How can multi-agent simulation improve city risk mitigation?
Towards resilient cities: A hybrid simulation framework for risk mitigation through data-driven decision making
January 10, 2025
https://arxiv.org/pdf/2501.04746This paper proposes a hybrid simulation framework for improving city resilience against risks like pandemics and cyberattacks. It models a city as interconnected critical infrastructures (energy, water, healthcare, ICT, transport, etc.) driven by a social system operating within an urban landscape.
Key points for LLM-based multi-agent systems:
- Hybrid agent-based and network-based modeling: Agents represent city entities (hospitals, citizens, etc.) with sub-agents interacting within different infrastructure networks (healthcare, ICT, etc.). This allows modeling both inter- and intra-system dependencies. LLMs can enhance the agents' decision-making and communication capabilities.
- Layered metrics: Performance metrics are collected at entity, system, and city levels, allowing granular and holistic views of the city's operation. LLMs can aid in interpreting these metrics and generating explanations for decision-makers.
- Simulation federation: The framework supports integrating existing simulators (e.g., traffic simulators). LLMs can facilitate communication and data exchange between federated simulators.
- Discrete simulation in accelerated time: Enables simulating long-term scenarios and exploring multiple "what-if" scenarios. LLMs can be used for scenario generation and analysis.
- Object-oriented design: Promotes code reusability and simplifies model development. LLM-based agents can be easily integrated into object-oriented frameworks.
- Focus on decision-making: The framework aims to support risk-informed decision-making by providing quantitative insights. LLMs can augment decision-making by providing natural language explanations and recommendations.