Machine-assisted agent-based modeling: Opening the black box

Firouzeh Taghikhah*, Alexey Voinov, Tatiana Filatova, J. Gareth Polhill

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)
34 Downloads (Pure)


While agent-based modeling (ABM) has become one of the most powerful tools in quantitative social sciences, it remains difficult to explain their structure and performance. We propose to use artificial intelligence both to build the models from data, and to improve the way we communicate models to stakeholders. Although machine learning is actively employed for pre-processing data, here for the first time, we used it to facilitate model development of a simulation model directly from data. Our suggested framework, ML-ABM accounts for causality and feedback loops in a complex nonlinear system and at the same time keeps it transparent for stakeholders. As a result, beside the development of a behavioral ABM, we open the ‘blackbox’ of purely empirical models. With our approach, artificial intelligence in the simulation field can open a new stream in modeling practices and provide insights for future applications.

Original languageEnglish
Article number101854
JournalJournal of computational science
Publication statusPublished - Oct 2022


  • Behavioral analytics
  • Conceptual modeling
  • Interpretable artificial intelligence
  • Social communications
  • Systems thinking
  • 2023 OA procedure
  • UT-Hybrid-D

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