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Policy Co-creation in the Era of Data Science

Sluban, Borut; Battiston, Stefano

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        <foaf:name>Sluban, Borut</foaf:name>
            <foaf:name>University of Zurich</foaf:name>
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        <foaf:name>Battiston, Stefano</foaf:name>
            <foaf:name>University of Zurich</foaf:name>
    <dct:title>Policy Co-creation in the Era of Data Science</dct:title>
    <dct:issued rdf:datatype="">2017</dct:issued>
    <dcat:keyword>policy co-creation</dcat:keyword>
    <dcat:keyword>citizen engagement</dcat:keyword>
    <dcat:keyword>social media</dcat:keyword>
    <dcat:keyword>text mining</dcat:keyword>
    <dcat:keyword>network analysis</dcat:keyword>
    <dct:issued rdf:datatype="">2017-09-15</dct:issued>
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    <dct:description>&lt;p&gt;Policy co-creation holds promises to increase citizens’ trust in institutions and policy acceptance but no established approach has emerged yet. We propose a policy co-creation framework that leverages on complexity science and data science to involve both experts and stakeholders from the policy area, as well as the wider audience. Our iterative process consists of three stages: (i) identification of the main policy issues by a balanced expert group and launch of a non-technical stakeholder survey, (ii) analysis of stakeholders’ positions towards the issues via a Policy Network Map, and (iii) assessing the public leaning towards the issues using text mining techniques on big data from the social media. The output from stage two and three feeds back into a new round of assessments until a target level of support is reached. At this point, the expert group can formulate its final recommendations that will be used by policy makers to prepare a first policy proposal. We illustrate the feasibility of the proposed workflow by means of use cases from on-going and previous work.&lt;/p&gt;</dct:description>
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