Agent-Based Modelling of Complexity in Life Sciences. With a special emphasis on the impact of public funding on research activities

This dissertation aims to explore the effects of public research funds on innovation in life sciences by elaborating an agent-based model of a regional life sciences innovation system. Life sciences are a research-intensive field and though have a major impact on economic growth. Industrial and scientific organisations operate in a highly dynamic environment characterised by fast-expanding scientific knowledge and scattered expertise, and the ability to create innovations is crucial for the competitiveness of firms. In general, organisations operate under high uncertainty, and, in order to keep pace with innovation, they engage in research networks. The demand for external knowledge and a high degree of tacitness lead the agents to interdisciplinary cooperation with regional and extra-regional partners, facilitating different kinds of knowledge exchange. Moreover, the interactions within collaboration networks provoke feedback loops on the agents´ behaviour and strategies, thus generating non-linear dynamics and evoking additional unpredictability. Interdependencies among agents are manifold, fostering dynamics and complexity in the system. Extending previous models of innovation networks, this specific model serves as a tool for policy makers to explore potential future impacts of different public research funding mixes. In order to improve the credibility and usability of the simulation findings, a modelling strategy is adopted that accounts for a high degree of empirical detail in the spirit of history-friendly modelling. Empirical calibration and testing is provided by detailed agent-level data on research activities, innovation output and performance of life sciences organisations in the innovation system of the Vienna Region during the period 1999 to 2010.