A Framework for Modeling Human Behavior in Large-scale Agent-based Epidemic Simulations. de Mooij, J., Bhattacharya, P., Dell'Anna, D., Dastani, M., Logan, B., & Swarup, S. Simulation, 99(12):1183–1211, 2023. ![A Framework for Modeling Human Behavior in Large-scale Agent-based Epidemic Simulations [link]](https://bibbase.org/img/filetypes/link.svg "link")
Link Paper Video Supplement doi abstract bibtex Agent-based modeling is increasingly being used in computational epidemiology to characterize important behavioral dimensions, such as the heterogeneity of the individual responses to interventions, when studying the spread of a disease. Existing agent-based simulation frameworks and platforms currently fall in one of two categories: those that can simulate millions of individuals with simple behaviors (e.g., based on simple state machines), and those that consider more complex and social behaviors (e.g., agents that act according to their own agenda and preferences, and deliberate about norm compliance) but, due to the computational complexity of reasoning involved, have limited scalability. In this paper, we present a novel framework that enables large-scale distributed epidemic simulations with complex behaving social agents whose decisions are based on a variety of concepts and internal attitudes such as sense, knowledge, preferences, norms, and plans. The proposed framework supports simulations with millions of such agents that can individually deliberate about their own knowledge, goals and preferences, and can adapt their behavior based on other agents’ behaviors and on their attitude towards complying with norms. We showcase the applicability and scalability of the proposed framework by developing a model of the spread of COVID-19 in the US state of Virginia. Results illustrate that the framework can be effectively employed to simulate disease spreading with millions of complex behaving agents and investigate behavioral interventions over a period of time of months.
@article{DBLP:journals/simulation/MooijBDDLS23,
author = {Jan de Mooij and
Parantapa Bhattacharya and
Davide Dell'Anna and
Mehdi Dastani and
Brian Logan and
Samarth Swarup},
title = {A Framework for Modeling Human Behavior in Large-scale Agent-based Epidemic Simulations},
journal = {Simulation},
volume = {99},
number = {12},
pages = {1183--1211},
year = {2023},
url_Link = {https://doi.org/10.1177/00375497231184898},
url_Paper = {https://journals.sagepub.com/doi/epdf/10.1177/00375497231184898},
url_Video = {https://www.youtube.com/watch?v=MI63SK1FpKg},
url_Supplement = {https://github.com/A-Practical-Agent-Programming-Language/Sim-2APL},
doi = {10.1177/00375497231184898},
doi = {10.1177/00375497231184898},
timestamp = {Wed, 20 Dec 2023 09:58:01 +0100},
biburl = {https://dblp.org/rec/journals/simulation/MooijBDDLS23.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
keywords = {Agent-based modeling, Social simulation, Synthetic population, Computational epidemiology, COVID-19, PanSim, Sim-2APL, Large-Scale Agent-Based Simulation},
abstract = {Agent-based modeling is increasingly being used in computational epidemiology to characterize important behavioral
dimensions, such as the heterogeneity of the individual responses to interventions, when studying the spread of a
disease. Existing agent-based simulation frameworks and platforms currently fall in one of two categories: those that
can simulate millions of individuals with simple behaviors (e.g., based on simple state machines), and those that
consider more complex and social behaviors (e.g., agents that act according to their own agenda and preferences,
and deliberate about norm compliance) but, due to the computational complexity of reasoning involved, have limited
scalability. In this paper, we present a novel framework that enables large-scale distributed epidemic simulations with
complex behaving social agents whose decisions are based on a variety of concepts and internal attitudes such as
sense, knowledge, preferences, norms, and plans. The proposed framework supports simulations with millions of such
agents that can individually deliberate about their own knowledge, goals and preferences, and can adapt their behavior
based on other agents’ behaviors and on their attitude towards complying with norms. We showcase the applicability
and scalability of the proposed framework by developing a model of the spread of COVID-19 in the US state of Virginia.
Results illustrate that the framework can be effectively employed to simulate disease spreading with millions of complex
behaving agents and investigate behavioral interventions over a period of time of months.}
}
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