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.

人工智能代理和人类之间以及代理之间的不同意见的管理需要进一步加深对不同意见本质的理解，其中包括对代理对物体评估的差异和代理对物体表征的差异所引发的不同意见的研究。