  This work studies the learning abilities of agents sharing partial beliefs
over social networks. The agents observe data that could have risen from one of
several hypotheses and interact locally to decide whether the observations they
are receiving have risen from a particular hypothesis of interest. To do so, we
establish the conditions under which it is sufficient to share partial
information about the agents' belief in relation to the hypothesis of interest.
Some interesting convergence regimes arise.
