If hundreds of scientists created predictive algorithms with high-quality data, how well would the best predict life outcomes? Not very well. The paper summarizing the methods and results of the FFCWS Challenge led by Matt Salganik and Ian Lundberg has been published in Proceedings of the National Academy of Science. Socius has also published a special collection of 12 articles describing participants’ approaches to predicting these six outcomes as well as 3 articles describing methodological and procedural insights from running the Challenge. 

B. Rose Huber summarized the Challenge results in her article on Princeton's website:

"One hundred and sixty research teams of data and social scientists built statistical and machine-learning models to predict six life outcomes for children, parents and households. Even after using a state-of-the-art modeling and a high-quality dataset containing 13,000 data points about more than 4,000 families, the best AI predictive models were not very accurate."

The full paper can be found here.

Illustration by Egan Jimenez, Woodrow Wilson School of Public and International Affairs