This talk describes stochastic control problems involving social sensing models. Humans can be viewed as social sensors that input information to a social network. The interaction of social sensors present unique challenges: sensors interact with and influence other social sensors resulting in herding behaviour. Social sensors are risk averse decision makers. This talk describes how herding can be mitigated by providing incentives to individual sensors. Also experimental data on human interaction will be discussed to motivate the models and algorithms presented.