Given a set of probability measures $\mathcal{P}$ representing an agent's knowledge on the elements of a sigma-algebra $\mathcal{F}$, we can compute upper and lower bounds for the probability of any event $A\in\mathcal{F}$ of interest. A procedure generating a new assessment of beliefs is said to constrict $A$ if the bounds on the probability of $A$ after the procedure are contained in those before the procedure. It is well documented that (generalized) Bayes' updating does not allow for constriction, for all $A\in\mathcal{F}$. In this work, we show that constriction can take place with and without evidence being observed, and we characterize these possibilities.

翻译：给定一个表示智能体对σ-代数$\mathcal{F}$中元素认知的概率测度集合$\mathcal{P}$，我们可以计算任意感兴趣事件$A\in\mathcal{F}$概率的上界和下界。若某种信念评估生成程序使得事件$A$概率的上下界在程序执行后包含于执行前，则该程序被称为对$A$具有收缩性。现有文献充分证明，（广义）贝叶斯更新无法对所有$A\in\mathcal{F}$实现收缩。本文表明，无论是否观察到证据，收缩现象均可能发生，并对这些可能性进行了刻画。