  System identification aims to build models of dynamical systems from data.
Traditionally, choosing the model requires the designer to balance between two
goals of conflicting nature; the model must be rich enough to capture the
system dynamics, but not so flexible that it learns spurious random effects
from the dataset. It is typically observed that the model validation
performance follows a U-shaped curve as the model complexity increases. Recent
developments in machine learning and statistics, however, have observed
situations where a "double-descent" curve subsumes this U-shaped
model-performance curve. With a second decrease in performance occurring beyond
the point where the model has reached the capacity of interpolating - i.e.,
(near) perfectly fitting - the training data. To the best of our knowledge,
such phenomena have not been studied within the context of dynamic systems. The
present paper aims to answer the question: "Can such a phenomenon also be
observed when estimating parameters of dynamic systems?" We show that the
answer is yes, verifying such behavior experimentally both for artificially
generated and real-world datasets.
