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

  • Manfred PaierEmail author
  • Martina Dünser
  • Thomas Scherngell
  • Simon Martin
Part of the Economic Complexity and Evolution book series (ECAE)


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.


Knowledge Creation Baseline Scenario Expertise Level Technology Class Patent Count 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Aiginger K, Falk R, Reinstaller A (2009) Reaching out to the future needs radical change—towards a new policy for innovation, science and technology in Austria. In: Evaluation of Government Funding in RTDI from a Systems Perspective in Austria (ed) Synthesis report, WIFO, WienGoogle Scholar
  2. Antonelli C (2011) The economic complexity of technological change: interactions, knowledge and path dependence. In: Antonelli C (ed) Handbook on the economic complexity of technological change. Edward Elgar, Cheltenham, UKCrossRefGoogle Scholar
  3. Arthur WB (2007) The structure of invention. Res Pol 36(2):274–287CrossRefGoogle Scholar
  4. Astor M, Heinrich S, Klose G, Riesenberg D (2009) Interventionslogik und Markt-/Systemversagen sowie Zusammenspiel der Institutionen und Akteure. In: Systemevaluierung der österreichischen Forschungsförderung und -finanzierung (ed) Teilbericht 9, Prognos AG, BerlinGoogle Scholar
  5. Autio E, Kanninen S, Gustafsson R (2008) First- and second-order additionality and learning outcomes in collaborative R&D programs. Res Pol 37:59–76CrossRefGoogle Scholar
  6. Axelrod R, Tesfatsion L (2006) A guide for newcomers to agent-based modelling in the social sciences. In: Judd KL, Tesfatsion L (eds) Handbook of computational economics, Vol. 2: Agent-based computational economics. North-Holland, AmsterdamGoogle Scholar
  7. Barfield C, Calfee JE (2007) Biotechnology and the patent system. Balancing innovation and property rights. AEI, Washington, DCGoogle Scholar
  8. Basberg BL (1987) Patents and the measurement of technological change: a survey of the literature. Res Pol 16:131–141CrossRefGoogle Scholar
  9. Baum JAC, Ingram P (2002) Interorganizational learning and network organizations: toward a behavioral theory of the ‘interfirm’. In: Augier M, March JG (eds) The economics of choice, change, and organization. Essays in the memory of Richard M. Cyert. Edward Elgar, Cheltenham, UKGoogle Scholar
  10. Breitfeller D, Scherngell T, Paier M (2014) The evolution of the biotechnology sector in Austria: evidence using patents over the time period 1990-2010. SSRN Working Paper Series No. 2512257, Rochester, NYGoogle Scholar
  11. Breschi S, Catalini C (2010) Tracing the links between science and technology: an exploratory analysis of scientists’ and inventors’ networks. Res Pol 39(1):14–26CrossRefGoogle Scholar
  12. Burton ML, Hicks MJ (2006) Do university based biotechnology centres impact regional biotechnology related (commercial) employment? Int J Technol Tran Commercialisation 5(4):390–400CrossRefGoogle Scholar
  13. Cameron AC, Trivedi PK (2012) Regression analysis of count data. Cambridge University Press, Cambridge, UKGoogle Scholar
  14. Cerulli G (2015) Econometric evaluation of socio-economic programs: theory and applications. Springer, HeidelbergCrossRefGoogle Scholar
  15. Christensen TA, Frosch H, Boysen-Jensen D (2014) Central innovation manual on excellent econometric evaluation of the impact of public R&D investments (CIM 2.0). Danish Ministry of Science, Innovation and Higher Education, and Danish Agency of Science, Technology and Innovation, CopenhagenGoogle Scholar
  16. Cockburn IM (2004) The changing structure of the pharmaceutical industry. Health Aff 23(1):10–22CrossRefGoogle Scholar
  17. Cunningham P, Gök A (2015) The impact of innovation policy schemes for collaboration. In: Edler J, Cunningham P, Gök A, Shapira P (eds) Handbook of innovation policy impact. Edward Elgar, LondonGoogle Scholar
  18. Dawid H (2006) Agent-based models of innovation and technological change. In: Judd KL, Tesfatsion L (eds) Handbook of computational economics, Volume 2: Agent-based computational economics. North-Holland, AmsterdamGoogle Scholar
  19. Delanghe H, Muldur U (2007) Ex-ante impact assessment of research programmes: the experience of the European Union’s 7th Framework Programme. Sci Publ Pol 34(3):169–183CrossRefGoogle Scholar
  20. European Commission (2009) Impact assessment guidelines. PART III: Annexes to SEC(2009) 92. SEC(2009) 92, European Commission, BrusselsGoogle Scholar
  21. European Commission (2013) The 2013 EU industrial R&D investment scoreboard. In: Industrial research monitoring and analysis (IRMA). European Commission – Joint Research Centre, Institute for Prospective Technological Studies, Seville, SpainGoogle Scholar
  22. Fischer MM, Fröhlich J (2001) Knowledge, complexity and innovation systems: prologue. In: Fischer MM, Fröhlich J (eds) Knowledge, complexity and innovation systems. Springer, BerlinCrossRefGoogle Scholar
  23. Freeman C (1987) Technology and economic performance: lessons from Japan. Pinter, LondonGoogle Scholar
  24. Genet C, Errabi K, Gauthier C (2012) Which model of technology transfer for nanotechnology? A comparison with biotech and microelectronics. Technovation 32(3–4):205–215CrossRefGoogle Scholar
  25. Giesecke S (2000) The contrasting roles of government in the development of biotechnology industry in the US and Germany. Res Pol 29(2):205–223CrossRefGoogle Scholar
  26. Gilbert N (1997) A simulation of the structure of academic science. Sociol Res Online 2(2).
  27. Gilbert N, Pyka A, Ahrweiler P (2001) Innovation networks—a simulation approach. J Artif Soc Soc Simulat 4(3):8Google Scholar
  28. Gittelman M (2006) National institutions, public–private knowledge flows, and innovation performance: a comparative study of the biotechnology industry in the US and France. Res Pol 35(7):1052–1068CrossRefGoogle Scholar
  29. Griliches Z (1990) Patent statistics as economic indicators: a survey. J Econ Lit 28(4):1661–1707Google Scholar
  30. Griliches Z, Mairesse J (1983) Comparing productivity growth: an exploration of French and U.S. industrial and firm data. Eur Econ Rev 21(1–2):89–119CrossRefGoogle Scholar
  31. Guellec D, van Pottelsberghe de la Potterie B (2003) The impact of public R&D expenditure on business R&D. Econ Innovat New Technol 12(3):225–243CrossRefGoogle Scholar
  32. Gulas C, Dorfmayr R, Luptacik P, Streibl W, Schneider HW, Katzmair H (2014) Wertschöpfungsökologie der Biotech in Österreich. Stärken, Schwächen und Chancen. In: Thesenpapier. Studie im Auftrag des Rat für Forschung und Technologieentwicklung. IWI; FAS Research, WienGoogle Scholar
  33. Hall LA, Bagchi-Sen S (2007) An analysis of firm-level innovation strategies in the US biotechnology industry. Technovation 27:4–14CrossRefGoogle Scholar
  34. Hoekman J, Frenken K, van Oort F (2009) The geography of collaborative knowledge production in Europe. Ann Reg Sci 43(3):721–738(18)CrossRefGoogle Scholar
  35. Hopkins MM, Martin PA, Nightingale P, Kraft A, Mahdi S (2007) The myth of the biotech revolution: an assessment of technological, clinical and organisational change. Res Pol 36:566–589CrossRefGoogle Scholar
  36. Kancs dA, Siliverstovs B (2016) R&D and non-linear productivity growth. Res Pol 45(3):634–646Google Scholar
  37. Katz JS (2006) Indicators for complex innovation systems. Res Pol 35:893–909CrossRefGoogle Scholar
  38. Khoury TA, Pleggenkuhle-Miles EG (2011) Shared inventions and the evolution of capabilities: examining the biotechnology industry. Res Pol 40(7):943–956CrossRefGoogle Scholar
  39. Klette TJ, Møen J, Griliches Z (2000) Do subsidies to commercial R&D reduce market failures? Microeconometric evaluation studies. Res Pol 29(4–5):471–495CrossRefGoogle Scholar
  40. Koput KW, Smith-Doerr L, Powell WW (1997) Strategies of learning and industry structure: the evolution of networks in biotechnology. Adv Strat Manag 14:229–254Google Scholar
  41. Korber M, Paier M (2014) Simulating the effects of public funding on research in life sciences: direct research funds vs. tax incentives. In: Gilbert N, Ahrweiler P, Pyka A (eds) Simulating the knowledge dynamics of innovation networks. Springer, BerlinGoogle Scholar
  42. Krafft J, Quatraro F (2011) The dynamics of technological knowledge: from linearity to recombination. In: Antonelli C (ed) Handbook on the economic complexity of technological change. Edward Elgar, Cheltenham, UKGoogle Scholar
  43. Linton K, Stone P, Wise J (2008) Patenting trends and innovation in industrial biotechnology. In: Staff Research Study 31. U.S. International Trade Commission, Office of Industries, Washington, DCGoogle Scholar
  44. Long JS, Freese J (2001) Regression models for categorical dependent variables using STATA. Stata, College Station, TXGoogle Scholar
  45. Lundvall B-Å, Borrás S (2004) Science, technology and innovation policy. In: Fagerberg J, Mowery DC, Nelson RR (eds) The Oxford handbook of innovation. Oxford University Press, OxfordGoogle Scholar
  46. Magerman T, Van Looy B, Debackere K (2015) Does involvement in patenting jeopardize one’s academic footprint? An analysis of patent-paper pairs in biotechnology. Res Pol 44(9):1702–1713CrossRefGoogle Scholar
  47. McKelvey M, Alm H, Riccaboni M (2003) Does co-location matter for formal knowledge collaboration in the Swedish biotechnology–pharmaceutical sector? Res Pol 32(3):483–501CrossRefGoogle Scholar
  48. McMillan GS, Narin F, Deeds DL (2000) An analysis of the critical role of public science in innovation: the case of biotechnology. Res Pol 29:1–8CrossRefGoogle Scholar
  49. Mohnen P, Lokshin B (2009) What does it take for an R&D tax incentive policy to be effective? CIRANO Scientific Series, Montreal.
  50. Nelson RR (1993) National innovation systems: a comparative study. Oxford University Press, New YorkGoogle Scholar
  51. Nelson AJ (2009) Measuring knowledge spillovers: what patents, licenses and publications reveal about innovation diffusion. Res Pol 38(6):994–1005CrossRefGoogle Scholar
  52. Niosi J (2000) Science-based industries: a new Schumpeterian taxonomy. Technol Soc 22(4):429–444CrossRefGoogle Scholar
  53. OECD (2008) OECD patent databases: identifying technology areas for patents. OECD, Paris.
  54. OECD (2009) The bioeconomy to 2030: designing a policy agenda. Main findings and policy conclusions. OECD International Futures Project, OECD, ParisGoogle Scholar
  55. OECD (2015) Statistical definition of biotechnology. Last accessed 22 Jan 2016
  56. Orbis (2014) ORBIS company database. Bureau van DijkGoogle Scholar
  57. Owen-Smith J, Powell WW (2004) Knowledge networks as channels and conduits: the effects of spillovers in the Boston Biotechnology Community. Organ Sci 15(1):5–21CrossRefGoogle Scholar
  58. Powell WW, Snellman K (2004) The knowledge economy. Annu Rev Sociol 30:199–220CrossRefGoogle Scholar
  59. Powell WW, Koput KW, Smith-Doerr L (1996) Interorganizational collaboration and the locus of innovation: networks of learning in biotechnology. Admin Sci Q 41:116–145CrossRefGoogle Scholar
  60. Reiner C, Smoliner S (2012) Outputorientierte Evaluierung öffentlich geförderter FTI-Programme—Möglichkeiten und Grenzen. In: Studie im Auftrag des Bundesministeriums für Verkehr, Innovation und Technologie. Gesellschaft zur Förderung der Forschung (GFF), Joanneum Research (JR) und Zentrum für Soziale Innovation (ZSI), WienGoogle Scholar
  61. Reinold F, Paier M, Fischer MM (2013) Joint knowledge production in European R&D networks: results from a discrete choice modeling perspective. In: Scherngell T (ed) The geography of networks and R&D collaborations. Springer, BerlinGoogle Scholar
  62. Rip A, Courtial P (1984) Co-word maps of biotechnology: an example of cognitive scientometrics. Scientometrics 6:381–400CrossRefGoogle Scholar
  63. Romer PM (1990) Endogenous technological change. J Polit Econ 98, Part 1(5):S71–S102CrossRefGoogle Scholar
  64. Roper S, Hewitt-Dundas N (2015) Knowledge stocks, knowledge flows and innovation: evidence from matched patents and innovation panel data. Res Pol 44(7):1327–1340CrossRefGoogle Scholar
  65. Santos FM (2003) The coevolution of firms and their knowledge environment: insights from the pharmaceutical industry. Technol Forecast Soc Change 70(7):687–715CrossRefGoogle Scholar
  66. Saviotti PP, Catherine D (2008) Innovation networks in biotechnology. In: Patzelt H, Brenner T (eds) Handbook of bioentrepreneurship. Springer, New YorkGoogle Scholar
  67. Schibany A, Berger M, Buchinger E, Dachs B, Dinges M, Ecker B, Falk M, Gassler H, Heller-Schuh B, Hofer R, Huber P, Janger J, Reinstaller A, Streicher G, Unterlass F (2010) Österreichischer Forschungs- und Technologiebericht 2010. In: Lagebericht gem. § 8 (1) FOG über die aus Bundesmitteln geförderte Forschung, Technologie und Innovation in Österreich. BMWF, BMVIT, BMWFJ, WienGoogle Scholar
  68. Shapiro C (2001) Navigating the patent thicket: cross licenses, patent pools, and standard-setting. In: Jaffe AB, Lerner J, Stern S (eds) Innovation policy and the economy. National Bureau of Economic Research, Cambridge, MAGoogle Scholar
  69. Siebers PO, Macal CM, Garnett J, Buxton D, Pidd M (2010) Discrete-event simulation is dead, long live agent-based simulation! J Simulat 4(3):204–210CrossRefGoogle Scholar
  70. Smajgl A, Barreteau O (2014) Designing empirical agent-based models: an issue of matching data, technical requirements and stakeholders expectations. In: Smajgl A, Barreteau O (eds) Empirical agent-based modelling—challenges and solutions. Volume 1: The characterisation and parameterisation of empirical agent-based models. Springer, New YorkCrossRefGoogle Scholar
  71. Stuart TE, Ozdemir SZ, Ding WW (2007) Vertical alliance networks: the case of university-biotechnology-pharmaceutical alliance chains. Res Pol 36:477–498CrossRefGoogle Scholar
  72. Thiele JC, Kurth W, Grimm V (2014) Facilitating parameter estimation and sensitivity analysis of agent-based models: a cookbook using NetLogo and ‘R’. J Artif Soc Soc Simulat 17(3):11Google Scholar
  73. Tödtling F, Trippl M (2007) Knowledge links in high-technology industries: markets, networks, or milieu? The case of the Vienna Biotechnology Cluster. Int J Entrepren Innovat Manag 7(2–5):345–365Google Scholar
  74. Trippl M, von Gabain J, Tödtling F (2006) Policy agents as catalysts of knowledge links in the biotechnology sector. SRE-Discussion 2006/01, Institute for Multilevel Governance and Development, Department of Socioeconomics, Vienna University of Economics and BusinessGoogle Scholar
  75. Triulzi G, Pyka A, Scholz R (2014) R&D and knowledge dynamics in university-industry relationships in biotech and pharmaceuticals: an agent-based model. Int J Biotechnol 13(1–3):137–179CrossRefGoogle Scholar
  76. Verhoeven D, Bakker J, Veugelers R (2016) Measuring technological novelty with patent-based indicators. Res Pol 45(3):707–723CrossRefGoogle Scholar
  77. Wajsman N, Thumm N, Kazimierczak M, Lazaridis G, Arias Burgos C, Domanico F, García Valero F, Boedt G, Garanasvili A, Mihailescu A (2013) Intellectual property rights intensive industries: contribution to economic performance and employment in the European Union. In: Industry-level analysis report. European Patent Office and Office for Harmonization in the Internal Market, Munich, Germany, and Alicante, SpainGoogle Scholar
  78. Wieczorek AJ, Hekkert MP (2012) Systemic instruments for systemic innovation problems: a framework for policy makers and innovation scholars. Sci Publ Pol 39:74–87CrossRefGoogle Scholar
  79. Wirth M (2013) Der Campus Vienna Biocenter. Entstehung, Entwicklung und Bedeutung für den Life Sciences-Standort Wien. Studienverlag, InnsbruckGoogle Scholar
  80. Zhang J, Baden-Fuller C, Mangematin V (2007) Technological knowledge base, R&D organization structure and alliance formation: evidence from the biopharmaceutical industry. Res Pol 36:515–528CrossRefGoogle Scholar
  81. Zucker L, Darby M, Brewer MB (1998) Intellectual human capital and the birth of U.S. biotechnology enterprises. Am Econ Rev 88(1):290–306Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Manfred Paier
    • 1
    Email author
  • Martina Dünser
    • 1
  • Thomas Scherngell
    • 1
  • Simon Martin
    • 2
  1. 1.Innovation Systems DepartmentAIT Austrian Institute of Technology GmbHViennaAustria
  2. 2.Institut für VolkswirtschaftslehreUniversity of ViennaWienAustria

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