  The increasing prevalence of artificial agents creates a correspondingly
increasing need to manage disagreements between humans and artificial agents,
as well as between artificial agents themselves. Considering this larger space
of possible agents exposes an opportunity for furthering our understanding of
the nature of disagreement: past studies in psychology have often cast
disagreement as two agents forming diverging evaluations of the same object,
but disagreement can also arise from differences in how agents represent that
object. AI research on human-machine alignment and recent work in computational
cognitive science have focused on this latter kind of disagreement, and have
developed tools that can be used to quantify the extent of representational
overlap between agents. Understanding how divergence and misalignment interact
to produce disagreement, and how resolution strategies depend on this
interaction, is key to promoting effective collaboration between diverse types
of agents.
