Human action isn’t just another physical process because it’s driven by intention, not mechanical causation. A rock falls because gravity compels it; a person acts because they want something to happen. That difference makes human behavior fundamentally qualitative. It’s rooted in meaning, interpretation, and choice. You can measure motion, but you can’t measure purpose. Once you strip away intention to fit behavior into a mathematical model, you’re no longer describing human action. You’re describing an abstraction that behaves like a machine. The numbers might be tidy, but they stop representing what people actually do.

Human action is entirely causally dependent on human psychology vis a vis biology, of which we have now only a rudimentary formal understanding and certainly not a sufficient model of its structure, let alone the relation between its aspects and the resulting actions.

At the same time, statistical methods are interesting and suggestive but should be understood at the relatively coarse level they inhabit.

Both approaches have their uses and it is worth delineating the boundary between their respective appropriate contexts.

You should deploy a quant trading model based on your predictive behavior theory and compound your way to be the richest person on earth. It's not that complicated apparently!

Natural scientific models use the vocabulary of pre-existing intuitions we have from interacting with the world: particles, rotation, etc. We try to simplify predictions by mapping experimental outcomes using things we already know and can directly perceive.

Using these abstractions as foundation for models of social dynamics and modern media feels a little wrong: the mapping is imperfect and incomplete, and as these physical models inevitably become outdated it will be more and more difficult to make sense of social dynamics models that use these physical models as a given.

The word “some” in the quote from Box is doing a lot of heavy lifting.

If a model is useful, I’d like to see it being used (outside academia, where there’s minimal penalty for complexity and a high emphasis on novelty).

If models like these are widely adopted at social media companies or news agencies, it’s fair to say OP’s take isn’t valid. Otherwise they may have a point.

Predictive power alone doesn’t equal causal understanding. The paper models news and opinion spread as physical processes that may (over)fit observed data, but it never establishes why these patterns occur. No counterfactuals, no intervention logic, no identification strategy. As causal inference work (like Stefan Wager's) makes clear, explanation demands more than correlation. Treating human communication as node-to-node contagion might predict past outcomes, but it misses the purposive, context-driven nature of choice. So while the model captures statistical regularities, it lacks the causal rigor needed to claim genuine understanding of human behavior.

I'm assuming you've never predicted things in practice for a living? e.g. as a quant trader? Quants have something called a "deflated sharpe ratio" since p-hacking / overfitting historical data is such a common thing and results in losses when projected into the future.

Wow man. Thermodynamics operates on mechanical causality while human behavior does not. Because of the internal state of a human.

Do you just believe anything that was p-hacked together? I can tell you have never predicted human behavior in practice for a living. If you're so interested, why not learn the basics of what quant traders do to predict behavior? Then your opinions would be a little more informed and not quite so embarrassing for yourself. Just a suggestion.

Also, if A implies B and B implies C then A implies C. You're welcome!!