Mathematics of an updated, plural Economics

Having similar applications, users, and backgound, at a distance Machine Learning may sometimes be confused with an application of Statistics.

A closer look reveal fundamental differences, as in “Why a Mathematician, Statistician, & Machine Learner Solve the Same Problem Differently” by Nir Kaldero.

One scientific field this difference comes to surface in a distinguished manner is economics, as Noah Smith’s “Economics Has a Math Problem” sensibly puts the emphasis on the way economics uses math.

Pushing science to new fields, scientists can now employ much more data and computational power than the time when a significant part of mainstream economics was developed.  If econometric tools set the tone for neoclassic economic papers in the final decades of last century, could machine learning, Bayesian inference, and neural networks open new possibilities to economic theory?

One arguable example is “Mechanisms for Multi-unit Combinatorial Auctions with a Few Distinct Goods” by Piotr Krysta, Orestis Telelis, Carmine Ventre.  Not a coincidence, researchers are not from Economics departments.  Even if economists are stubborn enough to dismiss game theory as a non-fundamental field, message is clear: if economists don’t embrace new math, other scientists (human or not) could engulf economics less cerimoniously.

If this happens, will we find that Keynesian uncertainty and weight of arguments fits big data better than deterministic parameters of neoclassic mainstream?