人类在不确定时会权衡风险与成本，决定是否提问澄清。
Act or Clarify? Modeling Sensitivity to Uncertainty and Cost in Communication
- 用预期后悔模型建模提问决策，融合不确定性与行动成本
- 实验显示提问频率随错误代价升高而显著增加
- 适用于需理性决策的对话系统与人机交互设计
在不确定性下做决策时，智能体可选择行动以降低不确定性，或在不确定下直接行动。在交流场景中，提出澄清问题（CQs）是减少不确定性的关键方式。我们预测，是否提出澄清问题取决于上下文不确定性以及替代行动的成本，且两者相互作用：当错误行动代价高时，不确定性影响更大。我们基于预期后悔（expected regret）构建计算模型来形式化这一交互关系。通过两项实验验证：第一项研究纯语言响应，第二项扩展至澄清与非语言行动的选择。结果表明，人类倾向于根据错误带来的重大损失风险，合理调整寻求澄清的频率，体现理性权衡。
原文摘要 · Abstract (English)
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.
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