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The Complexities of Agent-Based Modeling Output Analysis

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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.

Suggested Citation

  • 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, 2015. "The Complexities of Agent-Based Modeling Output Analysis," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(4), pages 1-4.
  • Handle: RePEc:jas:jasssj:2015-55-2
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    File URL: http://jasss.soc.surrey.ac.uk/18/4/4/4.pdf
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    1. Riccardo Boero & Giangiacomo Bravo & Marco Castellani & Flaminio Squazzoni, 2010. "Why Bother with What Others Tell You? An Experimental Data-Driven Agent-Based Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(3), pages 1-6.
    2. Simon Angus & Behrooz Hassani-Mahmooei, 2015. ""Anarchy" Reigns: A Quantitative Analysis of Agent-Based Modelling Publication Practices in JASSS, 2001-2012," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(4), pages 1-16.
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    Cited by:

    1. Lamperti, Francesco & Roventini, Andrea & Sani, Amir, 2018. "Agent-based model calibration using machine learning surrogates," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 366-389.
    2. repec:spr:bioerq:v:2:y:2017:i:3:d:10.1007_s41247-017-0026-z is not listed on IDEAS
    3. Ernesto Carrella & Richard M. Bailey & Jens Koed Madsen, 2018. "Indirect inference through prediction," Papers 1807.01579, arXiv.org.
    4. Arosa, M.L. & Bastos, R. & Cabral, J.A. & Freitas, H. & Costa, S.R. & Santos, M., 2017. "Long-term sustainability of cork oak agro-forests in the Iberian Peninsula: A model-based approach aimed at supporting the best management options for the montado conservation," Ecological Modelling, Elsevier, vol. 343(C), pages 68-79.
    5. repec:eee:ejores:v:269:y:2018:i:3:p:794-805 is not listed on IDEAS
    6. repec:eee:agisys:v:164:y:2018:i:c:p:95-106 is not listed on IDEAS
    7. repec:eee:ecomod:v:369:y:2018:i:c:p:13-41 is not listed on IDEAS
    8. repec:gam:jlands:v:7:y:2018:i:2:p:47-:d:140720 is not listed on IDEAS
    9. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    10. repec:eee:enepol:v:118:y:2018:i:c:p:325-345 is not listed on IDEAS
    11. Thomas, Spencer A. & Lloyd, David J.B. & Skeldon, Anne C., 2016. "Equation-free analysis of agent-based models and systematic parameter determination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 464(C), pages 27-53.

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