Many complex systems involve large-scale interconnection of agents with heterogeneous information who interact strategically. Such systems are prevalent in diverse domains ranging from cyber-physical systems, transportation networks, financial markets, consumer networks, and more broadly, complex social and economic networks. The strategic interactions of the agents affect both the direct and indirect (inferred) flow of information, hence introducing new challenges to modeling, analysis, and control. In my talk, I discuss several applications of this nature, ranging from endogenous spreading processes in large networks, to dynamic information aggregation and learning, to stochastic optimal control with non-classical information structure (e.g., Witsenhausen’s counterexample). In all such cases, I present a collection of ideas and methods from game theory, networks, microeconomics, and applied probability theory to address the challenges resulting from the interplay between the information flow and actions.