Abstract
Simulation is a useful tool for evaluating behavioral interventions when the adoption rate among a population is uncertain. Individual agent models are often prohibitively expensive, but, unlike stochastic models, allow studying compliance heterogeneity. In this paper we 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 the utility of our approach by studying the effectiveness of interventions used in Virginia in early 2020 through counterfactual simulations.
| Original language | English |
|---|---|
| Number of pages | 8 |
| Journal | CEUR Workshop Proceedings |
| Volume | 3182 |
| Publication status | Published - 2022 |
| Event | 1st Workshop on Agent-Based Modeling and Policy-Making, AMPM 2021 - Virtual, Vulnius, Lithuania Duration: 8 Dec 2021 → 8 Dec 2021 |
Bibliographical note
Funding Information: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 US Census block group level. PB and SS were supported in part by NSF Expeditions in Computing Grant CCF-1918656.
Publisher Copyright:
© 2021 Copyright for this paper by its authors.
Keywords
- Agent-based Computational Epidemiology
- Agent-based Modeling
- Belief-Desire-Intention
- Complex Social Simulation
- Multi-agent Simulation
- Normative Reasoning
- Policy Evaluation
- Synthetic Population