@inproceedings{DBLP:conf/mabs/MooijDBDLS21,
  author    = {Jan de Mooij and
               Davide Dell'Anna and
               Parantapa Bhattacharya and
               Mehdi Dastani and
               Brian Logan and
               Samarth Swarup},
  title     = {Quantifying the Effects of Norms on {COVID-19} Cases Using an Agent-Based
               Simulation},
  booktitle = {Proceedings of the 22nd International Workshop on Multi-Agent Systems and Agent-Based Simulation, {MABS@AAMAS} 2021},
  volume    = {13128},
  pages     = {99--112},
  year      = {2021},
  url_Link = {https://doi.org/10.1007/978-3-030-94548-0_8},
  url_Paper      = {2021_MABS/MABS21_deMooijDBDLS.pdf},
  url_Supplement       = {https://bitbucket.org/goldenagents/sim2apl-episimpledemics/src/master/},
  doi       = {10.1007/978-3-030-94548-0\_8},
  timestamp = {Wed, 19 Jan 2022 09:36:09 +0100},
  biburl    = {https://dblp.org/rec/conf/mabs/MooijDBDLS21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  keywords  = {COVID-19, synthetic population, Sim-2APL, BDI, multiagent systems, agent-based simulation, norms, executive orders, behavior, Large-Scale Agent-Based Simulation},
  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 and validated 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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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 and validated 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.","bibtex":"@inproceedings{DBLP:conf/mabs/MooijDBDLS21,\n author = {Jan de Mooij and\n Davide Dell'Anna and\n Parantapa Bhattacharya and\n Mehdi Dastani and\n Brian Logan and\n Samarth Swarup},\n title = {Quantifying the Effects of Norms on {COVID-19} Cases Using an Agent-Based\n Simulation},\n booktitle = {Proceedings of the 22nd International Workshop on Multi-Agent Systems and Agent-Based Simulation, {MABS@AAMAS} 2021},\n volume = {13128},\n pages = {99--112},\n year = {2021},\n url_Link = {https://doi.org/10.1007/978-3-030-94548-0_8},\n url_Paper = {2021_MABS/MABS21_deMooijDBDLS.pdf},\n url_Supplement = {https://bitbucket.org/goldenagents/sim2apl-episimpledemics/src/master/},\n doi = {10.1007/978-3-030-94548-0\\_8},\n timestamp = {Wed, 19 Jan 2022 09:36:09 +0100},\n biburl = {https://dblp.org/rec/conf/mabs/MooijDBDLS21.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org},\n keywords = {COVID-19, synthetic population, Sim-2APL, BDI, multiagent systems, agent-based simulation, norms, executive orders, behavior, Large-Scale Agent-Based Simulation},\n 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 and validated using mobility and COVID-19 case data. 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