We present a new method, "kairosis", for aggregating probability forecasts made over a time period of a single outcome determined at the end of that period. Informed by work on Bayesian change-point detection, we begin by constructing for each time during the period a posterior probability that the forecasts before and after this time are distributed differently. The distribution of these probabilities is then integrated to give a cumulative mass function, which is then used to calculate a weighted median forecast. The effect is to construct a pool in which those forecasts are most heavily weighted which have been made since the likely most recent change in the forecasts' distribution. Kairosis outperforms standard methods, and is especially suitable for geopolitical forecasting tournaments because it is observed to be robust across disparate questions and forecaster distributions.

翻译：本文提出了一种名为“kairosis”的新方法，用于聚合针对某一时期内最终确定结果所做出的概率预测。该方法借鉴贝叶斯变点检测的研究，首先为该时期内每个时间点构建一个后验概率，用于表征该时间点前后的预测分布是否发生改变。随后对这些概率的分布进行积分得到累积质量函数，并以此计算加权中位数预测。其核心效果在于构建一个预测池，其中自预测分布最可能发生最近一次变化后产生的预测将被赋予最大权重。Kairosis在性能上优于传统方法，尤其适用于地缘政治预测竞赛，因为其在不同类型问题及预测者分布条件下均表现出良好的鲁棒性。