  We analyze the dynamics of the Learning-Without-Recall model with Gaussian
priors in a dynamic social network. Agents seeking to learn the state of the
world, the "truth", exchange signals about their current beliefs across a
changing network and update them accordingly. The agents are assumed memoryless
and rational, meaning that they Bayes-update their beliefs based on current
states and signals, with no other information from the past. The other
assumption is that each agent hears a noisy signal from the truth at a
frequency bounded away from zero. Under these conditions, we show that the
system reaches truthful consensus almost surely with a convergence rate that is
polynomial in expectation. Somewhat paradoxically, high outdegree can slow down
the learning process. The lower-bound assumption on the truth-hearing frequency
is necessary: even infinitely frequent access to the truth offers no guarantee
of truthful consensus in the limit.
