How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.

Measuring and forecasting migration patterns has important implications for understanding broader population trends, for designing policy effectively and for allocating resources. However, data on migration and mobility are often lacking, and those that do exist are not available in a timely manner. Social media data offer new opportunities to provide more up-to-date demographic estimates and to complement more traditional data sources. Facebook's Advertising Platform, for example, is a potentially rich data source of demographic information that is regularly updated. However, Facebook's users are not representative of the underlying population. This paper proposes a statistical framework to combine social media data with traditional survey data to produce timely 'nowcasts' of migrant stocks by state in the United States. The model incorporates bias adjustment of Facebook data, and a pooled principal component time series approach, to account for correlations across age, time and space. We use the model to estimate and project migrants from Mexico, India and Germany, three migrant groups with varying levels and trends of migration in the US. By comparing short-term projections with data from the American Community Survey, we show that the model predictions outperform alternatives that rely solely on either social media or survey data.

No abstract

Survey data sets are often wider than they are long. This high ratio of variables to observations raises concerns about overfitting during prediction, making informed variable selection important. Recent applications in computer science have sought to incorporate human knowledge into machine-learning methods to address these problems. The authors implement such a “human-in-the-loop” approach in the Fragile Families Challenge. The authors use surveys to elicit knowledge from experts and laypeople about the importance of different variables to different outcomes. This strategy offers the option to subset the data before prediction or to incorporate human knowledge as scores in prediction models, or both together. The authors find that human intervention is not obviously helpful. Human-informed subsetting reduces predictive performance, and considered alone, approaches incorporating scores perform marginally worse than approaches that do not. However, incorporating human knowledge may still improve predictive performance, and future research should consider new ways of doing so.