Abstract
We focus on the evolution of interfirm innovation networks which are composed of and driven by heterogeneous actors that follow a number of well-defined cooperation partner selection strategies. For this purpose, we simulate micro level firm behaviour which shapes the macro level network patterns. In order to analyse the micro-macro link we build an agent-based simulation model (ABSM) which allows us to test causal relationships between firm strategies and the emerging network structures. With this model we analyse the structural consequences of homophily, reputation and cohesion mechanisms in a situation of information scarcity. We start with a simple model which is in a next step extended by adding a market mechanism which links the knowledge base of a firm with the reward a firm receives and with the incentives to cooperate. We show that (a) a transitive closure mechanism combined with a tendency for preferential attachment produces networks that show both, small world characteristics as well as a power law degree distribution; (b) diversity in the selection of cooperation partners is an important determinant of innovative performance if we consider an evolving network.
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Notes
- 1.
Small world networks are characterized by a small average shortest path length and a relatively high level of clustering (Watts and Strogatz 1998).
- 2.
According to this scheme, the first character specifies the number of mutual dyads, the second character gives the number of asymmetric dyads, the third character displays the number of null-dyads, and the last character gives a further characterization of how the ties are directed to each other within these specific isomorphism classes by using the characters “D” (for down), “U” (for up), “T” (for transitive) and “C” (for cyclic). For further details, see Wasserman and Faust (1994).
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Müller, M., Buchmann, T., Kudic, M. (2014). Micro Strategies and Macro Patterns in the Evolution of Innovation Networks: An Agent-Based Simulation Approach. In: Gilbert, N., Ahrweiler, P., Pyka, A. (eds) Simulating Knowledge Dynamics in Innovation Networks. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43508-3_4
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