System Science Can Relax the Tension Between Data and Theory

The actual hype around machine learning (ML) methods has pushed the old epistemic struggle between data-driven and theory-driven scientific styles well beyond the academic realm. The potential consequences of the widespread adoption of ML in scientific work have fueled a harsh debate between opponents predicting the decay of basic curiosity-driven science and enthusiasts hoping for the advent of a ‘theory-free’ objective science. In this work, I suggest how the system science style of reasoning could drastically de-potentiate this (sometimes deceptive) opposition through the generation of multi-purpose relational theoretical frames stemming from the network paradigm. The recognition of the virtual non-existence of purely ‘theoryfree’ approaches and the need for a careful balancing of theoretical and empirical contributions is the main claim of the present work.