Using Agent-Based Simulation to Investigate Behavioral Interventions in a Pandemic. de Mooij, J., Dell'Anna, D., Bhattacharya, P., Dastani, M., Logan, B., & Swarup, S. In Proceedings of the 1st Workshop on Agent-based Modelling and Policy-Making, AMPM@JURIX 2021, 2021. Paper Slides abstract bibtex 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.
@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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