Could turn out not to be useful, I’m posting before I start reading carefully and have only skimmed the paper.
Copying the first few posts of that bsky thread here, to reduce trivial inconveniences:
This paper resolves a key outstanding issue in the literature on the free energy principle (FEP): Namely, to develop a principled approach to the detection of dynamic Markov blankets 2⁄16
The FEP is a generalized modeling method that describes arbitrary objects that persist in random dynamical systems. The FEP starts with a mathematical definition of a “thing” or “object”: any object that we can sensibly label as such must be separated from its environment by a boundary 3⁄16
Under the FEP, this boundary is formalized as a Markov blanket that establishes conditional independence between object and environment. Nearly all work on the free energy principle has been devoted to explicating the dynamics of information flow in the presence of a Markov blanket 4⁄16
And so, the existence of a Markov blanket is usually assumed. Garnering significantly less interest is the question of how to discover Markov blankets in the first place in a data-driven manner 5⁄16
Accordingly, in this preprint, we leverage the FEP, and the associated constructs of Markov blankets and ontological potential functions, to develop a Bayesian approach to the identification of objects, object types, and the macroscopic, object-type-specific rules that govern their behavior 6⁄16
This is accomplished by reframing the problem of object identification and classification and the problem of macroscopic physics discovery as Markov blanket discovery. More specifically, we develop a class of macroscopic generative models that use two types of latent variables 7⁄16
These are: (1) macroscopic latent variables that coarse-grain microscopic dynamics in a manner consistent with the imposition of Markov blanket structure, and (2) latent assignment variables that label microscopic elements in terms of their role in a macroscopic object, boundary, or environment 8⁄16
Crucially, these latent assignment variables are also allowed to evolve over time, in a manner consistent with Markov blanket structure 9⁄16
As such, this algorithm allows us to identify not only the static Markov blankets that have concerned the literature to date, but also, crucially, to detect and classify the dynamic, time dependent, wandering blankets that have caused controversy in the literature since the turn of the 2020s 10⁄16
abstract:
The free energy principle (FEP), along with the associated constructs of Markov blankets and ontological potentials, have recently been presented as the core components of a generalized modeling method capable of mathematically describing arbitrary objects that persist in random dynamical systems; that is, a mathematical theory of every'' thing″. Here, we leverage the FEP to develop a mathematical physics approach to the identification of objects, object types, and the macroscopic, object-type-specific rules that govern their behavior. We take a generative modeling approach and use variational Bayesian expectation maximization to develop a dynamic Markov blanket detection algorithm that is capable of identifying and classifying macroscopic objects, given partial observation of microscopic dynamics. This unsupervised algorithm uses Bayesian attention to explicitly label observable microscopic elements according to their current role in a given system, as either the internal or boundary elements of a given macroscopic object; and it identifies macroscopic physical laws that govern how the object interacts with its environment. Because these labels are dynamic or evolve over time, the algorithm is capable of identifying complex objects that travel through fixed media or exchange matter with their environment. This approach leads directly to a flexible class of structured, unsupervised algorithms that sensibly partition complex many-particle or many-component systems into collections of interacting macroscopic subsystems, namely, objects'' or things″. We derive a few examples of this kind of macroscopic physics discovery algorithm and demonstrate its utility with simple numerical experiments, in which the algorithm correctly labels the components of Newton’s cradle, a burning fuse, the Lorenz attractor, and a simulated cell.
to wentworthpilled folks: - Arxiv: “Dynamic Markov Blanket Detection for Macroscopic Physics Discovery” (via author’s bsky thread, via week top arxiv)
Could turn out not to be useful, I’m posting before I start reading carefully and have only skimmed the paper.
Copying the first few posts of that bsky thread here, to reduce trivial inconveniences:
abstract:
@Fernando Rosas
I’m curious what y’all ended up thinking of this