Act or Clarify? Modeling Sensitivity to Uncertainty and Cost in Communication
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Abstract (arXiv)
When deciding how to act under uncertainty, agents may choose to act to reduce uncertainty or they may act despite that uncertainty. In communicative settings, an important way of reducing uncertainty is by asking clarification questions (CQs). We predict that the decision to ask a CQ depends on both contextual uncertainty and the cost of alternative actions, and that these factors interact: uncertainty should matter most when acting incorrectly is costly. We formalize this interaction in a computational model based on expected regret: how much an agent stands to lose by acting now rather than with full information. We test these predictions in two experiments, one examining purely linguistic responses to questions and another extending to choices between clarification and non-linguistic action. Taken together, our results suggest a rational tradeoff: humans tend to seek clarification proportional to the risk of substantial loss when acting under uncertainty.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 33 references that are in the map. First posted on arXiv 2 Feb 2026.
Builds on
8 of 33 references.
References · 8
- 2024STaR-GATE: Teaching Language Models to Ask Clarifying Questionscited by 99
- 2024CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Modelscited by 48
- 2021Ask what's missing and what's useful: Improving Clarification Question Generation using Global Knowledgecited by 40
- 2024Asking the Right Question at the Right Time: Human and Model Uncertainty Guidance to Ask Clarification Questionscited by 32
- 2026Value of Information: A Framework for Human-Agent Communicationcited by 10
- 2025Shoot First, Ask Questions Later? Building Rational Agents that Explore and Act Like Peoplecited by 7
- 2025Reasoning about Intent for Ambiguous Requestscited by 2
- 2025How2: How to learn from procedural How-to questionscited by 1
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Citing papers per year: 2026: 1.
1 paper.