Abstract
A review of the complex networks theory reveals a variety of important insights into customer-centric valuation in software markets. These allow one the formulation of the research hypotheses concerning the properties, dynamics and topologies of customer networks in software markets. In this chapter, a numerical complex networks adoption and diffusion simulator is developed for a two-fold purpose. First, the simulator is designed, as stated, in order to investigate the hypotheses in the following complex networks analysis of customer networks. The second, more general motivation is to provide a guideline for the design of a simulator that can be applied in order to investigate complex customer networks of real world software companies. Therefore, it is integrated in a later chapter of the book into the previously developed network effects framework. The result is a complex networks framework for valuation in software markets based on the complex networks adoption and diffusion simulator. For both reasons, the purpose of this chapter is to provide an overview of the design and implementation process as well as on the main features of the simulator.1
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Notes
- 1.
Please note that this chapter only summarizes the most relevant mechanics of the program which are required in order to understand the subsequent analyzes.
- 2.
- 3.
Object-orientation is a software engineering approach that models a system as a group of interacting objects (Booch 2007). In such a system each object represents some relevant entity that is described by its class, its state, and its behavior. Object-oriented models can be created to show the static structure, dynamic behavior, and run-time deployment of these collaborating objects.
- 4.
Please confer also section 10.3.
- 5.
Please confer section 7.2.2.2.
- 6.
Further empirical research is required in order to study the distribution of benefits for different software products. This is in particular necessary with respect to the perceived derivative benefits of network effects. But such investigations are beyond the defined scope of this book. Please confer 18.1 for a discussion of this issue.
- 7.
Please confer the discussion on program extensions in the next subsection.
- 8.
Many thanks to Prof. Dr. Matthias Krause and Dirk Stegemann for their support.
- 9.
For further information please confer the project website: http://jung.sourceforge.net/index.html
- 10.
For further information please confer the project website: http://commons.apache.org/collections/apidocs/index.html
- 11.
For further information please confer the project website: http://dsd.lbl.gov/∼hoschek/colt/index.html
- 12.
For further information please confer the project website: http://xerces.apache.org/xerces-j/
- 13.
Motivation: If i is linked to j, then j is also linked to i, but i may benefit from a confirmed contact to actor j more than j benefits from his contact to i, e.g., i=Joe Sixpack and j=Barack Obama.
- 14.
Possible extensions are time-dependent benefits u ij D(t) and costs c ij D(t).
- 15.
Please note that E[a(j,t)]∈[0,1], e.g., by forcing to one if greater than one and forcing to zero if less than zero.
- 16.
Under the given computational restrictions, the simulator allowed one to simulate a random network of 300,000 nodes with an average connectivity of 5. A network with an average connectivity of 50, in turn, is limited to a network with 50,000 nodes.
- 17.
The computational power of the fastest computers increased by a factor of thousand during the last 10years (Heise 2008).
- 18.
Please note that today highly specific scientific projects have already access to high performance computers and high performance computing fnetworks, e.g. JUGENE in Jlich (Heise 2008).
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Kemper, A. (2010). Complex Networks Adoption and Diffusion Simulator. In: Valuation of Network Effects in Software Markets. Contributions to Management Science. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2367-7_11
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