We propose a new, more general definition of extended probability measures. We study their properties and provide a behavioral interpretation. We put them to use in an inference procedure, whose environment is canonically represented by the probability space $(\Omega,\mathcal{F},P)$, when both $P$ and the composition of $\Omega$ are unknown. We develop an ex ante analysis -- taking place before the statistical analysis requiring knowledge of $\Omega$ -- in which the true composition of $\Omega$ is progressively learned. We describe how to update extended probabilities in this setting, and introduce the concept of lower extended probabilities. We apply our findings to a species sampling problem and to the study of the boomerang effect (the empirical observation that sometimes persuasion yields the opposite effect: the persuaded agent moves their opinion away from the opinion of the persuading agent).

翻译：我们建议对扩大概率计量方法进行新的、更一般性的定义。我们研究其特性并提供行为解释。我们将其运用于一种推论程序,其环境可以由概率空间$(Omega,\mathcal{F},P)代表,当美元和美元构成都不为人知时。我们开发了事先分析 -- -- 在需要了解美元统计分析之前进行,其中逐渐了解美元的真实构成。我们描述了如何更新这一环境中的扩展概率,并引入了较低概率的概念。我们将我们的调查结果应用于物种取样问题和对潮流效应的研究(有时说服产生相反效果的经验观察:说服者的意见偏离了说服者的意见 ) 。