@inproceedings{DBLP:conf/ampm/MooijDBDLS21,
  author    = {Jan de Mooij and
               Davide Dell'Anna and
               Parantapa Bhattacharya and
               Mehdi Dastani and
               Brian Logan and
               Samarth Swarup},
  title     = {Using Agent-Based Simulation to Investigate Behavioral Interventions in a Pandemic},
  booktitle = {Proceedings of the 1st Workshop on Agent-based Modelling and Policy-Making, {AMPM@JURIX} 2021},
  year      = {2021},
  url_Paper = {http://ceur-ws.org/Vol-3182/paper7.pdf},
   url_Slides       = {https://github.com/ampmresearch/ampmresearch.github.io/blob/main/presentations/7_JdeMooij.pdf},
  keywords  = {COVID-19, synthetic population, Sim-2APL, BDI, multiagent systems, agent-based simulation, norms, executive orders, behavior, Large-Scale Agent-Based Simulation},
  abstract = {Simulation is a useful tool for evaluating behavioral interventions when adoption rate among the population may be uncertain. Individual agent models are often prohibitively expensive, but unlike stochastic models allow studying compliance heterogeneity.
In this paper we aim to demonstrate the feasibility of evaluating behavioral intervention policies using large-scale data-driven agent-based simulations. We explain how the simulation is calibrated with respect to real-world data, and demonstrate its utility by studying the effectiveness of interventions used in Virginia in early 2020 through counterfactual simulations.}
}

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