Individual probabilities refer to the probabilities of outcomes that are realized only once: the probability that it will rain tomorrow, the probability that Alice will die within the next 12 months, the probability that Bob will be arrested for a violent crime in the next 18 months, etc. Individual probabilities are fundamentally unknowable. Nevertheless, we show that two parties who agree on the data -- or on how to sample from a data distribution -- cannot agree to disagree on how to model individual probabilities. This is because any two models of individual probabilities that substantially disagree can together be used to empirically falsify and improve at least one of the two models. This can be efficiently iterated in a process of "reconciliation" that results in models that both parties agree are superior to the models they started with, and which themselves (almost) agree on the forecasts of individual probabilities (almost) everywhere. We conclude that although individual probabilities are unknowable, they are contestable via a computationally and data efficient process that must lead to agreement. Thus we cannot find ourselves in a situation in which we have two equally accurate and unimprovable models that disagree substantially in their predictions -- providing an answer to what is sometimes called the predictive or model multiplicity problem.

翻译：个体概率指的是仅发生一次的结果的概率：明天下雨的概率、爱丽丝未来12个月内去世的概率、鲍勃未来18个月内因暴力犯罪被捕的概率等。个体概率从根本上而言是不可知的。然而，我们证明，如果两方在数据上达成一致——或者就如何从数据分布中采样达成一致——那么他们不能同意对个体概率进行建模的方式存在分歧。这是因为任何两种存在实质性分歧的个体概率模型，可以共同用于经验性地证伪并改进至少其中一个模型。通过“调和”过程可以高效地迭代进行，最终得到双方一致认为优于初始模型的模型，并且这些模型本身在（几乎）所有地方对个体概率的预测（几乎）一致。我们的结论是：尽管个体概率不可知，但通过一个计算和数据高效的、必然导向共识的过程，它们是可争议的。因此，我们不会陷入这样一种情景：存在两个预测精度相当且无法进一步改进的模型，但其预测却存在重大分歧——这为有时被称为预测或模型多重性问题提供了答案。