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Contribution Details

Type Working Paper
Scope Discipline-based scholarship
Title Can System Dynamics Learn from Social Network Analysis?
Organization Unit
Authors
  • Lukas Schönenberger
  • Andrea Schenker-Wicki
Language
  • English
Institution University of Zurich
Series Name UZH Business Working Paper Series
Number 349
ISSN 2296-0422
Number of Pages 26
Date 2015
Abstract Text This article deals with the analysis of large or complex system dynamics (SD) models, exploring the benefits of a multimethodological approach to model analysis. We compare model analysis results from SD and social network analysis (SNA) by deploying SNA techniques on a pertinent example from the SD literature—the world dynamics model. Although SNA is a clearly distinct method from SD in that it focuses on social actors and their interrelationships, we contend that SD can indeed learn from SNA, particularly in terms of model structure analysis. Our argumentation follows renowned system dynamicists who acknowledge the potential of SD to synthesize and advance theories in social science at both the conceptual and technical levels.
Official URL https://www.business.uzh.ch/de/research/wps.html
Digital Object Identifier 10.2139/ssrn.2550593
Other Identification Number merlin-id:18194
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Keywords Social network analysis, centrality, mixing methods, model structure analysis, large models