The complexities of agent-based modeling output analysis

Journal article (2015)

Authors

Ju Sung Lee University of Twente
T. Filatova University of Twente
Arika Ligmann-Zielinska Michigan State University
Behrooz Hassani-Mahmooei Monash University
Forrest Stonedahl Augustana College
Iris Lorscheid Hamburg University of Technology
A. Voinov University of Twente
J. Gareth Polhill The James Hutton Institute
Zhanli Sun Leibniz Institute of Agricultural Development in Transition Economies (IAMO)
Dawn C. Parker University of Waterloo
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Published Date

2015

Language

English

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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.