Abstract
This study aims at identifying the effects of agents’ specialization in research fields on their research performance by means of an agent-based model of the Vienna life sciences, which builds upon the SKIN model. Specialization of agents, e.g. research organizations, firms or universities, is found to play a crucial role in the innovative performance of an industry or a research area. Also in the policy arena, specialization of regions and sectors attained renascent importance through the concept of smart specialization. In order to contribute to the crucial discussion whether specialization or rather diversification is more likely to promote innovative activities, we run simulation scenarios with varying degrees of specialization. Findings provide evidence for both aspects; whereas a higher degree of specialization is found to be favourable for the creation of patent applications and high-tech jobs, diversification is found to be favourable for the creation of scientific publications.
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- 1.
The abbreviation ABM will be used equivalently for ‘agent-based modeling’ and ‘agent-based models’.
- 2.
Life sciences include biotechnology, pharmaceuticals, health services, and medical devices and equipment (OECD 2009, p. 96). Biotechnology is defined as “the application of science and technology to living organisms, as well as parts, products and models thereof, to alter living or non-living materials for the production of knowledge, goods and services.” (OECD 2005, p. 9).
- 3.
‘Dedicated’ refers to the predominant activity of the firm (LISAvienna 2011, p. 151).
- 4.
‘Related’ refers to providing activities, e.g. providing technical products, biotechnology/medical device-specific services (LISAvienna 2011, p. 151).
- 5.
The Jaccard-index (Leydesdorff 2008, p. 79) is defined as \( {J}_{r_1{r}_2}=\frac{X_{r_1{r}_2}}{X_{r_1}+{X}_{r_2}-{X}_{r_1{r}_2}} \), where r 1 and r 2 denote research fields with \( {r}_1,{r}_2\in R=\left\{{r}_m\left|m=1,\dots, M\right.\right\} \), \( {X}_{r_1{r}_2} \) denotes the number of co-occurrences of research fields in organizations and \( {X}_{r_1} \) and \( {X}_{r_2} \) denote the occurrence of research field r 1and r 2, respectively.
References
Ahrweiler P, Pyka A, Gilbert N (2004) Simulating knowledge dynamics in innovation networks. In: Leombruni R, Richiardi M (eds) Industry and labor dynamics: the agent-based computational economics approach. World Scientific, Singapore, pp 284–296
Arrow K (1962) Economic welfare and the allocation of resources for invention. The rate and direction of inventive activity: economic and social factors. Princeton University Press, Princeton, NJ, pp 609–626
Austrian Life Science Directory (2012) Austrian Life Science Directory. URL: http://www.lifesciencesdirectory.at/. Accessed 10 Jan 2012
Beaudry C, Schiffauerova A (2009) Who’s right, Marshall or Jacobs? The localization versus urbanization debate. Res Pol 38:318–337
Biocom AG (2011) Life sciences in Österreich 2012. Biocom AG, Berlin
Edquist C (1997) Systems of innovation approaches—their emergence and characteristics. In: Edquist C (ed) Systems of innovation. Technologies, institutions and organizations. Pinter, London, pp 1–35
European Commission (2002) Life sciences and biotechnology—a strategy for Europe. COM (2002) 27, Luxemburg
European Commission (2013) Smart specialization platform. URL: http://s3platform.jrc.ec.europa.eu. Accessed 16 Sept 2013
Farhauer O, Kröll A (2013) Indizes räumlicher Konzentration und regionaler Spezialisierung. In: Farhauer O, Kröll A (eds) Stadorttheorien. Regional- und Standortökonomik in Theorie und Praxis. Springer Gabler, Wiesbaden, pp 299–369
Feldman MP, Audretsch DB (1999) Innovation in cities: science-based diversity, specialization and localized competition. Eur Econ Rev 43(2):409–429
Fischer MM, Fröhlich J (2001) Knowledge, complexity and innovation systems: prologue. In: Fischer MM, Fröhlich J (eds) Knowledge, complexity and innovation systems. Springer, Berlin, pp 1–17
Fritsch M, Slavtchev V (2008) How does industry specialization affect the efficiency of regional innovation systems? Jena Economic Research Papers # 2008-058
Gilbert N (1997) A simulation of the structure of academic science. Sociol Res Online 2(2). URL: http://www.socresonline.org.uk/socresonline/2/2/3.html. Accessed 12 Aug 2013
Gilbert N, Pyka A, Ahrweiler P (2001) Innovation networks—a simulation approach. J Artif Soc Soc Simulat 4(3). URL: http://jasss.soc.surrey.ac.uk/4/3/8.html. Accessed 12 Aug 2013
Glaeser EL, Kallal HD, Scheinkman JA, Shleifer A (1992) Growth in cities. J Polit Econ 100(6):1126–1152
Greunz L (2004) Industrial structure and innovation—evidence from European regions. J Evol Econ 14(5):563–592
Heller-Schuh B, Paier M (2009) Regional—National—Europäisch: Wiener F&E-Netzwerke aus der Mehr-Ebenen-Perspektive. In: Leitner K-H, Weber M, Fröhlich J (eds) Innovationsforschung und Technologiepolitik in Österreich: Neue Perspektiven und Gestaltungsmöglichkeiten. Studienverlag, Innsbruck, pp 154–179
Jacobs J (1969) The economy of cities. Random House, New York
Korber M (2012) Agent-based modeling of complexity in life sciences—with a special emphasis on the impact of public funding on research activities [=Doctoral work]. Book series of the Innovation Economics Vienna—Knowledge and Talent Development Programme PhD- & Master-Theses 16, AIT Austrian Institute of Technology and Vienna University of Economics and Business
Korber M, Paier M (2011) Exploring the effects of public research funding on biotech innovation: an agent-based simulation approach. In: Sayama H, Minai AA, Braha D, Bar-Yam Y (eds) Proceedings of the 8th international conference on complex systems “unifying themes in complex systems”, vol 8, New England Complex Systems Institute Series on Complexity, 26 June–1 July 2011. NECSI Knowledge Press, Boston, MA, pp 599–613
Leydesdorff L (2008) On the normalization and visualization of author co-citation data: Salton’s cosine versus the Jaccard index. J Am Soc Inform Sci Technol 59(1):77–85
LISAvienna (2011) Vienna life science report: sector survey: facts and directory 2011/2012. Life Science Austria, Biocom AG, Vienna
Macal CM, North MJ (2010) Tutorial on agent-based modeling and simulation. J Simulat 4(3):151–162
Marshall A (1890) Principles of economics. Macmillan, London
OECD (2005) A framework for biotechnology statistics. Organisation for Economic Co-operation and Development, Paris
OECD (2009) Biotechnology statistics 2009. Organisation for Economic Co-operation and Development, Paris
Paci R, Usai S (1999) Externalities, knowledge spillovers and the spatial distribution of innovation. GeoJournal 49(4):381–390
Paci R, Usai S (2000) The role of specialization and diversity externalities in the agglomeration of innovative activities. Rivista Italiana degli Economisti 2:237–268
Parunak HV, Savit R, Riolo R (1998) Agent-based modeling vs. equation-based modeling: a case study and users’ guide. In: Sichman JS, Conte R, Gilbert N (eds) Multi-agent systems and agent-based simulation (MABS ‘98). Springer, Berlin, pp 10–25
Pyka A, Grebel T (2006) Agent-based modelling—a methodology for the analysis of qualitative development processes. In: Billari FC, Fent T, Prskawetz A, Scheffran J (eds) Agent-based computational modelling—applications in demography, social, economic and environmental sciences. Physica, Springer, Heidelberg, pp 17–35
Pyka A, Gilbert N, Ahrweiler P (2002) Simulating innovation networks. In: Pyka A, Küppers G (eds) Innovation networks: theory and practice. Edward Elgar, Cheltenham, pp 169–196
Romer P (1986) Increasing returns and long-run growth. J Polit Econ 94:1002–1037
van der Panne G (2004) Agglomeration externalities: Marshall versus Jacobs. J Evol Econ 14(5):593–604
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Dünser, M., Korber, M. (2017). Regional Specialization and Knowledge Output: An Agent-Based Simulation of the Vienna Life Sciences. 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_10
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