Understanding Barandes' microscopic theory of causality

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For me the only conceptual resolution to this is the insight that the laws of nature is emergent. I see it like this: Its exactly because nature does not "know" that the natural resolution is stochastic progression, but one that is constrained by interaction history. Each subsystems "random walk" as it interact with other subsystems has a definite history, but this does not map onto an single objective beable state, like in system dynamics; this is where I see paradigm breakdown. Instead what reaplces the state space is more like an evolving "network" or contexts. A "trajectory" in this is a different animal than a "path" in a state space. This makes sense to me. But the problem is that I see "law" (ie differential equations) beening replaced by a kind of stochastic process/algorithm. So the arrow of time as defined by dynamical law, is conceptually replaced by a "learning" network. And they can be consistent if and only if the learning network reaches some steady state, where "effective" dynamical laws can be seem at moderate time scales.

This is conceptually fine for me, but the missing part is the algoritmic implementation, and proof that steady states that map onto the standard models "effective theories" - ie differential equations in effective state spaces - exists. This is more complicated, but conceptually more satisfactory than having a dynamical law that "just is" for no particular reasons. This is easier to accept if one realizes that even classical physics has mysteries. We are just "used to" those mysteries and accept them.

You might find this Curt Jaimungal episode interesting:

But this view is a plain descrpitive probability of all time history, which in itself has zero predictive value in the situation where you want to guess the near future from a limited sample. Descriptive statistics give no insight into causal mechanisms at all.

Well I think all probabilities bottom-out this way. Probabilities just won't be veridical or even meaningful, at least for physical systems, if they don't match approximately what would happen if you repeated scenarios infinitely many times. The Omniscient observer has just made a direct calculation of these probabilities in an objectivist / frequentist manner. From the perspective of another kind of observer standing at some point in time of the history of the specific scenario being codified, and subjectively not knowing what will happen next, these probabilities will still have predictive value.

At the same time, this descriptive kind of laws is the same kind of perspective Morbert was pointing at when he said Laws codify what we can say about it - Humean regularities in the behavior of the universe. Albeit, to some extent this codification kind of depends on what an observer happens to be able to see or cognitively process, and the omniscient observer can count stuff that ordinary people are unable to see. But the point is that from this perspective; if definite trajectories exist and they can be counted, then you can codify probabilistic laws about them in principle from which the indivisible probabilities emerge as a special case.