Births are difficult to predict even with rich survey and full-population register data

2026-09-01

AI Summary

研究通过数据挑战预测荷兰居民三年内生育情况，使用从逻辑回归到大型语言模型等方法，结果显示预测精度中等，受数据、方法及偶然性限制。

Abstract

Major life events have proven difficult to predict. Does this reflect limits of theory, data, and algorithms, or the large role of chance? We examine one outcome - having a child within three years - through a near-ideal setting for prediction: a data challenge where 147 researchers predicted births for Dutch residents aged 18-45, using survey data and full-population registers. Methods ranged from logistic regression to a large language model and transformers. Predictions were moderately accurate (best F1: register 0.59, survey 0.76); advanced models did not outperform classical ones; and the larger registers did not beat the survey. Simulating the stochastic biology of conception and pregnancy, we estimated a predictive ceiling (survey F1 ~ 0.86-0.94, register 0.88-0.96). Observed performance falls short of this ceiling, implicating imperfect data, methods, and unmodelled chance, while the ceiling itself shows that chance in reproduction alone sets a non-trivial limit on predicting individual lives.

Problem

• births
• prediction
• chance
• data
• algorithms

Innovation

• data challenge
• reproduction prediction ceiling
• stochastic biology simulation