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

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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.

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Notes

  1. 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).

  2. 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).

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

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Acknowledgments

We gratefully acknowledge the financial support from the Dieter Schwarz Stiftung. In addition, we would like to thank Andreas Pyka, Robin Cowan, three anonymous reviewers, the participants of the EMAEE Conference, 1–3 June 2015, Maastricht, the Netherlands and the participants of the 1st EAEPE RA[X] Workshop, 2–3 November 2015, Essen, Germany for their helpful comments and suggestions. Needless to say, we are solely responsible for any remaining errors and omissions.

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Authors and Affiliations

  1. University of Hohenheim, Stuttgart, Germany

    Matthias Mueller, Kristina Bogner & Tobias Buchmann

  2. Stifterverband, Essen, Germany

    Muhamed Kudic

  3. University of Bremen, Bremen, Germany

    Muhamed Kudic

Authors
  1. Matthias Mueller
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  2. Kristina Bogner
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  3. Tobias Buchmann
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  4. Muhamed Kudic
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Correspondence to Matthias Mueller.

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Mueller, M., Bogner, K., Buchmann, T. et al. The effect of structural disparities on knowledge diffusion in networks: an agent-based simulation model. J Econ Interact Coord 12, 613–634 (2017). https://doi.org/10.1007/s11403-016-0178-8

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