论文标题
核对个人概率预测
Reconciling Individual Probability Forecasts
论文作者
论文摘要
个人概率是指结果仅实现的结果的概率：明天下雨的可能性，爱丽丝在未来12个月内死亡的可能性，鲍勃在未来18个月内因暴力犯罪而被捕的概率等。个人概率基本上是不可知的。然而，我们表明，有两个在数据分发中的数据或如何从数据分发中进行采样的当事方不同意在如何建模个人概率上不同意。这是因为实质上不同意的任何两个单个概率模型都可以将其用于凭经验伪造和改善至少两个模型中的一种。这可以在“对帐”的过程中有效地迭代，该过程导致双方同意的模型优于他们开始的模型，并且（几乎）（几乎）都同意对各个概率（几乎）到处的个人概率的预测。我们得出的结论是，尽管个人概率是不可知的，但它们通过必须导致共识的计算和数据效率流程有争议。因此，我们无法发现自己有两个同样准确且无法解决的模型，这些模型在其预测中基本上不同意 - 为有时所谓的预测性或模型多样性问题提供答案。
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.
