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

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Innovation Networks for Regional Development

Part of the book series: Economic Complexity and Evolution ((ECAE))

Abstract

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.

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Notes

  1. 1.

    We use the acronym ABM to refer to both “agent-based-modelling” and “agent-based model”.

  2. 2.

    Note: \( {\overline{T}}_{jm} \) is chosen from the set of technology classes T based on an empirical similarity measure given by the Jaccard index of technology classes (see Sect. 4.1).

  3. 3.

    Strictly speaking, the notion of spillover is not a deliberate actor strategy since it describes a phenomenon that occurs unintentionally within a population. Nevertheless, in the model the process can be formally conceptualised in the same procedure as the two other research strategies (see also Sect. 3.2).

  4. 4.

    Note that the identical number of total technology classes and agents (i.e. 61) is not intentional but stems from the empirical initialisation of the model.

  5. 5.

    The empirical values of J lm used in the current application (61-by-61 matrix) can be obtained from the authors.

  6. 6.

    http://cs.gmu.edu/~eclab/projects/mason/

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Authors and Affiliations

  1. Innovation Systems Department, AIT Austrian Institute of Technology GmbH, Donau-City-Straße 1, 1220, Vienna, Austria

    Manfred Paier, Martina Dünser & Thomas Scherngell

  2. Institut für Volkswirtschaftslehre, University of Vienna, Oskar-Morgenstern-Platz 1, 1090, Wien, Austria

    Simon Martin

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  1. Manfred Paier
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  2. Martina Dünser
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  3. Thomas Scherngell
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  4. Simon Martin
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Correspondence to Manfred Paier .

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Editors and Affiliations

  1. Institute of Economics, University of Hohenheim, Stuttgart, Germany

    Ben Vermeulen

  2. Innovation Systems Department, AIT Austrian Institute of Technology GmbH, Vienna, Austria

    Manfred Paier

Appendix

Appendix

Table 5 Definition of technology classes (International Patent Classification, IPC)
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Paier, M., Dünser, M., Scherngell, T., Martin, S. (2017). Knowledge Creation and Research Policy in Science-Based Industries: An Empirical Agent-Based Model. In: Vermeulen, B., Paier, M. (eds) Innovation Networks for Regional Development. Economic Complexity and Evolution. Springer, Cham. https://doi.org/10.1007/978-3-319-43940-2_7

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