Decentralized Agent-Based Modeling
Abstract
The utility of agent-based models for practical decision making depends upon their ability to recreate populations with great detail and integrate real-world data streams. However, incorporating this data can be challenging due to privacy concerns. We alleviate this issue by introducing a paradigm for secure agent-based modeling. In particular, we leverage secure multi-party computation to enable decentralized agent-based simulation, calibration, and analysis. We believe this is a critical step towards making agent-based models scalable to the real-world application.
Cite
Text
Chopra et al. "Decentralized Agent-Based Modeling." NeurIPS 2023 Workshops: MASEC, 2023.Markdown
[Chopra et al. "Decentralized Agent-Based Modeling." NeurIPS 2023 Workshops: MASEC, 2023.](https://mlanthology.org/neuripsw/2023/chopra2023neuripsw-decentralized/)BibTeX
@inproceedings{chopra2023neuripsw-decentralized,
title = {{Decentralized Agent-Based Modeling}},
author = {Chopra, Ayush and Quera-Bofarull, Arnau and Kuru, Nurullah Giray and Raskar, Ramesh},
booktitle = {NeurIPS 2023 Workshops: MASEC},
year = {2023},
url = {https://mlanthology.org/neuripsw/2023/chopra2023neuripsw-decentralized/}
}