Quantifying the Effects of Norms on COVID-19 Cases Using an Agent-Based Simulation
22nd International Workshop on Multi-Agent-Based Simulation, MABS 2021
; 13128 LNAI:99-112, 2022.
Article
in English
| Scopus | ID: covidwho-1680637
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. © 2022, Springer Nature Switzerland AG.
Computational epidemiology; Large-scale social simulation; Norm reasoning agents; Computers; Agent based simulation; Behavioral interventions; Computational costs; Computational epidemiologies; External factors; Large-scale agent-based simulations; Large-scales; Norm reasoning agent; Social simulations; Artificial intelligence
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
Language:
English
Journal:
22nd International Workshop on Multi-Agent-Based Simulation, MABS 2021
Year:
2022
Document Type:
Article
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