This work addresses the problem of sharing partial information within social learning strategies. In traditional social learning, agents solve a distributed multiple hypothesis testing problem by performing two operations at each instant: first, agents incorporate information from private observations to form their beliefs over a set of hypotheses; second, agents combine the entirety of their beliefs locally among neighbors. Within a sufficiently informative environment and as long as the connectivity of the network allows information to diffuse across agents, these algorithms enable agents to learn the true hypothesis. Instead of sharing the entirety of their beliefs, this work considers the case in which agents will only share their beliefs regarding one hypothesis of interest, with the purpose of evaluating its validity, and draws conditions under which this policy does not affect truth learning. We propose two approaches for sharing partial information, depending on whether agents behave in a self-aware manner or not. The results show how different learning regimes arise, depending on the approach employed and on the inherent characteristics of the inference problem. Furthermore, the analysis interestingly points to the possibility of deceiving the network, as long as the evaluated hypothesis of interest is close enough to the truth.

翻译：这项工作解决了在社会学习战略中分享部分信息的问题。在传统的社会学习中,代理商通过每瞬间进行两次操作来解决分布式的多重假设测试问题:首先,代理商将私人观察的信息纳入到一套假设中,从而形成其信仰;第二,代理商将其在邻国之间的全部信仰结合起来;在信息充足的环境中,只要网络的连通允许信息在代理商之间传播,这些算法就能使代理商了解真实的假设。在这项工作中,代理商不分享其全部信仰,而是考虑这样的案例,即代理商只分享其对一种利益假设的信念,目的是评估其有效性,并列出这一政策不影响真相学习的条件。我们提出了分享部分信息的两个方法,取决于代理商是否以自我觉的方式行事。结果显示,根据所采用的方法和推断问题的内在特征,不同学习制度是如何产生的。此外,分析令人感兴趣的是,只要被评估的兴趣假设与真相足够接近,就有可能欺骗网络。