All this “physics of news” framing repeats the same mistake mainstream economics made decades ago, confusing human action with measurable physical phenomena. People aren’t particles, and opinions aren’t spin states. As Mises and Hazlitt argued, mathematical models give spurious precision when applied to purposeful behavior: they hide their "arbitrary assumptions" behind elegant equations. Treating communication, belief, and motivation as quantifiable variables may look rigorous, but it strips away meaning, choice, and context and the very essence of human decision. What results isn’t insight, but an illusion of control dressed up as science.
Physics operates on mechanical causality while human behavior does not.
People are not that complicated, and there's abundant evidence that physical models of social phenomena have decent predictive power, notwithstanding all the intervening complexity. The argument of statistical physics is not that people are particles, but that the equations we've found good at describing various natural phenomena work in social contexts because those processes tend toward efficiently conserving energy and the like. Same reason many human and animal activities tend toward power-law distributions.
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!
"All models are wrong; but some are useful." - George Box
Even particles aren't actually particles, nor spin states actually spin states. The map is not the territory. Physics models are only useful (and "correct") to the extent that they make successful predictions. If the adaptation of these principles to social communication yields useful predictions, then however inaccurate they may be in reproducing the exact nature of what they model, they are nonetheless useful and therefore worthwhile. FTA: "In summary, we review both empirical findings based on massive data analytics and theoretical advances, highlighting the valuable insights obtained from physics-based efforts to investigate these phenomena of high societal impact."
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.
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.
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.
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.
Did you read the paper? Or even the intro? If a model has predictive power, it's capturing something, end of story. What you do with it, popsci spins it, how you interpret it has nothing to do with whether or not it's useful. That's your projection. Everything you are saying it doesn't do as if it's an argument against the paper happens to overlap perfectly with the things it never claimed to do.
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.
Gas isn't a continuous medium, either; it's made up of particles. (Not even true particles, either, and sometimes that matters.) We can still create equations for sound waves, though.
Now, sure, humans are more complicated than gas molecules, and have an element of choice in what they do. But in bulk, they still behave in ways that can be modeled mathematically - perhaps not perfectly, but enough that it can still give some actual insight.
So, an Ising model?
(reads the article)
It's an Ising model!
Harry Seldon et. Al
All this “physics of news” framing repeats the same mistake mainstream economics made decades ago, confusing human action with measurable physical phenomena. People aren’t particles, and opinions aren’t spin states. As Mises and Hazlitt argued, mathematical models give spurious precision when applied to purposeful behavior: they hide their "arbitrary assumptions" behind elegant equations. Treating communication, belief, and motivation as quantifiable variables may look rigorous, but it strips away meaning, choice, and context and the very essence of human decision. What results isn’t insight, but an illusion of control dressed up as science.
Physics operates on mechanical causality while human behavior does not.
People are not that complicated, and there's abundant evidence that physical models of social phenomena have decent predictive power, notwithstanding all the intervening complexity. The argument of statistical physics is not that people are particles, but that the equations we've found good at describing various natural phenomena work in social contexts because those processes tend toward efficiently conserving energy and the like. Same reason many human and animal activities tend toward power-law distributions.
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!
1 reply →
"All models are wrong; but some are useful." - George Box
Even particles aren't actually particles, nor spin states actually spin states. The map is not the territory. Physics models are only useful (and "correct") to the extent that they make successful predictions. If the adaptation of these principles to social communication yields useful predictions, then however inaccurate they may be in reproducing the exact nature of what they model, they are nonetheless useful and therefore worthwhile. FTA: "In summary, we review both empirical findings based on massive data analytics and theoretical advances, highlighting the valuable insights obtained from physics-based efforts to investigate these phenomena of high societal impact."
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.
1 reply →
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.
are you claiming that human action is not measurable physical phenomena?
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.
1 reply →
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.
3 replies →
Did you read the paper? Or even the intro? If a model has predictive power, it's capturing something, end of story. What you do with it, popsci spins it, how you interpret it has nothing to do with whether or not it's useful. That's your projection. Everything you are saying it doesn't do as if it's an argument against the paper happens to overlap perfectly with the things it never claimed to do.
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.
Gas isn't a continuous medium, either; it's made up of particles. (Not even true particles, either, and sometimes that matters.) We can still create equations for sound waves, though.
Now, sure, humans are more complicated than gas molecules, and have an element of choice in what they do. But in bulk, they still behave in ways that can be modeled mathematically - perhaps not perfectly, but enough that it can still give some actual insight.