Sensor management plays a key role in control of critical infrastructure systems. This paper describes an approach for improving capabilities for interpretation, decision, and action based on sensor data through application of an intermediate level of aggregation. Improvements in complex system understanding are needed now at the interface between human understanding of system state and machine understanding of system state. The human understanding of the state of the system (situation understanding) must be achieved under ever more demanding time constraints. As expectations increase for faster, more-informed (better) decisions by humans at the supervisory-control level, improvements are needed for providing support for interpreting sensor data to understand current system behaviors and make informed human decisions on actions needed to cause future system behaviors to comply with some planned sequence of events or patterns of behavior. Likewise, as the number and capabilities of networked sensors increase, improvements are needed in enabling autonomous control systems at local levels to understand current system behaviors and make informed machine decisions on actions needed to cause future system behaviors to comply with some planned sequence of events or patterns of behavior. The paper discusses achieving an intermediate level of aggregation: (1) as a scientific basis for understanding complex system behaviors, (2) as an effective tool for creation of technologies based an intermediate level of aggregation and (3) as a basis for education of leaders who must make decisions based on understanding of the current system state.