Complex Systems Simulation Using Agent-Based Models (ABM) for ...

Complex Systems Simulation Using Agent-Based Models (ABM) for
Business Networks
Chairs
Frans Prenkert, BI Norwegian Business School
Ian Wilkinson, University of Sydney Business School
Louise Young, University of Western Sydney School of Marketing
Complex systems simulation modeling using agent-based models (ABM) is a new research
tools for the analysis of dynamic, emergent and complex phenomena in social science. This
tool enables researchers to model both the individual behaviors of agents at micro level and
the emergent macro-level patterns of the system. The advantage and power of ABM is that
researchers do not need to know the macro-pattern dynamics – instead the researcher
specifies the micro-rules that controls individual interactions and then measures the macrolevel
results that emerge through the simulation of the specified micro-interactions.
Powered with the computing power of ordinary desktop computers, this approach gives us
the opportunity to gain knowledge and understanding of emergent phenomena previously
beyond our reach. Research in the IMP tradition have acknowledged the complex nature of
interaction and business networks as the emergent result of many interacting agents for
which ABM is a suitable tool to be used for investigations. While some early attempts exist
at using ABM to model business networks and interaction accordingly, this approach is still in
its infancy and in much need of further development and refinement. This special track aims
at such discussions.
References
Anderson, P. W., Arrow, K. J., & Pines, D. (Eds.). (1988). The Economy As An Evolving Complex System (Vol. V).
Oxford: Westview Press.
Arthur, W. B. (1999). Complexity and the Economy. Science(284), 107-109.
Arthur, W. B., Durlauf, S., & Lane, D. (Eds.). (1997). The Economy as an Evolving Complex System II. Reading,
Ma: Addison-Wesley.
Epstein, J. M., & Axtell, R. (1996). Growing artificial societies: Social science from the bottom up. Cambridge,
MA.: MIT Press.
Følgesvold, A., & Prenkert, F. (2009). Magic Pelagic - An agent-based simulation of twenty years of emergent
value accumulation in the North Atlantic herring exchange system. Industrial Marketing Management,
38(5), 529-540.
Lee, S. M., & Pritchett, A. R. (2008). Predicting Interactions Between Agents in Agent-Based Modeling and
Simulation of Sociotechnical Systems. IEEE Transactions on Systems, Man & Cybernetics: Part A, 38(6),
1210-1220. doi: 10.1109/tsmca.2008.2001059

Olaru, D., Denize, S., & Purchase, S. L. (2009). Alternative ways of verification and validation of computational
models: A case of replication in the innovation networks. Paper presented at the The 25th annual IMPconference,
Marseille, France.
Purchase, S. L., Olaru, D., & Denize, S. (2008). Exploring innovation networks: two simulations, two perspectives
and the mechanisms that drive innovation performance. Paper presented at the the 24th annual IMPconference,
Uppsala, Sweden.
Wilkinson, I., Held, F., Marks, R., & Young, L. (2011). Developing Agent-Based Models of Business Relations
and Networks Networks in Society: Links and Language. Singapore: Pan Stanford.
Wilkinson, I., & Young, L. (2003). A view from the edge. Marketing Theory, 3(1), 179.
Wilkinson, I. F. (2008). Business Relating Business: Managing Organisational Relations and Networks.
Cheltenham: Edvard Elgar.
For more information contact the track chairs:
Frans Prenkert frans.prenkert@bi.no
Ian Wilkinson
Louise Young
ian.wilkinson@sydney.edu.au
louise.young@uws.edu.au