IPSE’s research deals with Irish innovation networks facing the key challenges of economic recovery. The IPSE work package combines empirical research on issues identified as important for Irish innovation performance with computational methods such as network analysis, agent-based modelling and social simulation, to implement and test innovation policy scenarios. It is a key component of the UCD PRTLI Cycle 5 application, which focuses on innovation through its partnership with TCD, the UCD/TCD Innovation Alliance. Our research will concern both fields already firmly present in Ireland (e.g. entrepreneurship, finance, services, ICT, biotech-pharmaceuticals) and new fields for the Irish Smart Economy (e.g. green-tech sector, all-Ireland initiatives, regional and national policy and regulation issues).
The research plan directly contributes to the policy of the five Action Areas in the Government framework “Building Ireland’s Smart Economy” (2008), which defines an ambitious set of actions to reorganise the economy over the next five years. IPSE’s research themes are structured in various Sub-Projects (SPs) around these action areas.
For our high-quality strategic research in three interacting research, technology and development (RTD) sub-projects, we will use an innovative methodology: realistic simulation/computation handling large databases coming from empirical quantitative and qualitative research. This new “hard science” approach to innovation research aims at bringing together rigorous mathematics (for controlled computation) and empirical analyses (for realistic simulation). Using computation/simulation for innovation research, our interdisciplinary approach cuts across economics, social sciences, and computer science.
With IPSE, we will establish a computational policy laboratory in silico in order to inform Irish policy makers on optimal network structures for innovation performance adapted to regional and local capabilities.
IPSE’s work package is organised in five sub-projects (SPs), three RTD ones and two organisational (dissemination and management). The case studies in the empirical SPs 2 and 3 were selected because:
(i) they reflect a research need as both the projects and their interactions are areas of major importance to Irish industry and policy, but have not previously been addressed together;
(ii) the interdisciplinary approach to a combination of empirical research and modelling has potential to contribute to industrial practice and improve the use of such enabling technologies across the economy; (iii) from an academic perspective the projects combine challenging theoretical problems; and (iv) they address the requirements of the Programme for Research in Third-Level Institutions (PRTLI) Call to research relevant issues to build the “Smart Economy”, which is their common reference point.
All our case studies share an important feature: the empirical work in SP2 “Sectoral and geographical aspects of innovation” and SP3 “Firm level innovation and entrepreneurship” is organised in a way to collect and provide data and analyses for computation and modelling, which will be carried out by IRU’s Computational Policy Lab (SP1).
The results of IPSE are targeted to make innovation work for the Smart Economy. IPSE’s explicit output is to provide indications and directions for increasing the ability to sustain efficient innovation policy-making in Ireland (with an extension to offer guidelines for decision-making also to Irish business actors) in key areas:
• Technology transfer of universities
• Fourth level education in ICT and Biotechnology
The IPSE project could ultimately create methodological foundations for the sustainable transformation of innovative regions both within Ireland and beyond. Combining a unique set of modelling skills and empirical research excellence within a broad partnership, the project will deliver the first evidence-based agent based model (ABM) simulation of regional innovation networks with case-specific applications. This is, on the one hand, a big step for complexity science to combine computational thinking and empirical large data sets. Furthermore, providing regional innovation policy laboratories in silico is a revolutionary contribution to policy and innovation research. We will elaborate on other impacts issues in section 4.1 providing more details.
In IPSE, added value comes through new collaborations and new combinations of skill sets and competences. Many IPSE participants have not collaborated on innovation research previously. The new collaborations were created because participants have complementary skills and expertise and will create the opportunity to extend the collaborations in both research and teaching.
Ahrweiler, P. (2011) 'Modelling Theory Communities in Science'. Journal of Artificial Societies and Social Simulation, 14 (4(8)) (October 2011)
Edmonds, B., Gilbert, N., Ahrweiler, P. and Scharnhorst, A. (2011) 'Simulating the social Processes of Science'. Journal of Artificial Societies and Social Simulation, 14 (4) (October 2011)
Wiengarten, F., Pagell, M. and Fynes, B. (2012) “Supply chain environmental investments in dynamic industries: Comparing investment and performance differences with static industries”, International Journal of Production Economics, Vol. 135, No. 2, pp. 541-551.
Chavez, R., Fynes, B., Gimenez, C. and Wiengarten, F. (2012) “Assessing the effect of industry clockspeed on the supply chain management practice-performance relationship”, Supply Chain Management: An International Journal (accepted)
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