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Modelling Research Policy: Ex-Ante Evaluation of Complex Policy Instruments

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Abstract

This paper presents the agent-based model INFSO-SKIN, which provides ex-ante evaluation of possible funding policies in Horizon 2020 for the European Commission’s DG Information Society and Media (DG INFSO). Informed by a large dataset recording the details of funded projects, the simulation model is set up to reproduce and assess the funding strategies, the funded organisations and projects, and the resulting network structures of the Commission’s Framework 7 (FP7) programme. To address the evaluative questions of DG INFSO, this model, extrapolated into the future without any policy changes, is taken as an evidence-based benchmark for further experiments. Against this baseline scenario the following example policy changes are tested: (i) What if there were changes to the thematic scope of the programme? (ii) What if there were changes to the instruments of funding? (iii) What if there were changes to the overall amount of programme funding? (iv) What if there were changes to increase Small and Medium Enterprise (SME) participation? The results of these simulation experiments reveal some likely scenarios as policy options for Horizon 2020. The paper thus demonstrates that realistic modelling with a close data-to-model link can directly provide policy advice.

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  • Petra Ahrweiler & Michel Schilperoord & Andreas Pyka & Nigel Gilbert, 2015. "Modelling Research Policy: Ex-Ante Evaluation of Complex Policy Instruments," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(4), pages 1-5.
    • Handle: RePEc:jas:jasssj:2015-49-3
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    1. Nigel Gilbert & Andreas Pyka & Petra Ahrweiler, 2001. "Innovation Networks - a Simulation Approach," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 4(3), pages 1-8.
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    1. Vivek Shastry & D Cale Reeves & Nicholas Willems & Varun Rai, 2022. "Policy and behavioral response to shock events: An agent-based model of the effectiveness and equity of policy design features," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-21, January.
    2. Feliciani, Thomas & Morreau, Michael & Luo, Junwen & Lucas, Pablo & Shankar, Kalpana, 2022. "Designing grant-review panels for better funding decisions: Lessons from an empirically calibrated simulation model," Research Policy, Elsevier, vol. 51(4).
    3. Petra Ahrweiler, 2017. "Agent-based simulation for science, technology, and innovation policy," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 391-415, January.
    4. Ekici, Ahmet & Önsel Ekici, Şule, 2021. "Understanding and managing complexity through Bayesian network approach: The case of bribery in business transactions," Journal of Business Research, Elsevier, vol. 129(C), pages 757-773.
    5. Nigel Gilbert & Petra Ahrweiler & Pete Barbrook-Johnson & Kavin Preethi Narasimhan & Helen Wilkinson, 2018. "Computational Modelling of Public Policy: Reflections on Practice," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(1), pages 1-14.
    6. Marie Lisa Kapeller & Georg Jäger, 2020. "Threat and Anxiety in the Climate Debate—An Agent-Based Model to Investigate Climate Scepticism and Pro-Environmental Behaviour," Sustainability, MDPI, vol. 12(5), pages 1-25, February.
    7. Flaminio Squazzoni & J. Gareth Polhill & Bruce Edmonds & Petra Ahrweiler & Patrycja Antosz & Geeske Scholz & Emile Chappin & Melania Borit & Harko Verhagen & Francesca Giardini & Nigel Gilbert, 2020. "Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(2), pages 1-10.
    8. Bernardo Alves Furtado & Gustavo Onofre Andre~ao, 2022. "Machine Learning Simulates Agent-Based Model Towards Policy," Papers 2203.02576, arXiv.org, revised Nov 2022.

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