Many infrastructure systems can be modeled as networks of components with binary states (intact, damaged). Information about components conditions is crucial for the maintenance process of the system. However, it is often impossible to collect information of all components due to budget constraints. Several metrics have been developed to assess the importance of the components in relation to maintenance actions: an important component is one that should receive high maintenance priority. Instead, in this paper we focus on the priority to be assigned for component inspections and information collection. We investigate metrics based on system level (global) and component level (local) decision making after inspection for networks with different topology, and compare these results with traditional ones. We then discuss the computational challenges of these metrics and provide possible approximation approaches.We acknowledge the support of NSF project CMMI #1653716, titled CAREER: Infrastructure Management under Model Uncertainty: Adaptive Sequential Learning and Decision Making
