Algorithmic criminology

Computational criminology has been seen primarily as computer-intensive simulations of criminal wrongdoing. But there is a growing menu of computer-intensive applications in criminology that one might call "computational," which employ different methods and have different goals. This paper provides an introduction to computer-intensive, tree-based, machine learning as the method of choice, with the goal of \textit{forecasting} criminal behavior. The approach is "black box," for which no apologies are made. There are now in the criminology literature several such applications that have been favorably evaluated with proper hold-out samples. Peeks into the black box indicate that conventional, causal modeling in criminology is missing significant features of crime etiology.