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1. Negative value of information in systems’ maintenance

   Pozzi, M; Malings, C; Minca, AC (August 2017, Safety, Reliability, Risk, Resilience and Sustainability of Structures and Infrastructure 12th Int. Conf. on Structural Safety and Reliability, Vienna, Austria, 6–10 August 2017)

   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. 

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2. Information avoidance and overvaluation under epistemic constraints: Principles and implications for regulatory policies

   https://doi.org/10.1016/j.ress.2020.106814  

   Pozzi, Matteo; Malings, Carl; Minca, Andreea (January 2020, Reliability engineering systems safety)

   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. 

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3. Optimal inspection of binary systems via value of information analysis

   https://doi.org/https://doi.org/10.1016/j.ress.2021.107944  

   Chaochao Lin, Junho Song (July 2021, Reliability engineering systems safety)

   null (Ed.)

   We develop computable metrics to assign priorities for information collection on binary systems composed of binary components. Components are worth inspecting because their condition states are uncertain, and system functioning depends on them. The Value of Information (VoI) enables assessment of the impact of information in decision making under uncertainty, including the component’s reliability and role in the system, the precision of the observation, the available maintenance actions and the expected economic loss. We introduce the VoI-based metrics for system-level (“global”) and component-level (“local”) maintenance actions, analyze the properties of these metrics, and apply them to series and parallel systems. We discuss their computational complexity in applications to general network systems and, to tame the complexity for the local metric assessment, we present a heuristic and assess its performance on some case studies. 

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4. Disaster Management and Information Transmission Decision-Making in Public Safety Systems

   https://doi.org/10.1109/GLOBECOM38437.2019.9013440  

   Fragkos, Georgios; Tsiropoulou, Eirini Eleni; Papavassiliou, Symeon (December 2019, 2019 IEEE Global Communications Conference (GLOBECOM))

   This paper introduces a Multi-Agency DisAster Management (MADAM) framework for Unmanned Aerial Vehicle (UAV)-assisted public safety systems, based on the principles of game theory and reinforcement learning. Initially, the information quality and criticality (IQC) provided by each agency to an UAV-assisted public safety network is introduced and quantified, and the concept of Value of Information (VoI) that measures each agency’s positive contribution to the overall disaster management process is defined. Based on these, a holistic cost function is adopted by each agency, reflecting its relative abstention from the information provisioning process. Each agency aims at minimizing its personal cost function in order to better contribute to the disaster management. This optimization problem is formulated as a non-cooperative game among the agencies and it is proven to be an exact potential game, thus guaranteeing the existence of at least one Pure Nash Equilibrium (PNE). We propose a binary log-linear reinforcement learning algorithm that converges to the optimal PNE. The performance of the proposed approach is evaluated through modeling and simulation under several scenarios, and its superiority compared to other approaches is demonstrated. 

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5. Optimal adaptive inspection and maintenance for redundant systems

   https://doi.org/10.1177/1748006X211020151  

   Lin, Chaochao; Pozzi, Matteo (August 2021, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability)

   null (Ed.)

   Optimal exploration of engineering systems can be guided by the principle of Value of Information (VoI), which accounts for the topological important of components, their reliability and the management costs. For series systems, in most cases higher inspection priority should be given to unreliable components. For redundant systems such as parallel systems, analysis of one-shot decision problems shows that higher inspection priority should be given to more reliable components. This paper investigates the optimal exploration of redundant systems in long-term decision making with sequential inspection and repairing. When the expected, cumulated, discounted cost is considered, it may become more efficient to give higher inspection priority to less reliable components, in order to preserve system redundancy. To investigate this problem, we develop a Partially Observable Markov Decision Process (POMDP) framework for sequential inspection and maintenance of redundant systems, where the VoI analysis is embedded in the optimal selection of exploratory actions. We investigate the use of alternative approximate POMDP solvers for parallel and more general systems, compare their computation complexities and performance, and show how the inspection priorities depend on the economic discount factor, the degradation rate, the inspection precision, and the repair cost. 

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