My core objection is, the way I see it, reductionism doesn't stop working for soft sciences in any fundamental way. There's no fundamental irreducibility of a phenomenon (uncertainty principle notwithstanding, soft sciences aren't anywhere near worrying about that); the limit is our computational capacity - of our brains, of our computers, of our scientific discourse. We just can't keep so many pieces in our heads simultaneously, so we don't bother, and create higher-level abstractions to make things easier on ourselves.

That's how I view emergence too: there's no new behavior suddenly appearing when your system is complex enough, behavior that couldn't be predicted from looking at the pieces - it's just too much work to deal with pieces directly. The discontinuity we see doesn't exists in the real world - it's caused by the rungs of our ladder of abstraction.

As an example of the rungs on the ladder: we study gases on a molecular level, modelling them as bouncy balls. We also study gases at a higher level, modelling them as fluids. We go further still, viewing them as a bunch of parameters (pressure, volume, temperature). Three different perspectives, three separate set of behaviors - yet there's no actual discontinuity in the real world, and a lot of interesting phenomena can be observed when we try to create a smooth transition between the models; that is, we look in between the rungs.

> For example, if you are modelling the behaviour of a thermometer at the level of its components, you can model the causal relationships between heater and sensor as a self-correcting feedback loop - this is a qualitative model. Only at the level of the total behaviour can you model the ambient temperature and desired temperature quantitatively.

The way I see it, casual models have only a coarse relationship with the real world. A self-correcting feedback loop can be analyzed in terms of its conceptual components, which are mathematical in nature - but you won't get from here to predicting the behavior of a real-world thermostat until you start plugging in physical models. How much complexity you'll have to deal with depends on the physical model you plug in. There's lots of space for reduction and quantification here, depending on the answers you seek. For example, the concept of "ambient temperature" is a very high-level abstraction in itself - if you're willing to break it apart, suddenly a lot more things across the model become more directly related to the real world, and easier to quantify.

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The point I'm trying to express here is, in my view, there are three types of limits to reductionism and quantification:

- The Uncertainty Principle - the fundamental limit, around which you can't quantify some things. IANAPhysicist, but my feeling is, it's not a principled limit to quantification - it only reveals that we're trying to quantify measures that are ill-defined.

- Fundamental limits to computation - I'm thinking of the Halting Problem, Gödel's Incompleteness Theorems. We can't create quantitative metrics and build reductive models in ways that are uncomputable.

- Practical limits, aka. too hard to bother - this is what I believe is 99% of common arguments against reductionism and examples of emergence. We look at systems as a whole, because looking at pieces is too much work. But whether it's too much for our working memory, or too much for all computing power of our civilization - it's still not a fundamental, philosophical limit, and therefore not a philosophical argument against reductionism.

On that last point, if one can prove that a higher-granularity model would require more compute than the universe could provide over its lifetime, then I'll give it a solid shmaybe as a fundamental limit.

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Addendum on systems science.

I have an interest in systems science, which I pursue to the extent my free time allows. I've studied the basics, done some toy modelling, and one thing I've learned so far is: the most insightful part of modelling a system is plugging numbers into it.

For example, see: https://insightmaker.com/insight/206860/Musings-on-a-HN-comm.... It was my attempt to push numbers through a system model I first described on HN. The working, executable model is on the left, for the conceptual one and original HN comment, scroll to the right. Extra commentary: https://news.ycombinator.com/item?id=26137264.

I now no longer trust models that aren't executable - it's too easy to create something that looks fine in the abstract, but is completely wrong. Making it run on real - quantified - data is the fastest way to discover the depths of one's ignorance, like I did in the example linked above.

(Well, to be honest, 80% of my ignorance was revealed by defining units of measurement for each sink and flow - so if you want a quick way to debullshit a systems model, I suggest starting with that.)