Abstract
System dynamics provides the means for modelling complex systems such as those required to analyse many economic and marketing phenomena. When tackling highly complex problems, modellers can soundly increase their understanding of these systems by automatically identifying the key variables that arise from the model structure. In this work we propose the application of social network analysis metrics, like degree, closeness or centrality, to quantify the relevance of each variable. These metrics shall assist modellers in identifying the most significant variables of the system. We apply our proposed key variable detection algorithm to a brand management problem modelled via system dynamics. Simulation results show how changes in these variables have an noteworthy impact over the whole system.
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Acknowledgements
This work has been supported by RØD Brand Consultants under ZIO project and Ministerio de EconomÃa y Competitividad under SOCOVIFI2 (TIN2012-38525-C02-01 and TIN2012-38525-C02-02) projects.
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Barranquero, J., Chica, M., Cordón, O., Damas, S. (2015). Detecting Key Variables in System Dynamics Modelling by Using Social Network Metrics. In: Amblard, F., Miguel, F., Blanchet, A., Gaudou, B. (eds) Advances in Artificial Economics. Lecture Notes in Economics and Mathematical Systems, vol 676. Springer, Cham. https://doi.org/10.1007/978-3-319-09578-3_17
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DOI: https://doi.org/10.1007/978-3-319-09578-3_17
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