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Daniel L. Chen
n° 18-975, décembre 2018
Predictive judicial analytics holds the promise of increasing the fairness of law. Much empirical work observes inconsistencies in judicial behavior. By predicting judicial decisions—with more or less accuracy depending on judicial attributes or case characteristics—machine learning offers an...
n° 18-978, décembre 2018
Using data from 1946–2014, we show that audio features of lawyers’ introductory statements improve the performance of the best prediction models of Supreme Court outcomes. We infer voice attributes using a 15-year sample of human-labeled Supreme Court advocate voices. Audio features improved...
Elliott Ash, Daniel L. Chen, Nischal Mainali et Liam Meier
n° 18-979, décembre 2018
What modes of moral reasoning do judges employ? We construct a linear SVM classifier for moral reasoning mode trained on applied ethics articles written by consequentialists and deontologists. The model can classify a paragraph of text in held out data with over 90 percent accuracy. We then apply...
Jean-Pierre Amigues et Michel Moreaux
n° 18-981, décembre 2018
We study the transition to a carbon-free economy in a model with a polluting non-renewable resource and a clean renewable resource. Transforming primary energy into ready-to-use energy services is costly and more efficient energy transformation rates are more costly to achieve. Renewable energy...
Ramya Vunikili, Hitesh Ochani, Divisha Jaiswal, Richa Deshmukh, Daniel L. Chen et Elliott Ash
n° 18-982, décembre 2018
n° 18-92, décembre 2018
Matthew Gentzkow
2018
Maria Dimakopoulou, Zhengyuan Zhou, Susan Athey et Guido W. Imbens
décembre 2018
Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity or complex outcome models, which can lead to difficult estimation problems along the path of learning. We develop...
May Wong
Glenn Ellison
