The CROSSOVER Research Roadmap - Conclusions: Closing the Loop of Policy-Making 2.0 The research challenges identified so far are not just a simple collection of research issues, but an integrated bundle of innovative solutions that together can lead to a paradigm shift in policy-making. The CROSSROAD roadmap already identified an integrated approach based on a technology layer model with three layers: data, analysis and decision/action: * The data layer provides new information that was previously not available. * The analysis layer provides a new perspective and understanding of data. * The behavioural change layer acts on the incentives and barriers to action and behaviour. The CROSSOVER project refines this approach by linking each of the research challenge to a specific challenges in the framework ... The present roadmap represents the starting point for collaboration. It will be published in commentable format for practitioners and researchers to comment and revise. In particular, the present roadmap aiming to become a living platform, comments will be sought: * From all stakeholders, on the relevance, clarity and completeness of the proposed research challenges * From researchers, on the actual research carried out on these challenges * From policy-makers, on the actual usage of these tools and methodologies, lessons learnt and challenges encountered. In addition, a dedicated survey of policy-makers on their needs and challenges will be carried out, in order to fine-tune the final roadmap to their needs. CROSSOVER Project CP _9dc50786-0341-11e2-851f-09c7522abe92 Researchers Comments will be sought from researchers, on the actual research carried out on these challenges Policy-Makers Comments will be sought from policy-makers, on the actual usage of these tools and methodologies, lessons learnt and challenges encountered ... a radically different context for policy-making 2.0. _2fe4c5bc-1c69-11e2-ba80-5ec6c7ccd5b7 To relate each of the research challenges in the policy-making cycle. _2fe4ca44-1c69-11e2-ba80-5ec6c7ccd5b7 Modeling On policy modelling and simulation, thanks to standardisation and reusability of models and tools, system thinking and modelling applied to policy impact assessment has become pervasive throughout government activities, and is no longer limited to high-profile regulation. Model building and simulation is carried out directly by the responsible civil servants, collaborating with different domain experts and colleagues from other departments. Visual dynamic interfaces allow users to directly manipulate the simulation parameters and the underlying model. Simulation Standardisation Reusability Productization Policy modelling software becomes productized and engineered, and is delivered as-a-service, through the cloud, bundled with added-value services and multidisciplinary support including mathematical, physics, economic, social, policy and domain-specific scientific support. Engineering Cloud Services Added-Value Services Multidisciplinary Support Mathematics Physics Economics Scientific Support. Interoperability Standards Cloud-based interoperability standards ensure full reusability and composability of models across platforms and software. Composability Dynamics System policy models are dynamically built, validated and adjusted taking into account massive dataset of heterogeneous data with different degrees of validity, including sensor-based structured data and citizens-generated unstructured opinions and comments. By integrating top-down and bottom-up agent based approaches, the models are able to better explain human behaviour and to anticipate possible tipping points and domino effects. Big Data Heterogeneous Data Structured Data Unstructured Data Opinions Comments Top-Down Approaches Bottom-Up Approaches Human Behaviour Tipping Points Domino Effects Collaborative Governance On collaborative governance, policy-making leverages collective intelligence and collective action. It accounts for the greater policentricity of our governance system. While traditional tools are designed for the public decision-makers, these research challenges are more symmetric by nature, in order to engage stakeholders all through the phases of the policy-making cycle. Thanks to visualisation and design, it is able to reach out to new stakeholders and lower the barriers to entry in the policy discussions. Policy-making 2.0 is not only designed to be more effective, but also more participatory. Policy-Making Collective Intelligence Collective Action Policentricity Stakeholder Engagement Visualisation Design Participation Effectiveness Agenda Setting Identify and analyze the problem. _2fe4cb3e-1c69-11e2-ba80-5ec6c7ccd5b7 1 The policy cycle starts with the agenda setting phase, where the problem is identified and analyzed. In this section, visualization and opinion mining can help to identify the problems at an early stage. Advanced modelling techniques are then used to untangle the casual relationships behind the problem, understanding the causal roots that need to be addressed by policy. Visualization Visualize the problem. _2fe4cbd4-1c69-11e2-ba80-5ec6c7ccd5b7 1.1 e755bfc2-683a-447c-b834-6bb256677ce0 Opinion Mining Mine opinions about the problem. _2fe4cc56-1c69-11e2-ba80-5ec6c7ccd5b7 1.2 f9a1cdb4-8425-4967-a2a7-e4b6bb92713b Advanced Modelling Techniques Use advanced modelling techniques to untangle the casual relationships behind the problem and understand the causal roots that need to be addressed by policy. _2fe4ccec-1c69-11e2-ba80-5ec6c7ccd5b7 1.3 7e73ccd1-1abd-4e07-a63d-7e8746a69af7 Policy Design Design the policy. _2fe4cd78-1c69-11e2-ba80-5ec6c7ccd5b7 2 Once the problem is clearly spelled out, we move to the policy design phase, where collaborative solutions are useful to identify the widest range of options, by leveraging collective intelligence. In order to facilitate the choice of the most effective option, immersive simulations support decision-makers by taking into account unexpected impacts and relationships. Collaborative governance enables then to develop further and fine-tune the most effective option, for example through commentable documents. Option Identification Collaborate to identify the widest range of options, by leveraging collective intelligence. _2fe4ce04-1c69-11e2-ba80-5ec6c7ccd5b7 2.1 fb6a87a0-f14b-46f6-b4e7-ac8b49535922 Option Selection Use immersive simulations to support decision-makers in the choice of the most effective option by taking into account unexpected impacts and relationships. _2fe4cecc-1c69-11e2-ba80-5ec6c7ccd5b7 2.2 f4e0b4c8-f6cb-4f68-aa4e-ecfae1fedc77 Collaborative Governance Enable further development and fine-tuning of the most effective option through collaborative governance, such as commentable documents. _2fe4cf62-1c69-11e2-ba80-5ec6c7ccd5b7 2.3 22699b13-f31d-4145-a0b5-16cd7de8f4d3 Policy Implementation Implement the policy. _2fe4cff8-1c69-11e2-ba80-5ec6c7ccd5b7 3 Once the option is developed and adopted, we enter into policy implementation. In this phase, it is crucial to ensure awareness, buy-in and collaboration from the widest range of stakeholders: social network analysis, crowdsourcing and serious gaming are useful to deliver this. Awareness, Buy-In & Collaboration Ensure awareness, buy-in and collaboration from the widest range of stakeholders. _2fe4d0a2-1c69-11e2-ba80-5ec6c7ccd5b7 3.1 Social network analysis, crowdsourcing and serious gaming are useful to deliver this. 59ffe875-b74a-4700-abc6-8fde24c77aa1 Social Network Analysis _2fe4d138-1c69-11e2-ba80-5ec6c7ccd5b7 3.1.1 840b8189-f1bc-41cb-a50d-785b6eb29ee7 Crowdsourcing _2fe4d1ce-1c69-11e2-ba80-5ec6c7ccd5b7 3.1.2 3a727e58-6ef4-4ae9-99b0-37d8a984768f Serious Gaming _2fe4d282-1c69-11e2-ba80-5ec6c7ccd5b7 3.1.3 f6d3255d-e9b9-4490-a37d-20580a7b2d41 Monitoring & Evaluation Monitor and evaluate the policy. _2fe4d322-1c69-11e2-ba80-5ec6c7ccd5b7 4 Already during this implementation, we move into the monitoring and evaluation. Open data allow stakeholders and decision makers to better monitor execution; together with sentiment analysis, they can be used to evaluate the impact of the policy, also through advanced visualization techniques. Open Data Allow stakeholders and decision makers to better monitor execution through open data. _2fe4d3c2-1c69-11e2-ba80-5ec6c7ccd5b7 4.1 3c0b2b11-455a-452c-abca-caa8b1b5e80c Sentiment Analysis Use sentiment analysis to evaluate the impact of the policy. _2fe4d476-1c69-11e2-ba80-5ec6c7ccd5b7 4.2 2becfdff-fefe-468a-9000-01c30b7d8898 Visualization Use advanced visualization techniques to evaluate the impact of the policy. _2fe4d516-1c69-11e2-ba80-5ec6c7ccd5b7 4.3 b858d4a2-c0c3-4e55-9eb3-723af0fdd80c 2012-10-22 http://www.crossover-project.eu/ResearchRoadmap/ClosingtheLoopofPolicyMaking20.aspx Owen Ambur Owen.Ambur@verizon.net Submit error.