Probabilistic Assessment of Decentralized Decision-making for Interdependent Network Restoration

This study introduces a statistical model that guides decentralized infrastructure restoration processes aligned with field practices. In particular, we make more analytically tractable the previously proposed Judgment Call method to simulate real-world decisions under time and resource constraints. The Judgment Call method explicitly models the largely ignored feature of decentralization in the restoration planning across interdependent networks. The method solves the Decentralized Interdependent Network Design Problem (D-INDP) while acknowledging the lack of proper communication among decision making agents, and hence, the lack of essential information. Here, we use a Bayesian Hierarchical Model (BHM) to simulate the agents’ practical use of their field expertise and judgments to compensate for essential information shortage. We train the model using synthetic restoration plans that emphasize the local preferences of the agents. The method is applied to the interdependent infrastructure network of Shelby County, TN, and the results show that the performance of BHM-aided restoration plans is close to the conceptual upper bound. Decisions for real-world interdependent networks are typically made by multiple distributed agents. Modeling such a decision-making environment must be decentralized and probabilistic. The former reflects the interaction of several potentially selfish agents, and the latter accounts for the uncertainties of real environments. However, most mathematical methods for decision-making in interdependent networks rely on centralized or deterministic formulations. In a previous study (Talebiyan and Duenas-Osorio, 2018), we proposed the Judgment Call method, which accounts for the decentralized nature of the realistic decision-making processes guiding the restoration of interdependent networks. Judgment Call recognizes the fact that human decision-makers use their field expertise and judgment to compensate for their bounded rationality (Sarma, 1994) as well as the lack of necessary information stemming from poor or no communication among agents. In this paper, we employ a Bayesian Hierarchical Model (BHM) to probabilistically study the intuitive decisions and utilization of expert opinion on the part of agents, and therefore, push the Judgment Call method closer to realism. As for the formulation of the network restoration problem, we adopt the Interdependent Network Design Problem (INDP), a family of centralized optimization problems concerned with the restoration of disrupted networked systems subject to budget and operational constraints, which serves as baseline for quantifying the effects of decentralization. Like the general problem of decentralized decision-making (Tsitsiklis, 1984), the lack of proper communications among agents is a crucial aspect of the new Decentralized-INDP (D-INDP) class. Judgment Call assumes that agents use their field expertise about the potential decisions of other agents to compensate for scant communications. To model such compensation, we explored several simple assumptions such as optimistic and pessimistic agents (Talebiyan and Duenas-Osorio, 2018). In this study, we replace these simple assumptions with a BHM, which aims to simulate the decision process of agents when they make judg-