Abstract:
Critical infrastructures systems are governed by several sectors working together to maintain social,
economic, and environmental well-being. Their cyber-physical interdependencies, on the other hand,
influence their performance and resilience to routine failures and extreme events. To balance investment
and restoration decisions before, during, and after disruptive events, different mathematical formulations
and solutions, mainly focused on centralized view, were presented in the literature. While necessary and
useful, 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. We first model each infrastructure as a weighted graph with relevant geospatial attributes. Decision makers (e.g., maintenance crew) for each infrastructure sector are represented by intelligent agents. We then define three information and coordination structures among agents, including no communication, leader-follower, and decentralized coalitions. The framework is applied to the interdependent water distribution and road networks in the City of Tampa, FL.We simulate different magnitudes of cyberphysical failures, evaluate resource allocation decisions, made by agents under each coordination structure, and quantify the aggregated resilience. Specifically, we develop a rank aggregation performance measure to evaluate restoration effectiveness for each scenario. This research helps municipalities to quantify the impact of their collective decision making and identify the best coordination structures when interdependencies are modeled in infrastructure systems.
