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UML for ABM

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Abstract

Although the majority of researchers interested in ABM increasingly agree that the most natural way to program their models is to adopt OO practices, UML diagrams are still largely absent from their publications. In the last 15 years, the use of UML has risen constantly, to the point where UML has become the de facto standard for graphical visualization of software development. UML and its 13 diagrams has many universally accepted virtues. Most importantly, UML provides a level of abstraction higher than that offered by OO programming languages (Java, C++, Python, .Net ...). This abstraction layer encourages researchers to spend more time on modeling rather than on programming. This paper initially presents the four most common UML diagrams - class, sequence, state and activity diagrams (based on my personal experience, these are the most useful diagrams for ABM development). The most important features of these diagrams are discussed, and explanations based on conceptual pieces often found in ABM models are given of how best to use the diagrams. Subsequently, some very well known and classical ABM models such as the Schelling segregation model, the spatial evolutionary game, and a continuous double action free market are subjected to more detailed UML analysis.

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  • Hugues Bersini, 2012. "UML for ABM," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(1), pages 1-9.
    • Handle: RePEc:jas:jasssj:2011-105-1
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    2. Ngo-Hoang, Dai-Long, 2019. "A research paper of Hossein Sabzian (2019), Theories and Practice of Agent based Modeling: Some practical Implications for Economic Planners, ArXiv, 54p," AgriXiv xutyz, Center for Open Science.
    3. Hossein Sabzian & Mohammad Ali Shafia & Ali Maleki & Seyeed Mostapha Seyeed Hashemi & Ali Baghaei & Hossein Gharib, 2019. "Theories and Practice of Agent based Modeling: Some practical Implications for Economic Planners," Papers 1901.08932, arXiv.org.
    4. Luca Barone, 2013. "An ABM for Economics: Micro Explains Macro," Working papers 016, Department of Economics, Social Studies, Applied Mathematics and Statistics (Dipartimento di Scienze Economico-Sociali e Matematico-Statistiche), University of Torino.
    5. Jonas Hauke & Iris Lorscheid & Matthias Meyer, 2017. "Recent Development of Social Simulation as Reflected in JASSS Between 2008 and 2014: A Citation and Co-Citation Analysis," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(1), pages 1-5.
    6. Juan Miguel Rodriguez-Lopez & Meike Schickhoff & Shubhankar Sengupta & Jürgen Scheffran, 2021. "Technological and social networks of a pastoralist artificial society: agent-based modeling of mobility patterns," Journal of Computational Social Science, Springer, vol. 4(2), pages 681-707, November.
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    8. Scheller, Fabian & Johanning, Simon & Bruckner, Thomas, 2019. "A review of designing empirically grounded agent-based models of innovation diffusion: Development process, conceptual foundation and research agenda," Contributions of the Institute for Infrastructure and Resources Management 01/2019, University of Leipzig, Institute for Infrastructure and Resources Management.

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