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The increasing prevalence of artificial agents creates a correspondinglyincreasing need to manage disagreements between humans and artificial agents,as well as between artificial agents themselves. Considering this larger spaceof possible agents exposes an opportunity for furthering our understanding ofthe nature of disagreement: past studies in psychology have often castdisagreement as two agents forming diverging evaluations of the same object,but disagreement can also arise from differences in how agents represent thatobject. AI research on human-machine alignment and recent work in computationalcognitive science have focused on this latter kind of disagreement, and havedeveloped tools that can be used to quantify the extent of representationaloverlap between agents. Understanding how divergence and misalignment interactto produce disagreement, and how resolution strategies depend on thisinteraction, is key to promoting effective collaboration between diverse typesof agents.

Dimensions of Disagreement: Unpacking Divergence and Misalignment in Cognitive Science and Artificial Intelligence