This paper presents corpus and experimental data that problematize the traditional analysis of must as a strong necessity modal, as recently revived and defended by von Fintel and Gillies (in Nat Lang Semant 18(4):351–383, 2010). I provide naturalistic examples showing that must p can be used alongside an explicit denial of knowledge of p or certainty in p, and that it can be conjoined with an expression indicating that p is not certain or that not-p is possible. I also report the results of an experiment involving lotteries, where most participants endorsed a sentence of the form must not-p despite being instructed that p is a possibility. Crucially, endorsement was much higher for must in this context than for matched sentences with knowledge or certainty expressions. These results indicate that the requirements for felicitous use of must are weaker than for know and certain rather than being at least as strong, as the epistemic necessity theory would predict. However, it is possible to account for these data while retaining the key insights of von Fintel and Gillies’ analysis of the evidential component of must. I discuss several existing accounts that could be construed in this way and explain why none is completely satisfactory. I then propose a new model that embeds an existing scalar theory into a probabilistic model of informational dynamics structured around questions and answers.

中文翻译：

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必须，知识和直接性

本文礼物语料库和实验数据问题化的传统分析必须作为一个强大的必然模式，作为最近恢复和捍卫冯Fintel和吉利斯（在纳特郎Semant 18（4）：351-383，2010）。我提供显示出自然的实例必须p可以并排的知识的明确拒绝使用p或确定性p，并且它可以具有指示的表达式连体p是不能肯定或不-P是可能的。我还报告了实验涉及彩票，大部分与会者赞同形式的判决结果不能-P ，尽管被告知，p是可能的。至关重要的是，在这种情况下，必须的背书要比具有知识或确定性表达的匹配句子要高得多。这些结果表明，合法使用必需品的要求比认识和确定性要弱，而不是像认知必要性理论所预测的那样强大。然而，也可以考虑到这些数据，同时保留作为证据的分量冯Fintel和吉利斯分析的重要见解绝。我讨论了可以用这种方式解释的几个现有帐户，并解释了为什么没有一个完全令人满意。然后，我提出了一个新模型，该模型将现有的标量理论嵌入到围绕问题和答案构造的信息动力学概率模型中。