Motivated by economic and technological changes, Smart Cities are being constructed upon intelligent infrastructures spanning energy, healthcare, and transportation. The advent of these societal-scale infrastructures brings with it new opportunities for improving efficiency while simultaneously exposing novel vulnerabilities. In energy societal-scale cyber-physical systems (S-CPS), for example, smart metering technologies increase the availability of streaming data thereby enabling monetization of energy savings. Such savings can be realized by employing machine learning algorithms to customize offerings to consumers. On the other hand, the availability of this fine-grained consumer/system data and the increased number of access points to the broader system expose new privacy and security risks. The development of a S-CPS design methodology in support of resilient, sustainable operation of Smart Cities necessitates a rigorous analytical and computational framework for analyzing information exchanges between agents and for synthesizing new service models that improve efficiency. This may proceed, for instance, by introducing resilient controls for operations as well as incorporating the use of vulnerability-aware incentives to shape consumer choice, an essential element of operations.