Private Agent-Based Modeling
Abstract
The practical utility of agent-based models in decision-making relies on their capacity to accurately replicate populations while seamlessly integrating real-world data streams. Yet, the incorporation of such data poses significant challenges due to privacy concerns. To address this issue, we introduce a paradigm for private agent-based modeling wherein the simulation, calibration, and analysis of agent-based models can be achieved without centralizing the agents attributes or interactions. The key insight is to leverage techniques from secure multi-party computation to design protocols for decentralized computation in agent-based models. This ensures the confidentiality of the simulated agents without compromising on simulation accuracy. We showcase our protocols on a case study with an epidemiological simulation comprising over 150,000 agents. We believe this is a critical step towards deploying agent-based models to real-world applications.
Cite
@article{arxiv.2404.12983,
title = {Private Agent-Based Modeling},
author = {Ayush Chopra and Arnau Quera-Bofarull and Nurullah Giray-Kuru and Michael Wooldridge and Ramesh Raskar},
journal= {arXiv preprint arXiv:2404.12983},
year = {2024}
}
Comments
Accepted at the 23rd International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2024)