  The theory of bias-variance used to serve as a guide for model selection when
applying Machine Learning algorithms. However, modern practice has shown
success with over-parameterized models that were expected to overfit but did
not. This led to the proposal of the double descent curve of performance by
Belkin et al. Although it seems to describe a real, representative phenomenon,
the field is lacking a fundamental theoretical understanding of what is
happening, what are the consequences for model selection and when is double
descent expected to occur. In this paper we develop a principled understanding
of the phenomenon, and sketch answers to these important questions.
Furthermore, we report real experimental results that are correctly predicted
by our proposed hypothesis.
