Measuring the predictability of life outcomes with a scientific mass collaboration

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.

High-accuracy pipelined iterative-tree optical multiplication

A high-accuracy optical multiplier that uses an optical correlator is described. A symbolic substitution adder that uses the modified signed-digit number representation is used as the basic module. Emphasis is placed on the multiplication of many long words in parallel with minimum latency. The encoding method we employ in the adders permits the use of a new optical algorithm and architecture to generate partial products in symbolic form in parallel. Our multiplication algorithm and architecture are shown to be preferable to other optical techniques and to be competitive with digital technology; they are also shown to be particularly attractive for matrix-vector multiplication applications.