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

Petra Ahrweiler | Zendy; Michel Schilperoord | Zendy; Andreas Pyka | Zendy; Nigel Gilbert | Zendy

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

Author(s) -

Petra Ahrweiler,

Michel Schilperoord,

Andreas Pyka,

Nigel Gilbert

Publication year - 2015

Publication title -

journal of artificial societies and social simulation

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.768

H-Index - 59

ISSN - 1460-7425

DOI - 10.18564/jasss.2927

Subject(s) - scope (computer science) , benchmark (surveying) , commission , european commission , ex ante , baseline (sea) , time horizon , set (abstract data type) , political science , computer science , business , operations research , environmental economics , economics , european union , finance , engineering , macroeconomics , economic policy , geodesy , law , programming language , geography

This paper presents the agent-based model INFSO-SKIN, which provides ex-ante evaluation of possible funding policies in Horizon 2020 for the European Commissionâ€™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 Commissionâ€™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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