Internal Pluralism and the Limits of Pairwise Comparisons
Pairwise comparisons are a common tool for learning preferences, but they assume local comparisons are sufficient and people can answer decisively. This paper examines how internal pluralism—where individuals evaluate rules based on multiple priorities—compromises these assumptions. It identifies failures: global priorities like proportionality may be missed, and tension between priorities can distort behavior. Allowing indecision reduces queries needed, pointing to methods that elicit priorities directly.
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[Submitted on 2 Jul 2026]
Title:Internal Pluralism and the Limits of Pairwise Comparisons
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Abstract:Local pairwise comparisons are a standard tool for learning how people want decision rules to work, e.g., in participatory design or alignment. However, their use builds in two strong assumptions: that local comparisons are sufficient evidence about how a person wants an automated decision rule to behave, and that people can always answer those comparisons decisively. We investigate how these assumptions may be compromised under internal pluralism: the idea that an individual evaluates decision rules according to multiple authoritative priorities about how the rule should behave. We provide a formal model of such pluralistic preferences over decision rules, which then lets us identify two distinct failures of forced local pairwise comparison data. First, priorities such as proportionality, egalitarianism, and equal treatment are inherently global: what they imply in one case can depend on what happens elsewhere, so local comparisons may fail to capture them. Second, even when priorities are representable locally, tension between strongly-held priorities can generate internal conflict, producing potentially costly behavioral distortions when comparisons are forced. We then use our model to investigate the alternative -- allowing people to report indecision -- and our findings suggest that doing so can considerably reduce the number of queries needed to learn preferences accurately. We conclude by describing how our model points toward preference-learning methods that elicit these priorities directly, yielding more faithful and interpretable accounts of what people value.
Subjects:
Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
Cite as: arXiv:2607.02672 [cs.AI]
(or arXiv:2607.02672v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.02672
arXiv-issued DOI via DataCite
Submission history
From: Michelle Si [view email] [v1] Thu, 2 Jul 2026 18:08:50 UTC (5,280 KB)
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