Sampling on Social Networks from a Decision Theory Perspective
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Some of the most used sampling mechanisms that propagate through a social network are defined in terms of tuning parameters, for instance, Respondent-Driven Sampling (RDS) is specified by the number of seeds and maximum number of referrals. We are interested in the problem of optimising these tuning parameters with the purpose of improving the inference of a population quantity, where such quantity is a function of the network and measurements taken at the nodes. This is done by formulating the problem in terms of Decision Theory. The optimisation procedure for different sampling mechanisms is illustrated via simulations in the fashion of the ones used for Bayesian clinical trials.
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