Abstract
Modelling social phenomena in large-scale agent-based simulations has long been a challenge due to the computational cost of incorporating agents whose behaviors are determined by reasoning about their internal attitudes and external factors. However, COVID-19 has brought the urgency of doing this to the fore, as, in the absence of viable pharmaceutical interventions, the progression of the pandemic has primarily been driven by behaviors and behavioral interventions. In this paper, we address this problem by developing a large-scale data-driven agent-based simulation model where individual agents reason about their beliefs, objectives, trust in government, and the norms imposed by the government. These internal and external attitudes are based on actual data concerning daily activities of individuals, their political orientation, and norms being enforced in the US state of Virginia. Our model is calibrated using mobility and COVID-19 case data. We show the utility of our model by quantifying the benefits of the various behavioral interventions through counterfactual runs of our calibrated simulation.
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Notes
- 1.
Our choice of the factors influencing the agents’ decisions, as well as of the norms mentioned above, should be considered as a ‘proof of concept’ to illustrate our framework. In more realistic simulations, elicitation of the most relevant factors in a well-designed study would be paramount. This is left for future work.
- 2.
Due to space limitations, we refer to the code repository for the specific details of the factors: https://bitbucket.org/goldenagents/sim2apl-episimpledemics.
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Acknowledgments
We thank Cuebiq; mobility data is provided by Cuebiq, a location intelligence and measurement platform. Through its Data for Good program, Cuebiq provides access to aggregated mobility data for academic research and humanitarian initiatives. This first-party data is collected from anonymized users who have opted-in to provide access to their location data anonymously, through a GDPR and CCPA compliant framework. To further preserve privacy, portions of the data are aggregated to the census-block group level.
PB and SS were supported in part by NSF Expeditions in Computing Grant CCF-1918656 and DTRA subcontract/ARA S-D00189-15-TO-01-UVA.
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de Mooij, J., Dell’Anna, D., Bhattacharya, P., Dastani, M., Logan, B., Swarup, S. (2022). Quantifying the Effects of Norms on COVID-19 Cases Using an Agent-Based Simulation. In: Van Dam, K.H., Verstaevel, N. (eds) Multi-Agent-Based Simulation XXII. MABS 2021. Lecture Notes in Computer Science(), vol 13128. Springer, Cham. https://doi.org/10.1007/978-3-030-94548-0_8
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