  The proliferation of social media such as real time microblogging and online
reputation systems facilitate real time sensing of social patterns and
behavior. In the last decade, sensing and decision making in social networks
have witnessed significant progress in the electrical engineering, computer
science, economics, finance, and sociology research communities. Research in
this area involves the interaction of dynamic random graphs, socio-economic
analysis, and statistical inference algorithms. This monograph provides a
survey, tutorial development, and discussion of four highly stylized examples:
social learning for interactive sensing; tracking the degree distribution of
social networks; sensing and information diffusion; and coordination of
decision making via game-theoretic learning. Each of the four examples is
motivated by practical examples, and comprises of a literature survey together
with careful problem formulation and mathematical analysis. Despite being
highly stylized, these examples provide a rich variety of models, algorithms
and analysis tools that are readily accessible to a signal processing,
control/systems theory, and applied mathematics audience.
