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The Value of Information (VoI) assesses the impact of data in a decision process. A risk-neutral agent, quantifying the VoI in monetary terms, prefers to collect data only if their VoI surpasses the cost to collect them. For an agent acting without external constraints, data have non-negative VoI (as free “information cannot hurt”) and those with an almost-negligible potential effect on the agent's belief have an almost-negligible VoI. However, these intuitive properties do not hold true for an agent acting under external constraints related to epistemic quantities, such as those posed by some regulations. For example, a manager forced to repair an asset when its probability of failure is too high can prefer to avoid collecting free information about the actual condition of the asset, and even to pay in order to avoid this, or she can assign a high VoI to almost-irrelevant data. Hence, by enforcing epistemic constraints in the regulations, the policy-maker can induce a range of counter-intuitive, but rational, behaviors, from information avoidance to over-evaluation of barely relevant information, in the agents obeying the regulations. This paper illustrates how the structural properties of VoI change depending on such external epistemic constraints, and discusses how incentives and penalties can alleviate these induced attitudes toward information. 

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. 

We investigate how sequential decision making analysis can be used for modeling system resilience. In the aftermath of an extreme event, agents involved in the emergency management aim at an optimal recovery process, trading off the loss due to lack of system functionality with the investment needed for a fast recovery. This process can be formulated as a sequential decision-making optimization problem, where the overall loss has to be minimized by adopting an appropriate policy, and dynamic programming applied to Markov Decision Processes (MDPs) provides a rational and computationally feasible framework for a quantitative analysis. The paper investigates how trends of post-event loss and recovery can be understood in light of the sequential decision making framework. Specifically, it is well known that system’s functionality is often taken to a level different from that before the event: this can be the result of budget constraints and/or economic opportunity, and the framework has the potential of integrating these considerations. But we focus on the specific case of an agent learning something new about the process, and reacting by updating the target functionality level of the system. We illustrate how this can happen in a simplified setting, by using Hidden-Model MPDs (HM-MDPs) for modelling the management of a set of components under model uncertainty. When an extreme event occurs, the agent updates the hazard model and, consequently, her response and long-term planning. 

The value of information (VoI) provides a rational metric to assess the impact of data in decision processes, including maintenance of engineering systems. According to the principle that “information never hurts”, VoI is guaranteed to be non-negative when a single agent aims at minimizing an expected cost. However, in other contexts such as non-cooperative games, where agents compete against each other, revealing a piece of information to all agents may have a negative impact to some of them, as the negative effect of the competitors being informed and adjusting their policies surpasses the direct VoI. Being aware of this, some agents prefer to avoid having certain information collected, when it must be shared with others, as the overall VoI is negative for them. A similar result may occur for managers of infrastructure assets following the prescriptions of codes and regulations. Modern codes require the probability of some failure events be below a threshold, so managers are forced to retrofit assets if that probability is too high. If the economic incentive of those agents disagrees with the code requirements, the VoI associated with tests or inspections may be negative. In this paper, we investigate under what circumstance this happens, and how severe the effects of this issue can be.