The Great Recession that hit the world in 2007 had big consequences for Ireland. With many financial institutions risking collapse, the Irish Government instigated a bank bailout plan that led to many years of austerity policies. At the same time, in 2008, the Department of the Taoiseach published a framework paper, identifying innovation as a key opportunity for economic recovery, in light of Ireland’s competitive advantages in tech-related fields. However, post-bailout austerity left little place (and budget) for policymakers to try new things.

Generating policy scenarios to stimulate innovation

In that context, University College Dublin led the development of a simulation tool, generating scenarios to foster greater innovation in Ireland. The project, Innovation Policy Simulation for the Smart Economy (IPSE), was funded by the European Regional Development Fund and the Irish Programme for Research in Third-Level Institutions between 2010 and 2015. It uses a version of the Simulating Knowledge dynamics in Innovation Networks (SKIN) platform, a multi-agent based model, developed by academics Petra Ahrweiler, Nigel Gilbert and Andreas Pyka. SKIN models basic markets while introducing complex firm knowledge dynamics into them.

In practice, IPSE was adapted to model the Irish economy, its main sectors and regional innovation networks, to assist in turning Ireland into a global hub for innovation. It uses data from Knowledge Transfer Ireland and the European Patent Office and questions asked by Irish policymakers, including, for example, funding decisions or the kind of institutional models that best promote innovation.

With precise regional profiles and sector information, policymakers are able to simulate the effect of policy instruments before rolling them out in the real world, and to better understand the potential drivers of innovation, their evolution over time, and their impact.

Data and assumptions: The benefits and limits of simulation

The IPSE tool has been very well received and successful among policymakers, allowing them to detect high potential areas and de-risk decision-making processes in spite of strict budget constraints. Additionally, through the research needed to build the tool, large amounts of data on the Irish economy have been made available to and easily visualised by policymakers, building their capacity to analyse ecosystem needs and policy implications.

Simulation models are more powerful than linear ones for understanding complex systems like innovation, but models like IPSE also have limits of their own. While trustworthy models can only be built on very robust, context-relevant data, they are also always based on a set of assumptions, which are pre-established and can never be fully objective.

As a result, it is crucial to keep in mind that simulations and IPSE-like models cannot always provide reliable predictions, but rather an increased ability to understand and analyse information and trends.

Thanks to Petra Ahrweiler from Johannes Gutenberg-University Mainz for taking the time to speak with us.