Networks are ubiquitous in our modern society. Economic and social networks have been used extensively to model a variety of situations, in which individual decision-makers are affected by the choices of their peers in the network. For instance, the choices of individuals regarding which products to buy or whom to vote for are usually influenced by their friends and colleagues. The decision of an individual or a firm on whether or not to adopt a new technology (new software, messaging service, etc.) depends on who among their social or professional network are adopting that technology as well. Banks in financial networks may coordinate private or subsidized bailouts and rescue insolvent banks so as to stop financial contagion. The decision of an individual to become a criminal depends heavily on the behavior of others in his/her social network: more connections to criminals yield a higher profitability in the crime business and thus a higher chance of engaging in criminal activities. This course will introduce the main mathematical models for the study of these networks. It will discuss game theoretical and dynamic optimization techniques, which can be used to analyze a wide variety of these networks, including their resilience to shocks, the amplification effects resulting from their topological structure, and how the strategic behavior of network agents shapes the performance of the network.