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This work addresses the problem of sharing partial information within sociallearning strategies. In traditional social learning, agents solve a distributedmultiple hypothesis testing problem by performing two operations at eachinstant: first, agents incorporate information from private observations toform their beliefs over a set of hypotheses; second, agents combine theentirety of their beliefs locally among neighbors. Within a sufficientlyinformative environment and as long as the connectivity of the network allowsinformation to diffuse across agents, these algorithms enable agents to learnthe true hypothesis. Instead of sharing the entirety of their beliefs, thiswork considers the case in which agents will only share their beliefs regardingone hypothesis of interest, with the purpose of evaluating its validity, anddraws conditions under which this policy does not affect truth learning. Wepropose two approaches for sharing partial information, depending on whetheragents behave in a self-aware manner or not. The results show how differentlearning regimes arise, depending on the approach employed and on the inherentcharacteristics of the inference problem. Furthermore, the analysisinterestingly points to the possibility of deceiving the network, as long asthe evaluated hypothesis of interest is close enough to the truth.

Partial Information Sharing over Social Learning Networks