Why is language vague? Vagueness may be explained and rationalized if it can
be shown that vague language is more useful to speaker and hearer than precise
language. In a well-known paper, Lipman proposes a game-theoretic account of
vagueness in terms of mixed strategy that leads to a puzzle: vagueness cannot
be strictly better than precision at equilibrium. More recently, \'Egr\'e,
Spector, Mortier and Verheyen have put forward a Bayesian account of vagueness
establishing that using vague words can be strictly more informative than using
precise words. This paper proposes to compare both results and to explain why
they are not in contradiction. Lipman's definition of vagueness relies
exclusively on a property of signaling strategies, without making any
assumptions about the lexicon, whereas \'Egr\'e et al.'s involves a layer of
semantic content. We argue that the semantic account of vagueness is needed,
and more adequate and explanatory of vagueness.

模糊语言的可理解性和合理性取决于使用者和听者是否认为其比精确语言更有用，而本文通过比较 Lipman 的博弈论模型和 Égré 等人的贝叶斯模型的结果，解释了它们并不相互矛盾，并强调了语义模型对于解释模糊语言的必要性和准确性。

解释模糊语言

Explaining vague language

Recent work in cognitive science has uncovered a diversity of explanatory
values, or dimensions along which we judge explanations as better or worse. We
propose a Bayesian account of how these values fit together to guide
explanation. The resulting taxonomy provides a set of predictors for which
explanations people prefer and shows how core values from psychology,
statistics, and the philosophy of science emerge from a common mathematical
framework. In addition to operationalizing the explanatory virtues associated
with, for example, scientific argument-making, this framework also enables us
to reinterpret the explanatory vices that drive conspiracy theories, delusions,
and extremist ideologies.

提出了一种贝叶斯方法来解释认知科学中广泛的说明价值，并阐明了心理学、统计学和科学哲学中的核心价值如何从一个公共数学框架中出现。同时，这个框架还可以用来重新解释推动阴谋论、妄想和极端意识形态的说明副产品。