Top

Journal of Economic Interaction and Coordination

Published in:

30-08-2016 | Regular Article

The effect of structural disparities on knowledge diffusion in networks: an agent-based simulation model

Authors: Matthias Mueller, Kristina Bogner, Tobias Buchmann, Muhamed Kudic

Published in: Journal of Economic Interaction and Coordination | Issue 3/2017

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

We apply an agent-based simulation approach to explore how and why typical network characteristics affect overall knowledge diffusion properties. To accomplish this task, we employ an agent-based simulation approach (ABM) which is based on a “barter trade” knowledge diffusion process. Our findings indicate that the overall degree distribution significantly affects a network’s knowledge diffusion performance. Nodes with a below-average number of links prove to be one of the bottlenecks for an efficient transmission of knowledge throughout the analysed networks. This indicates that diffusion-inhibiting overall network structures are the result of the myopic linking strategies of the actors at the micro level. Finally, we implement policy experiments in our simulation environment in order to analyse consequences of selected policy interventions. This complements previous research knowledge on diffusion processes in innovation networks.

previous article The effect of communication channels on promise-making and promise-keeping: experimental evidence

next article On the coevolution of social norms in primitive societies

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Notably, in the model absorptive capacities are similar for all firms and exogeneously given. Hence, they can be considered as an industry level parameter rather than an agent-level parameter. An alternative approach has been conseptualized and applied by Savin and Egbetokun (2016).

At this point it would have been possible to decide in favor of other algorithms and the resulting network topologies as for example core periphery structures (Borgatti and Everett 1999; Cattani and Ferriani 2008; Kudic et al. 2015).

The exact rewiring procedure works as follows: The starting point is a ring lattice with n nodes and k links. In a second step, each link is then rewired randomly with the probability p. By altering the parameter p between \(p=0\) and \(p=1\), i.e. the network can be transformed from regularity to disorder.

In this paper we analyse diffusion processes in existing networks. In the case of the EV algorithm we assume that the linking process is repeated 100 times. To create comparable networks with a pre-defined number of links we further assume that links are deleted after 2 time steps of the rewiring process.

See also Fig. 2: The point in time the knowledge stock in the network has reached its steady state \(\bar{v}^{*}\) is \(t{^{*}=61}\) for Watts–Strogatz networks, \(t{^{*}=55}\) for Erdös–Rényi networks, \(t{^{*}=45}\) for Barabási–Albert networks and \(t{^{*}=32}\) for networks created with the Evolutionary network algorithm.

To determine why a node stops trading we define a variable for each node which contains the information on whether its unsuccessful trades failed because the respective node had insufficient knowledge or whether its trading partner actually had insufficient knowledge. The colour marking indicates the average results over a simulation run of 100 time steps.

In the policy intervention, we define ‘stars’ as those 10 % of all nodes that have the highest degree centrality, whereas ‘small’ is defined as those 10 % of the distribution that have the lowest degree centrality. ‘Medium’ agents are those 80 % of the distribution that are neither ‘stars’ nor ’small’. To measure the performance of the policy interventions we measure the steady-state knowledge stock \(\bar{v}\) for every policy after 100 simulation steps and over 500 simulation runs.

Ahuja G (2000) Collaboration networks, structural hole, and innovation: a longitudinal study. Admin Sci Q 45(3):425–455CrossRef

Baum JA, Calabrese T, Silverman BS (2000) Don’t go it alone: alliance network composition and startup’s performance in Canadian biotechnology. Strateg Manag J 21(3):267–294CrossRef

Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512CrossRef

Bollobás B, Riordan O, Spencer J, Tusnády G (2001) The degree sequence of a scale-free random graph process. Random Struct Algorithms 18(3):279–290CrossRef

Borgatti SP, Everett MG (1999) Models of core/periphery structures. Soc Netw 21(4):375–395CrossRef

Burt R (1992) Structural holes: the social structure of competition. Harvard, Cambridge

Cattani G, Ferriani S (2008) A core/periphery perspective on individual creative performance: social networks and cinematic achievements in the hollywood film industry. Organ Sci 19(6):824–844CrossRef

Coleman JS (1988) Social capital in the creation of human capital. Am J Sociol 94:S95–S120

Cowan R, Jonard N (2007) Structural holes, innovation and the distribution of ideas. J Econ Interact Coord 2(2):93–110CrossRef

Cowan R, Jonard N (2004) Network structure and the diffusion of knowledge. J Econ Dyn Control 28(8):1557–1575CrossRef

Erdös P, Rényi A (1959) On random graphs. Publ Math Debr 6:290–297

Erdös P, Rényi A (1960) On the evolution of random graphs. Publ Math Inst Hung Acad Sci 5:17–61

Fleming L, King C, Juda AI (2007) Small worlds and regional innovation. Organ Sci 18(6):938–954CrossRef

Grant RM, Baden-Fuller C (2004) A knowledge accessing theory of strategic alliances. J Manag Stud 41(1):61–84CrossRef

Gilsing V, Nooteboom B, Vanhaverbeke W, Duysters G, van den Oord A (2008) Network embeddedness and the exploration of novel technologies: technological distance, betweenness centrality and density. Res Policy 37(10):1717–1731CrossRef

Hanusch H, Pyka A (2007) Principles of neo-Schumpeterian economics. Camb J Econ 31(2):275–289CrossRef

Hanson JR, Krackhardt D (1993) Informal networks: the company behind the chart. Harv Bus Rev 71(4):104–111

Herstad SJ, Sandven T, Solberg E (2013) Location, education and enterprise growth. Appl Econ Lett 20(10):1019–1022CrossRef

Kim H, Park Y (2009) Structural effects of R&D collaboration network on knowledge diffusion performance. Expert Syst Appl 36(5):8986–8992CrossRef

Kudic M, Ehrenfeld W, Pusch T (2015) On the trail of core-periphery patterns in innovation networks—measurement and new empirical findings from the German laser industry. Ann Reg Sci 55(1):187–220CrossRef

Levén P, Holmstroem J, Mathiassen L (2014) Managing research and innovation networks: evidence from a government sponsored cross-industry program. Res Policy 43(1):156–168CrossRef

Lin M, Li N (2010) Scale-free network provides an optimal pattern for knowledge transfer. Phys A: Stat Mech Appl 389(3):473–480CrossRef

Malerba F (1992) Learning by firms and incremental technical change. Econ J 102(413):845–859CrossRef

Malerba F (2007) Innovation and the dynamics and evolution of industries: progress and challenges. Int J Ind Organ 25(4):675–699CrossRef

Morone P, Taylor R (2010) Knowledge diffusion and innovation: modelling complex entrepreneurial behaviours. Edward Elgar Publishing, CheltenhamCrossRef

Morone A, Morone P, Taylor R (2007) A laboratory experiment of knowledge diffusion dynamics. In: Canter U, Malerba F (eds) Innovation, industrial dynamics and structural transformation. Springer, Heidelberg, pp 283–302CrossRef

Morone P, Taylor R (2004) Knowledge diffusion dynamics and network properties of face-to-face interactions. J Evol Econ 14(3):327–351CrossRef

Mueller M, Buchmann T, Kudic M (2014) Micro strategies and macro patterns in the evolution of innovation networks-an agent-based simulation approach in simulating knowledge dynamics. In: Gilbert N, Ahrweiler P, Pyka A (eds) Innovation networks. Springer, Heidelberg, pp 73–95

Nelson RR, Winter SG (1982) Belknap Press of Harvard University Press

OECD (1996) The knowledge-based economy. General distribution OCDE/GD 96(102)

Podolny JM (2001) Networks as the pipes and prisms of the market. Am J Sociol 7(1):33–60CrossRef

Powell WW, Koput KW, Smith-Doerr L (1996) Interorganizational collaboration and the locus of innovation: networks of learning in biotechnology. Admin Sci Q 41(1):116–145CrossRef

Powell WW, White DR, Koput KW, Owen-Smith J (2005) Network dynamics and field evolution: the growth of interorganizational collaboration in the life sciences. Am J Sociol 110(4):1132–1205CrossRef

Powell WW, Grodal S (2005) Networks of innovators. In: Fagerberg J, Mowery DC, Nelson RR (eds) The Oxford handbook of innovation. Oxford University Press, Oxford, New York, pp 56–85

Pyka A (1997) Informal networking. Technovation 17(4):207–220CrossRef

Savin I, Egbetokun A (2016) Emergence of innovation networks from R&D cooperation with endogenous absorptive capacity. J Econ Dyn Control 64:82–103CrossRef

Schilling MA, Phelps CC (2007) Interfirm collaboration networks: the impact of large-scale network structure on firm innovation. Manag Sci 53(7):1113–1126CrossRef

Stuart TE (2000) Interorganizational alliances and the performance of firms: a study of growth and innovational rates in a high-technology industry. Strateg Manag J 21(8):791–811CrossRef

Uzzi B, Amaral LA, Reed-Tsochas F (2007) Small-world networks and management science research: a review. Eur Manag Rev 4(2):77–91CrossRef

Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442CrossRef

Title: The effect of structural disparities on knowledge diffusion in networks: an agent-based simulation model
Authors: Matthias Mueller
Kristina Bogner
Tobias Buchmann
Muhamed Kudic
Publication date: 30-08-2016
Publisher: Springer Berlin Heidelberg
Published in: Journal of Economic Interaction and Coordination / Issue 3/2017
Print ISSN: 1860-711X
Electronic ISSN: 1860-7128
DOI: https://doi.org/10.1007/s11403-016-0178-8

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 3/2017

Complexity and model comparison in agent based modeling of financial markets

Monetary policy and dark corners in a stylized agent-based model

On the coevolution of social norms in primitive societies

Heterogeneity of expectations and financial crises: a stochastic dynamic approach

The effect of communication channels on promise-making and promise-keeping: experimental evidence

Voting for the distribution rule in a Public Good Game with heterogeneous endowments