The complexities of agent-based modeling output analysis

Ju-Sung Lee, Tatiana Filatova, Arika Ligmann-Zielinska, Behrooz Hassani Mahmooei, Forrest Stonedahl, Iris Lorscheid, Alexey Voinov, J Gary Polhill, Zhanli Sun, Dawn C Parker

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105 Citations (Scopus)

Abstract

The proliferation of agent-based models (ABMs) in recent decades has motivated model practitioners to improve the transparency, replicability, and trust in results derived from ABMs. The complexity of ABMs has risen in stride with advances in computing power and resources, resulting in larger models with complex interactions and learning and whose outputs are often high-dimensional and require sophisticated analytical approaches. Similarly, the increasing use of data and dynamics in ABMs has further enhanced the complexity of their outputs. In this article, we offer an overview of the state-of-the-art approaches in analyzing and reporting ABM outputs highlighting challenges and outstanding issues. In particular, we examine issues surrounding variance stability (in connection with determination of appropriate number of runs and hypothesis testing), sensitivity analysis, spatio-temporal analysis, visualization, and effective communication of all these to non-technical audiences, such as various stakeholders.
Original languageEnglish
Pages (from-to)1 - 27
Number of pages27
JournalJournal of Artificial Societies and Social Simulation
Volume18
Issue number4
DOIs
Publication statusPublished - 2015

Cite this

Lee, J-S., Filatova, T., Ligmann-Zielinska, A., Hassani Mahmooei, B., Stonedahl, F., Lorscheid, I., Voinov, A., Polhill, J. G., Sun, Z., & Parker, D. C. (2015). The complexities of agent-based modeling output analysis. Journal of Artificial Societies and Social Simulation, 18(4), 1 - 27. https://doi.org/10.18564/jasss.2897