  This paper presents models and algorithms for interactive sensing in social
networks where individuals act as sensors and the information exchange between
individuals is exploited to optimize sensing. Social learning is used to model
the interaction between individuals that aim to estimate an underlying state of
nature. In this context the following questions are addressed: How can
self-interested agents that interact via social learning achieve a tradeoff
between individual privacy and reputation of the social group? How can
protocols be designed to prevent data incest in online reputation blogs where
individuals make recommendations? How can sensing by individuals that interact
with each other be used by a global decision maker to detect changes in the
underlying state of nature? When individual agents possess limited sensing,
computation and communication capabilities, can a network of agents achieve
sophisticated global behavior? Social and game theoretic learning are natural
settings for addressing these questions. This article presents an overview,
insights and discussion of social learning models in the context of data incest
propagation, change detection and coordination of decision making.
