Adaptive algorithm for dependent infrastructure network restoration in an imperfect information sharing environment

Abstract

Critical infrastructure networks are vital for a functioning society and their failure can have widespread consequences. Decision-making for critical infrastructure resilience can suffer based on several characteristics exhibited by these networks, including (i) that there exist interdependencies with other networks, (ii) that several decision-makers represent potentially competing interests among the interdependent networks, and (iii) that information about other decision-makers’ actions are uncertain and potentially unknown. To address these concerns, we propose an adaptive algorithm using machine learning to integrate predictions about other decision-makers’ behavior into an interdependent network restoration planning problem considering an imperfect information sharing environment. We examined our algorithm against the optimal solution for various types, sizes, and dependencies of networks, resulting in insignificant differences. To assess the proposed algorithm’s efficiency, we compared its results with a proposed heuristic method that prioritizes, and schedules components restoration based on centrality-based importance measures. The proposed algorithm provides a solution sufficiently close to the optimal solution showing the algorithm performs well in situations where the information sharing environment is incomplete.

Game‐theoretic algorithm for interdependent infrastructure network restoration in a decentralized environment

“…Furthermore, as the algorithm had to calculate all possible strategy combinations and find the Nash equilibrium using the backward induction, the proposed algorithm had to enumerate all possibilities, which makes the algorithm highly expensive regarding time. Rangrazjeddi et al (2022) developed an innovative adaptive algorithm using a notion of dictatorial game theory. This algorithm uses machine learning to predict the leader's behavior and updates input parameters to generate the best possible response for restoring an interdependent infrastructure network.…”

“…Rangrazjeddi et al. (2022) developed an innovative adaptive algorithm using a notion of dictatorial game theory. This algorithm uses machine learning to predict the leader's behavior and updates input parameters to generate the best possible response for restoring an interdependent infrastructure network.…”

“…Also, as information is gradually updated based on the decision-makers' turn, the one decision-maker who acts last can make a better decision as it has complete information about the actions taken by all other decisionmakers. Rangrazjeddi et al (2022) presented an adaptive algorithm based on dictatorial game theory. The system anticipates the leader's behavior using machine learning, updating the optimization input parameters, and preparing the optimal response for recovering an interdependent infrastructure network in which each decision-maker is solely accountable for and rewarded for its associated subnetwork.…”

“…Rangrazjeddi et al. (2022) presented an adaptive algorithm based on dictatorial game theory. The system anticipates the leader's behavior using machine learning, updating the optimization input parameters, and preparing the optimal response for recovering an interdependent infrastructure network in which each decision‐maker is solely accountable for and rewarded for its associated subnetwork.…”