>> If you are a scientist looking for research ideas which are pro-social, and have the potential to create a whole new field, you should consider building formal (mathematical) bridges between results on overfitting in machine learning, and problems in economics, political science, management science, operations research, and elsewhere.
Translation to laymen: ML is being analogized to the mathematical structure of signaling between entities and institutions in society.
Mathematician proposes problem that plagues one (overfitting in ML, the phenomena by which a neural network's ability to generalize is negatively impacted by overtraining so the functions it can emulate are tightly coupled to the training data), must plague the other.
In short, there must be a breakdown point at which overdevelopment of societal systems or signaling between them makes things simply worse.
I personally think all one need do is look at what would happen if every system were perfectly complied with to see we may already be well beyond that breakpoint in several industrial verticals.
Translation to laymen: ML is being analogized to the mathematical structure of signaling between entities and institutions in society.
Mathematician proposes problem that plagues one (overfitting in ML, the phenomena by which a neural network's ability to generalize is negatively impacted by overtraining so the functions it can emulate are tightly coupled to the training data), must plague the other.
In short, there must be a breakdown point at which overdevelopment of societal systems or signaling between them makes things simply worse.
I personally think all one need do is look at what would happen if every system were perfectly complied with to see we may already be well beyond that breakpoint in several industrial verticals.