Detecting Key Variables in System Dynamics Modelling by Using Social Network Metrics View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

J. Barranquero , M. Chica , O. Cordón , S. Damas

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. More... »

PAGES

207-217

References to SciGraph publications

  • 2012-11. On the validation of system dynamics type simulation models in TELECOMMUNICATION SYSTEMS
  • 2009-05. The Use of System Dynamics Simulation in Water Resources Management in WATER RESOURCES MANAGEMENT
  • 2001-06. Dynamic strategic thinking in JOURNAL OF THE ACADEMY OF MARKETING SCIENCE
  • 1975-09. Fuzzy logic and approximate reasoning in SYNTHESE
  • 1998-06. Collective dynamics of ‘small-world’ networks in NATURE
  • 1988-12. The biocybernetic approach as a basis for planning our environment in SYSTEMIC PRACTICE AND ACTION RESEARCH
  • Book

    TITLE

    Advances in Artificial Economics

    ISBN

    978-3-319-09577-6
    978-3-319-09578-3

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-09578-3_17

    DOI

    http://dx.doi.org/10.1007/978-3-319-09578-3_17

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1038369821


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