From my perspective, it's a term, coined by a 19th century philosopher, that gets dragged out by people with physics envy.
Modern biology, for example, is incredibly quantitative: molecular biologists collect massive genomics datasets, neuroscientists record from hundreds of sites in the brain, and ecologists, whom everyone thinks of as flannel-clad outdoorsmen, fit sophisticated statistical models. Psychology is getting there too, especially if you're willing to lump in related industries like advertising: Google and Facebook have massive datasets on people's activities and preferences.
It's true that these fields have had less success with mathematical modeling, but you also have to look at the sheer number of interacting variables vs the ~10 terms in Maxwell's equations. It's possible that the underlying phenomena might often just be irreducibly complex, as this perspective argues: (preprint: https://www.biorxiv.org/content/10.1101/764258v3 and was just published in Neuron).
From my perspective, it's a term, coined by a 19th century philosopher, that gets dragged out by people with physics envy.
Modern biology, for example, is incredibly quantitative: molecular biologists collect massive genomics datasets, neuroscientists record from hundreds of sites in the brain, and ecologists, whom everyone thinks of as flannel-clad outdoorsmen, fit sophisticated statistical models. Psychology is getting there too, especially if you're willing to lump in related industries like advertising: Google and Facebook have massive datasets on people's activities and preferences.
It's true that these fields have had less success with mathematical modeling, but you also have to look at the sheer number of interacting variables vs the ~10 terms in Maxwell's equations. It's possible that the underlying phenomena might often just be irreducibly complex, as this perspective argues: (preprint: https://www.biorxiv.org/content/10.1101/764258v3 and was just published in Neuron).