienen in: Advanced Data Analytics in Health

Verlag: Springer International Publishing

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Abstract

Comprehensive maps of selected issues such as obesity have been developed to list the key factors and their interactions, thus defining a network where factors (e.g., weight bias, disordered eating) are represented as nodes while causal connections are captured as edges. While such maps contain a wealth of information, they can be seen as a maze which practitioners and policymakers struggle to explore. For instance, the Foresight Obesity Map has been depicted as an ‘almost incomprehensible web of interconnectedness’. Rather than presenting maps as static images, we posit that their value can be unlocked through interactive visualizations. Specifically, we present five required functionalities for interactive visualizations, based on experimental studies and key concepts of systems thinking in public policy. These functionalities include shifting from simple ‘policy inputs’ to loops, capturing what unfolds between an intervention and its evaluation, and accounting for the rippling effects of interventions. We reviewed ten software that support different policy purposes (visualization, argumentation, or modeling) and found that none supports four or all five of the functionalities listed. We thus created a new open-source software, ActionableSystems. The chapter details its design principles and how it implements the five functionalities. The use of the software to address policy-relevant questions is briefly illustrated, taking obesity and public health nutrition as guiding example. We conclude with open questions for software development and public health informatics, emphasizing the need to design software that supports a more inclusive approach to policy-making and a more comprehensive exploration of complex systems.

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Fußnoten
1
Causal networks are formed of directed weighted relationships [14, 15], to which Fuzzy Cognitive Maps add an inference engine allowing to simulate the consequences of these relationships and thus test their validity [16, 17]. System Dynamics go even beyond, by including time effects and lags [18, 19].
 
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Metadaten
Titel
Navigating Complex Systems for Policymaking Using Simple Software Tools
verfasst von
Philippe J. Giabbanelli
Magda Baniukiewicz
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-77911-9_2