A graph-empowered agent-based simulation: Impacts of coordination schemes on critical infrastructures resilience

Abstract

Critical infrastructure systems are governed by several sectors working together to maintain social, economic, and environmental well-being. Their cyber–physical interdependencies influence their performance and resilience to routine failures and extreme events. To balance investment and restoration decisions in the face of disruptive events, mostly centralized mathematical formulations and solutions were presented in the literature. However, not all physical and dynamic characteristics of infrastructure systems and their decision makers can be modeled via mathematical models. In this study, we take a different approach and utilize agent-based modeling to simulate city-scale interdependent infrastructure networks as a complex adaptive system. In specific, we design a flexible modular (object-oriented) simulation tool capable of capturing the dynamic behaviors of various networks. We first model each infrastructure as a weighted graph with relevant geospatial attributes. Decision makers for each infrastructure sector are represented by intelligent agents. We then define three information and coordination schemes among agents, including no communication, leader–follower, and decentralized coalitions. To show the applicability of the approach, we use publicly available interdependent water distribution and road networks for the City of Tampa, FL, which is prone to hurricanes. We simulate different magnitudes of physical, cyber, and cyber–physical failures, evaluate resource allocation decisions, made by agents under each coordination scheme, and quantify the aggregated resilience. The simulation platform will help municipalities in various cities to quantify the impact of their collective decision making and identify the best coordination structures when interdependencies are modeled in infrastructure systems.