I also didn't care for the coarse characterizations nearby.
I take your point about the distinction between models that reproduce behavior ("simply predictive") vs. models of underlying components, and what you can learn from both.
This comes up in fields I work on with machine learning models vs. physics-based models. E.g., ML models that take a field of wind vectors at time t, and predict the wind at time t+1, vs. physical models that implement the flow equations. You can fit parameters of both flavors of models to match observations, but we certainly have more confidence in the robustness of the physics-based models.
About mathematically-challenged biologists - here's a hypothesis. I'll bet that if you started scanning conference abstracts in your domain for "uncertainty quantification," then some more carefully-posed modeling activities would crop up. (As you suggest, probably in the domains where more quantitative work is done.)
I take your point about the distinction between models that reproduce behavior ("simply predictive") vs. models of underlying components, and what you can learn from both.
This comes up in fields I work on with machine learning models vs. physics-based models. E.g., ML models that take a field of wind vectors at time t, and predict the wind at time t+1, vs. physical models that implement the flow equations. You can fit parameters of both flavors of models to match observations, but we certainly have more confidence in the robustness of the physics-based models.
About mathematically-challenged biologists - here's a hypothesis. I'll bet that if you started scanning conference abstracts in your domain for "uncertainty quantification," then some more carefully-posed modeling activities would crop up. (As you suggest, probably in the domains where more quantitative work is done.)