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This paper surveys mathematical models, structural results and algorithms incontrolled sensing with social learning in social networks.  Part 1, namely Bayesian Social Learning with Controlled Sensing addresses thefollowing questions: How does risk averse behavior in social learning affectquickest change detection? How can information fusion be priced? How is theconvergence rate of state estimation affected by social learning? The aim is todevelop and extend structural results in stochastic control and Bayesianestimation to answer these questions. Such structural results yield fundamentalbounds on the optimal performance, give insight into what parameters affect theoptimal policies, and yield computationally efficient algorithms.  Part 2, namely, Multi-agent Information Fusion with Behavioral EconomicsConstraints generalizes Part 1. The agents exhibit sophisticated decisionmaking in a behavioral economics sense; namely the agents make anticipatorydecisions (thus the decision strategies are time inconsistent and interpretedas subgame Bayesian Nash equilibria).  Part 3, namely {\em Interactive Sensing in Large Networks}, addresses thefollowing questions: How to track the degree distribution of an infinite randomgraph with dynamics (via a stochastic approximation on a Hilbert space)? Howcan the infected degree distribution of a Markov modulated power law networkand its mean field dynamics be tracked via Bayesian filtering given incompleteinformation obtained by sampling the network? We also briefly discuss how theglass ceiling effect emerges in social networks.  Part 4, namely \emph{Efficient Network Polling} deals with polling in largescale social networks. In such networks, only a fraction of nodes can be polledto determine their decisions. Which nodes should be polled to achieve astatistically accurate estimates?

Dynamics of Social Networks: Multi-agent Information Fusion, Anticipatory Decision Making and Polling