Complexities of Agent-Based Model Output Analysis

Jay Lee, T Filatova, B Hassani-Mahmooei, A Ligmann-Zielinska, F Stonedahl, I Lorscheid, A Voinov, Z Sun, D Parker

Research output: Contribution to journalArticleAcademicpeer-review


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 analysing 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
JournalJasss. The Journal of Artificial Societies and Social Simulation
Issue number4
Publication statusPublished - 2015
Externally publishedYes


Dive into the research topics of 'Complexities of Agent-Based Model Output Analysis'. Together they form a unique fingerprint.

Cite this