The long human lifespan enables long run forecasts of population size and age distribution. New methods include biodemographic research on upper limits to life expectancy and incorporation of early experiences affecting later life mortality such as smoking, obesity, and childhood health shocks. Some fertility forecasts incorporate education and quantum‐tempo insights. Statistical time series and Bayesian methods generate probabilistic forecasts. Yet recent decades have brought surprising changes in the economy, natural environment, and vital rates. In these changing circumstances we need new methods and the increasing use of probabilistic models and Bayesian methods incorporating outside information. The increasing use of microsimulation combined with aggregate forecasting methods is a very promising development enabling more detailed and heterogeneous forecasts. Some new uses of stochastic forecasts are interesting in themselves. Probabilistic mortality forecasts are used in finance and insurance, and a new Longevity Swap industry has been built on them. Random sample paths used to generate stochastic population forecasts can stress‐test public pension designs for fiscal stability and intergenerational equity. Population forecasting a few decades ago was a dull backwater of demographic research, but now it is increasingly important and is full of intellectual and technical challenges.

中文翻译：

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预测不确定世界中的人口：方法、新用途和令人不安的局限性


人类的寿命很长，可以对人口规模和年龄分布进行长期预测。新方法包括对预期寿命上限的生物人口学研究，以及纳入影响晚年死亡率的早期经历，如吸烟、肥胖和儿童健康冲击。一些生育率预测结合了教育和量子节奏的见解。统计时间序列和贝叶斯方法生成概率预测。然而，近几十年来，经济、自然环境和人口动态发生了令人惊讶的变化。在这些不断变化的情况下，我们需要新的方法，并越来越多地使用概率模型和结合外部信息的贝叶斯方法。微观模拟与综合预测方法相结合的使用越来越多，这是一个非常有前途的发展，可以实现更详细和异构的预测。随机预测的一些新用途本身就很有趣。概率死亡率预测应用于金融和保险领域，并在此基础上建立了新的长寿互换行业。用于生成随机人口预测的随机样本路径可以对财政稳定性和代际公平的公共养老金设计进行压力测试。几十年前，人口预测是人口研究的沉闷死水，但现在它变得越来越重要，并且充满了智力和技术挑战。