Decentralized Decision Making for the Restoration of Interdependent Networks

Abstract

This study introduces tractable algorithms to model the decentralized decision-making process for postdisaster restoration of interdependent networks. Restoration strategies are devised by a host of agents, who control different layers of interdependent networks. Because of interdependencies, each agent’s decisions are impacted by other agents’ decisions. However, agents communicate poorly in real-world settings—particularly after contingencies—and therefore, they cannot access all the information that is necessary in order to make a decision; instead, they compensate with their expert judgment. We propose the notion of Judgment Call to model this practical human contrivance. We explore several types of judgment, including pure random judgment, optimistic judgment, and judgments guided by importance measures. The performance of the method is compared to optimal solutions applied to a database of ideal networks that contains different configurations of random, scale-free, and grid networks. In addition, we apply the method to the interdependent infrastructure network of Shelby County, Tennessee, as a realistic case study. The results show that the method performs best when there is no randomness in the decentralized judgment process, which highlights the need to improve communication, monitoring, and modeling of distributed systems in order to reduce uncertainties. Moreover, the results show that interdependency density and resource availability influence the performance of the Judgment Call method the most. These insights are key for decision support in the practice of postdisaster recovery and community resilience.