  Here, I provide some reflections on Prof. Leo Breiman's "The Two Cultures"
paper. I focus specifically on the phenomenon that Breiman dubbed the "Rashomon
Effect", describing the situation in which there are many models that satisfy
predictive accuracy criteria equally well, but process information in the data
in substantially different ways. This phenomenon can make it difficult to draw
conclusions or automate decisions based on a model fit to data. I make
connections to recent work in the Machine Learning literature that explore the
implications of this issue, and note that grappling with it can be a fruitful
area of collaboration between the algorithmic and data modeling cultures.
