  We introduce an epistemic information measure between two data streams, that
we term $influence$. Closely related to transfer entropy, the measure must be
estimated by epistemic agents with finite memory resources via sampling
accessible data streams. We show that even under ideal conditions, epistemic
agents using slightly different sampling strategies might not achieve consensus
in their conclusions about which data stream is influencing which. As an
illustration, we examine a real world data stream where different sampling
strategies result in contradictory conclusions, explaining why some politically
charged topics might exist due to purely epistemic reasons irrespective of the
actual ontology of the world.
