Been reading, thinking more deeply Caticha's papers recently has some interesting stuff to say: (top two papers below)
https://scholar.google.co.uk/scholar?hl=en&as_sdt=0,5&as_vis=1&q=caticha+entropic+dynamics&oq=caticha
If time is constructed purely based on informational entropic assumptions it must be naturally irreversible and is described in a Markovian sense, independent of the past. He asks then how can some processes be time-reversible and dependent on the past - like quantum mechanics - if time is irreversible. Interestingly, the conditions for / that prevent time-reversibility actually seem to follow from Bayes theorem, and can be cashed out in the following way: the Markovianity conditions only work in one direction, which contradicts time-reversibility and so the system will look different forward in time vs backward - the difference between backward and forward is actually in Bayes' theorem. Seems things shouldn't work in a non-Markovian way under informational-entropic time; as he puts it -
there is no symmetry between the inferential past and inferential future. And this statement is inherently connected to Markovianity.
So it seems from Caticha's papers that you cannot get time-reversibility and non-Markovianity without something extra. He requires extra-variables in addition to particles in his quantum mechanics. Dynamics of extra variables influence dynamics of particles and dynamics of particles influence dynamics of extra-variables so its reciprocal, and this stops the irreversibility of time.
But the way he does it seems kind of tautological in the sense that he just asserts a condition where
something called "energy" is conserved; but, "energy" here is literally defined as just whatever is conserved in the dynamics due to invariance in time-translation - which seems to be another way of saying...
uhh, it just is.
But I think it can still be made more tangible in the following sense - if you assume that the Markovian time is occuring under an open system which interacts with its environment through force and energy. If we don't assume anything about what it gets back from the environment we have Markovianity as the random changes to the system described by entropy maximization are not counterbalanced - we also don't know where the force goes because we have decided to arbitrarily focus on this small compartment of a larger whole isolated system . Adding the extra variables may just be a way of modeling (even if just an idealization) isolatedness - in the sense that if the system is isolated, then the force being exerted elsewhere hasn't left the system - but it needs to go
somewhere, so we can talk about these extra variables as where this force goes. We can then effectively assume that everything lost by the particles, and exerted on the extra-variables, is returned - nothing is leaked out the system and so it is no longer Markovian. Because the informational-entropic time is based around the idea that the transition probability distributions maximize entropy,
but only in one direction, I guess one could see that as dynamics over time are inherently characterized by the loss of information which maybe is returned by the extra-variables.
The coupling of the particles to the extra variables to my eyes then looks, at least superficially, kind of like a recurrent neural network, especially an Elman network.
https://en.wikipedia.org/wiki/Recurrent_neural_network#Elman_network
The
y units in the Elman depiction receive inputs that change their state at one time-step, then outputs from the
y unit go into the
u unit which recycle
y's outputs back to it at the next time-step - this means
y not only depends on its immediate inputs but its history - what happened in the past. This recurrence then is the very basic principle of how recurrent neural networks (and indeed brains where recurrence is fundamental) can learn sequential, history-dependent information (e.g. what comes next in the sentence based on what came before, but modern large language models are based on a more efficient way of doing this than recurrent neural networks though). Maybe this is a way of an
intuition for the non-Markovianity - the particle outputs are fed back onto them so that nothing leaks out (In terms of physical energy, neural activity), but simultaneously rendering the system history-dependent and time-reversible.
The extra-variables take the role of the phase in quantum mechanics. Caticha later plays down the extra-variables because he doesn't have an interpretation at hand; but there is a trade-off in the sense that by playing down the extra-variables, you just have to make certain assumptions about the existence of phase and related things - but the extra-variables give you them all for free.
What perhaps is most interesting is that quantum-type non-locality appears to occur in Caticha's Newtonian theory (first two papers below but specifically second paper, section 5):
https://scholar.google.co.uk/schola...is=1&q=caticha+newtonian+inference+to+physics
You see in his Newtonian system for multiple particles in an isolated system, their Newtonian behaviors depend on each other in a similar way to Bohmian mechanics (but note equation (39) and the following note: particles still are statistically
conditionally independent). Seems to me this is from the fact that the Newtonian behavior conserves energy. And interestingly, in later papers you see that both the quantum potential in Bohmian mechanics, responsible for quantum non-locality; and the quantum configuration space both are consequences of the information metric which characterizes his entropic dynamics even in the Newtonian case (and curvedness is related to information): i.e.
https://scholar.google.co.uk/scholar?cluster=2247357911529834348&hl=en&as_sdt=0,5&as_vis=1
Caticha's Newtonian dynamics are obviously time-reversible and conserve energy but it still emerges from the lossy entropic dynamics, evident in the fact that the Newtonian dynamics is only along the most probably path: i.e. analogous to path of least action.