Knowledge Creation and Research Policy in Science-Based Industries: An Empirical Agent-Based Model

There is an increasing demand for ex-ante impact assessment of policy measures in the field of research. Existing methods to explore the effects of policy interventions in innovation systems often lack transparency or just extrapolate current trends, neglecting real-world complexities. Therefore, we propose a simulation approach and develop an empirical agent-based model (ABM) of knowledge creation in a localized system of researching firms in a science-based industry. With its strong emphasis on empirical calibration, the model represents the Austrian biotechnology industry. In our simulations, effects of different public research policies on the knowledge output—measured by the patent portfolio—are under scrutiny. By this, the study contributes to the development of ABMs in two main aspects: (1) Building on an existing concept of knowledge representation, we advance the model of individual and collective knowledge creation in firms by conceptualizing policy intervention and corresponding output indicators. (2) We go beyond symbolic ABMs of knowledge creation by using patent data as knowledge representations, adopting an elaborate empirical initialisation and calibration strategy using company data. We utilise econometric techniques to generate an industry-specific fitness function that determines the model output. The model allows for analysing the effect of different public research funding schemes on the technology profile of the Austrian biotechnology innovation system. The results demonstrate that an empirically calibrated and transparent model design increases credibility and robustness of the ABM approach in the context of ex-ante impact assessment of public research policy in an industry-specific and national context.