Private Agent-Based Modeling

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 agentbased 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.

Authors

• Ayush Chopra Massachusetts Institute of Technology
• Arnau Quera-Bofarull University of Oxford
• Nurullah Giray-Kuru Massachusetts Institute of Technology
• Michael Wooldridge University of Oxford
• Ramesh Raskar Massachusetts Institute of Technology

Keywords

• Differentiable Agent-based Modeling
• Privacy
• Multi-party Computation
• Automatic Differentiation
• Epidemiology