An Agent-Based Model for Simulating Flood Governance and Community Resilience

Area of Science:

• Environmental Science
• Social Science
• Computer Science

Background:

• Agent-based modeling (ABM) offers unique insights into social mechanisms and emergent phenomena.
• Flood governance networks play a crucial role in community loss-sharing and post-flood recovery.

Purpose of the Study:

• To develop an empirically grounded agent-based model simulating flood governance networks.
• To evaluate the impact of network structures, trust, and resilience measures on community flood resilience.
• To understand how community resilience emerges from micro-level interactions.

Main Methods:

• Designed and calibrated an agent-based model using empirical data from Guangzhou, China.
• Modeled diverse agents including government, NGOs, private sector, and local clans.
• Integrated core processes with modules for trust evolution and resource constraints.

Main Results:

• The model simulates dynamic, network-based collaborative processes among diverse stakeholders.
• It provides a framework to measure community robustness and adaptivity for flood resilience evaluation.
• Sensitivity analysis validated key parameters derived from literature and empirical research.

Conclusions:

• The agent-based model serves as an accessible tool for researchers and practitioners.
• It aids in understanding stakeholder collaborations in climate governance.
• Identifies optimal intervention strategies for enhancing community flood resilience.