The Complexities of Agent-Based Modeling Output Analysis

By Ju-Sung Lee, Tatiana Filatova, Arika Ligmann-Zielinska, Behrooz Hassani-Mahmooei, F. Stonedahl, Iris Lorscheid, Alexey A. Voinov, J. Gareth Polhill, Zhanli Sun, Dawn Cassandra Parker. Journal of Artificial Societies and Social Simulation, 2015

310 citations recorded by the source corpus.

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.

Subfields: Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/b0820eea609357d52901d88e9a4b0f04d67ea4ef

Citations in the corpus

• The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, unknown): “While the majority of SA applications have been to assess parametric variations in agent-based models (Lee et al., 2015), an open research question is how to include alternative agent-based elements in a comprehensive SA, so that one can assess sensitivity of the response to changes in the rule of…”