Proceedings of the Annual Meeting of the Cognitive Science Society

Ambiguity is pervasive in verbal expressions of uncertainty: utterances such as "likely'' are interpreted as different probabilities by different individuals, leading to systematic misunderstandings between speakers and hearers. What are the group-level effects of such misunderstandings? Can they lead deliberating collectives to inaccurate beliefs, or pronounced disagreements? To address these questions, we introduce a new Bayesian agent-based model of ambiguous testimony, designed to compare the effects of increasingly ambiguous communication against a baseline of ideal exchange. We find that increasing ambiguity reduces collective accuracy and increases belief dispersion. For mild ambiguity, denser communication networks mitigate these risks. Under strong ambiguity, however, increased communication can backfire: higher network density can exacerbate collective inaccuracy and polarization.