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Resilience of urban communities hit by extreme events relies on the prompt access to financial resources needed for recovery. Therefore, the functioning of physical infrastructures is strongly related to that of the financial system, where agents operate in the markets of insurance contracts. When the financial capacity of an agent is lower than the requests for funds from the communities, it defaults and fails at providing these requests, slowing down the recovery process. In this work, we investigate how the resilience of urban communities depends on the reliability of the financial agents operating in the insurance markets, and how to optimize the mechanism adopted by these agents to share the requests for funds from the policyholders. We present results for a set of loss functions that reflect the costs borne by society due to the default of the financial agents. 

The occurrence of extreme events, either natural or man-made, puts stress on both the physical infrastructure, causing damages and failures, and the financial system. The following recovery process requires a large amount of resources from financial agents, such as insurance companies. If the demand for funds overpasses their capacity, these financial agents cannot fulfill their obligations, thus defaulting, without being able to deliver the requested funds. However, agents can share risk among each other, according to specific agreements. Our goal is to investigate the relationship between these agreements and the overall response of the physical/financial systems to extreme events and to identify the optimal set of agreements, according to some risk-based metrics. We model the system as a directed and weighted graph, where nodes represent financial agents and links agreements among these. Each node faces an external demand of funds coming from the physical assets, modeled as a random variable, that can be transferred to other nodes, via the directed edges. For a given probabilistic model of demands and structure of the graph, we evaluate metrics such as the expected number of defaults, and we identify the graph configuration which optimizes the metric. The identified graph suggests to the agents a set of agreements to minimize global risk.