UPTM #3 | Understanding Man via the Free Energy Principle, The Bayesian Brain, and Markov Blankets
UPTM #3 | Understanding Man via the Free Energy Principle, The Bayesian Brain, and Markov Blankets
Can we leverage the latest insights in complexity science & computational neuroscience to deepen our understanding of the organic process themes found in nature?
|| UPTM Table of Contents || Prelude | Part 1: 21st Century Paradigm | Part 2: Nature | Part 3: Man | Part 4: Bible | Part 5: Organic-Computational Theology | Part 6: Transcendental Ideals | Part 7: Re-Imagining Modernity | Full Research PDF
A question arises after hearing the tune of evolution and complexification play throughout nature: are the theories of organic process we walked through merely a convenient framework or does it reflect a genuine ontological structure about how the cosmos - and our minds - operate? How might we put this to another empirical test? Can we bring the modern rigor of complexity science and mathematics to the conversation to establish a formal language?
3.1 The Science of Uncertainty: The Free Energy Principle
To answer these questions, the next link in our chain of argument will come from contemporary theoretical neuroscience and its paradigm-shifting discoveries about the brain’s core functional architecture.
Over the past decade, an elegant unified theory has emerged portraying all neural dynamics as an ongoing attempt to minimize “free energy” - the gap between the brain’s predictive model of the world and the sensory data flowing in. It is called the free energy principle, developed by neuroscientist Karl Friston in 2010.
Even more broadly, the free energy principle proposes that all self-organizing systems are essentially driven by the minimization of surprise (i.e., the long-term average of prediction error). In the context of the Bayesian brain, this imperative to sustain its ordered integrity makes the brain a multi-layered prediction engine to minimize uncertainty.1
In this view, the fundamental imperative of any system is to minimize the difference between its internal model of the world and the actual state of the world in order to maintain its own structural integrity and functional coherence. One can think about this as the mechanism to maintain a pocket of “local” order within a changing environment.
This imperative manifests as a balance between two complementary processes:
1. Reducing Uncertainty: Updating the internal model to better fit and predict the observed data (i.e. Internal States - learning and adapting to the environment)
2. Increasing Determinacy: Acting on the world to make the observed data better fit the internal model (i.e. Active States - shaping the environment to match expectations)
While we won’t go into the mathematical formalisms, the free energy principle has the further merit of unifying a variety of branches of mathematics - such as Newtonian conservative systems, Lagrangian mechanics, dissipative systems, quantum systems, and Bayesian self-organizing statistics - based on various ways of playing around with the parameters.
Given this generalizability, the free energy principle can be applied across all stages of the natural process philosophy model we’ve just outlined in prior stages. This provides a single meta-model for understanding the interplay of freedom and constraint, indeterminacy and determinacy. For example:
Quantum: At the level of quantum particles, the inherent indeterminacy and probabilistic nature of the quantum realm reflects a state of maximum uncertainty and random fluctuations. In technical terms, this relates to dealing with the complex root of the Non-Equilibrium Steady State Density which plays the role of the wave function (pg 53)
Stochastic Thermodynamics: To go from one particle to a collection of particles, we can derive statistical mechanics (i.e. work, stochastic entropy) by factorizing the amplitude of random fluctuations into “mobility” and “temperature” constants with potential becoming the thermodynamic potential (pg 64)
Newtonian Mechanics: At the level of macroscopic objects, Newtonian mechanics describes the deterministic behavior of physical systems based on the laws of motion and gravitation and stable, observable phenomena. In technical terms, we derive Newtonian laws by averaging out random fluctuations and setting “Active States” to Position and “Sensory States” to Momentum” (pg 77)
Life: At the level of life, the emergence of agency and goal-directed behavior reflects a shift towards actively increasing determinacy, as living systems strive to maintain homeostasis and achieve their own ends by shaping their environment. This represents the shift from “mere” active inference to “adaptive” active inference for active particles where internal states represent external states in a probabilistic sense (pg 84)
Mind & Consciousness: At the level of mind and consciousness, the development of abstraction, counterfactual reasoning, and explicit model- building reflects a new level of freedom in reducing uncertainty, as cognitive systems learn to predict and manipulate their world through symbolic representation and communication via greater “temporal depth” (pg 102)
For a full account of the mathematical detail, please see Karl Friston’s paper here: A Free Energy Principle For a Particular Physics.
In each case, the free energy principle provides a unified account of how systems navigate the trade-offs between freedom and constraint, exploring the space of possibilities within their given constraints while also actively shaping those constraints to their own advantage. The end result is a dynamic process of self- organization and emergence, in which the minimization of free energy drives the evolution of ever more complex and adaptive forms of order out of the primordial chaos.2
3.2 Applied Free Energy Principle: The Bayesian Brain
Note that the free energy principle is not a hypothesis that can be falsified - it is simply true on mathematical grounds. In this sense, it is similar to other fundamental principles in science, such as the conservation of energy or the equivalence of mass and energy.
We can see it as a guiding principle that provides a framework for understanding how self-organizing systems behave and adapt. So while the free energy principle itself cannot be falsified, its implications and predictions can be tested by examining to see whether specific self-organizing systems behave in a manner consistent with the principle empirically.
Let’s go ahead and do that. We’ll now dive into our latest understanding of how the brain itself functions through what is called the predictive processing account of the Bayesian Brain. See below for a presentation by Samil Chandaria on the Bayesian Brain & Meditation for a succinct overview.
There are a few particular snippet from Samil’s presentation that I’d like to call out. The first is the below, which highlights the pivotal aspect of the “feature vector” or the “code” - which literally puts the code in “predictive coding”. It is the place where the sensory data vector and predicted data vector impinge on each other.
At each level of the neural hierarchy, top-down expectations about lower-level states are generated - in the green at the top as “Generative Model”. These are then compared against bottom-up sensory inputs - “Recognition Model / Feed Forward Network” on the bottom - and iteratively revised based on prediction errors. Note that this “Recognition Model” is non-linear and very difficult to learn.
Over the cumulative learning cycles throughout the hierarchal predictive processing, each post-evidence posterior output becomes instantiated as the new prior upon the sensory data received, the brain converges on a unified coherent model that minimizes uncertainty and maximizes adaptive fit.
This predictive processing architecture implies that the brain spontaneously self-organizes into multi-layered hierarchies of coherence patterns from raw sensory data, particularly through “pivot points” of feature vectors.
The idea here is to get a sense for the interplay between abstract at top of our model and concrete at bottom, the differentiation and integration as data is fed up and down the bottom, the dynamics between recognition model and generative model, and self-organized complexity through an ever more integrated model of the world.3
As an aside, the other beauty of this formalism is that by understanding the brain computationally, we can then optimize it to achieve meta-stability and overall well-being. This goes for all complex organizations across scales including companies & institutions. Engineering Eudiomonia in a true 21st century Aristotelian fashion!
3.3 Process Philosophy & Predictive Coding Isomorphism
What does this interplay between abstract and concrete - through pivot points - sound like that we find empirically in our latest understanding of the brain? What’s immediately striking is how the hierarchical structure of predictive coding, with its downward flows from abstract priors to concrete percepts and upward flows of corrective feedback, directly mirrors the patterns we outlined in our process-based natural philosophy.
In the Bayesian Brain we arguably find a concrete implementation of the universal process by which unity-in-diversity evolves - which we can call complexity, beauty or even consciousness - in the form of a more integrated generative model of the world.4
Here are a few further specific mappings:
3.3.1 Perception-action cycles as micro-instantiations of the involution- evolution arc
Most fascinatingly, the perception-action cycle, where top-down predictions guide action and bottom-up sensory feedback updates the predictive models, can potentially be seen as a micro-instantiation of the involution-evolution arc. The deployment of abstract predictive models to guide concrete action parallels involution (i.e. Generative Model), while the updating of these models based on sensory evidence mirrors evolution (i.e. Recognition Model). We can also call the Generative Model as “Lowering Meaning” [Adam] and the Recognition Model as “Raising Matter” [Eve]. Over repeated cycles, the generative model grows in sophistication, much like the progressive complexification in nature. Given this potential significance as our bridge between Nature and Man, we will explore this deeper and look to formalize the dynamics.
3.3.2 Feature Vector <> Turn
The crucial “feature vector” in predictive processing and the “turn” in process philosophy both represent a critical point where bottoms-up information collides with top-down predictions, forcing a potential revision of the prior generative model. In predictive processing, the “feature vector” encodes salient sensory data that challenges the brain’s generative model, while in process philosophy, the Stage 4 “turn” marks the transition from the descending, materializing phase to the ascending, actualizing phase. This pivotal juncture drives iterative refinement and adaptation, enabling the system to better align with the emerging reality and transcend its previous limitations.
3.3.3 Left Brain & Right Brain <> Differentiation & Integration
In the spirit of Ian McGhilichrist’s thesis from the Master & His Emissary, even the functional differences between the brain’s hemisphere the granular, analytically dissecting left-brain versus the holistic, spatially-integrated right- brain intuiting the open participatory gestalts - finds a neurocognitive analog in the polarizing interplay of multiplicity and unity. This is also the thread that cognitive scientist Brett Anderson pulls in his Substack, integrating with the work of John Vervaeke & Jordan Peterson: Relevance Realization & The Cerebral Hemispheres.
3.3.4 Nested Hierarchal Levels <> Self-Similar Process Levels
The nested hierarchical levels of the Bayesian model mirror the self-similar sub- chiasms found in the theory of process. The hierarchical dynamics of our embodied minds elegantly enact the dialectics of emanation and return, involution and evolution through the scaling of abstract and concrete up and down the hierarchical model. The self-organizing, iterative “Bayesian” flow of model refinement pursued across scales is an elegant mirror - that we can explore empirically - into the metaphysical progressions by which order, novelty, and self-transcending complexity arise.
With this convergence between the hierarchical Bayesian brain models and our holistic process ontology established, we have looked to ground our integrated metaphysics within empirically-validated mechanisms governing the generative functioning of the human mind.
Poetically, we are a microcosm of the unfolding cosmic macrocosm.
3.4 The Modern Monad: Markov Blankets and 21st Century Mathematical Metaphysics
3.4.1 Markov Blanket Review
Let’s further explore the first point that we made: perception-action cycles as micro-instantiations of the involution-evolution arc.
If this is the case, can we potentially use Markov blankets to create a mathematical formalism for these dynamics? We explored this further in our prior work - Cosmic Bayesian Inference - which provided an initial hypothesis for the application of active inference to the Genesis story to test it’s viability as a physics-based metaphysical system. We now look to fold in our theory of process framework.
More detail on reconciling Markov Blankets with their intellectual precursors (namely Plato’s Receptable, Lucretius’ Void, Leibniz Monad, Pythagorean Monad) can be found at tweet threads here and here. The rich intellectual milieu provides additional conviction on just how foundational this Markovian monism ontology potentially is - and a mathematical solution to the Cartesian dualism problem.5
To recap, at the broadest level, interactions between an agent and it’s environment can be understood as occurring through a Markov blanket, a boundary of active through perception-action loops that separate the embodied system from its wider environment while preserving its integrity and still allowing influences to flow between them. I quote from Cosmic Bayesian Inference:
Think of living systems as “bubbles of order” actively & dynamically persisting in the face of entropy, the natural tendency to disorder. This is represented by a “Markov blanket” - a statistical formalism for “identity” that partitions internal and external states. Internal order is maintained through exchanges of information between internal states, beliefs in a world model, and external states, the raw environment beyond immediate perception. This intermediation occurs via two “blanket” states: sensory states, the subset of external states that are perceived, and active states that represent actions taken to alter external states and thus sensory states.
Just like a cell membrane, it is a negotiated interface across which an autonomous region of order persists by adaptively coupling to the surrounding flux. As the system maintains its conditional independence with the environment, it iteratively updates its generative model and minimizes uncertainty to maintain internal order. This occurs through a Markov blanket which is comprised of sensory-active loops.
For more background on the technical side, see here: The Markov blankets of life: autonomy, active inference, and the free energy principle
In philosophy terms, we can think of Markov Blankets as a Leibniz monad in a Heraclitan Flux. It brings to life Whitehead’s “organic realism”, where I quote:
"In the language of physical science, the change from materialism to ‘organic realism’ - as the new outlook may be terms - is the displacement of the notion of static stuff by the notion of fluent energy [i.e. random fluctuations from External States]. Such energy has its structure of action [i.e. path integral formulations] and flow [i.e. Fokker-Plank Equations / Non-Equilibrium Steady State] and is inconceivable apart from such structure. - P&R, pg 309
[Source for Math Translations: A Free Energy for a Particular Physics]
In our daily lives, we can think of Markov blankets as ways to describe the various communities we belong to which also have “inside” vs. “outside” dynamics - whether that’s families, friend groups, work organizations, or countries.
Note that these Markov blankets can be nested to provide a ontology for well, the entire universe.
3.4.2 A Mathematical Formalism for Identity & Existence
Said simply, the power of Markov blankets is that it provides us a mathematical formalism for philosophical concepts of “Identity” and “Existence” and “Being”. This occurs via the conditionally independency of “Internal” and “External” which separate “something” from “something else” and captures a pocket of “emergent order” within a changing environment.
Identity is also closely related to “Existence” (or more fancily, “Self-Evidence”) since to maintain your Markov blanket boundary condition is to exist and death - non-existence - is simply a rupture of the boundary. Note we also get “Being” too, which pertains to the Non-Equilibrium Steady State density. Given this, we can use Markov blankets to model out the scaling of Identity we find throughout our process Arc.
We will leverage the foundational power of this mathematical “Simple Substance” to it’s fullest extent to unlock key themes in the Biblical Narrative. For now, we’ll provide two examples.
3.4.3 Math Meets Philosophy: Internal & External States as Earth & Heaven
Markov blankets provide us a critical bridge between philosophy and mathematics. To give a hint at what’s to come with this insight, what else can we potentially formulate as conditionally independent yet coupled to each other, similar to our Internal and External States?
The punchline: “Heaven” and “Earth”. Heaven and Earth are separate from each other but coupled together, similar to how the boundary of active inference separates the embodied system from its wider environment while preserving its integrity and still allowing influences to flow between them. In Pageau’s Language of Creation, man serves as mediator between Heaven and Earth, similarly to how blanket states serve as mediator between Internal and External States. This happens through perception-action loops in our Markovian ontology, or forming-filling chiasms in the Biblical narrative to use a different nomenclature.
As a sneak-peak, we already see some consilience in Markov Blanket formalism and the Biblical structure. Citing Michael Bull - whose work we’ll explore in the following section - from his work Shape of Isaiah which lays out some of the core foundations to acquire the “skeleton keys” and understand Scripture:
So what do we see here? I’ll pull out a few quotes based on certain similarity of themes:
External and Internal States: “As in human history, and indeed in every human life, the two way conversation moves from the external law on tablets of stone, like the Ten Words, to the internal government of the Spirit, a state in which one not only submits to God’s law but is also animated by it, sharing the same mind. This shared mind, as the ultimate goal, is the “fellowship of the Spirit”. All the attempts of Man to achieve unity rely ultimately upon coercion - an external conformity. But the unity of the Spirit is a response to God’s word - an internal transformation. [i.e. update and complexification of internal generative model]”
Our Chiastic Structure: Every “cycle” in the sacred text functions like an equation. God puts something in and it comes back changed. At the middle of the pattern is a “tight spot” [i.e. the turn], like the center of an hourglass, through which a person or a nation passes in order to grow into a state of greater maturity. The natural becomes more spiritual. The good becomes greater. The seed becomes a harvest. The root bears fruit. The narrow path of self-denial (a voluntary death) leads int a broad land of plenty (the promised resurrection).
To further formalize the mapping, we propose that perception-action loops become forming-filling chiasms, will be our our bridge from Man to Bible. But let us save a deeper dive of synthesizing these computational & theological perspectives for our Organic-Computational Christianity section.
Before we do that, we will first look to make our case of the bridge between Nature and Bible to establish a solid process-based foundation for ourselves, which we’ll do so in the following section by showing how a symbolic philosophy of organism exists and narrative arcs mirrors the transformation arc that takes place in our natural philosophy.
3.4.4 Math Meets Philosophy: Scaling Identity and the Death & Resurrection of Markov Blankets
There’s a final-up follow-up question here that I’d like to touch on: how do we include the flow of time and teleological pull towards self-transcending wholeness? In philosophy terms, we can also call this “Goodness” which also closely relates to “Love”.
Let’s recall our process-based metaphysics from our prior section which depicts how identity changes and scales over time, namely with identity forming at Level III.
As we’ve discussed, Markov blankets are, by definition, a formalism for Identity, separating between Internal and External States. As such, we can attempt to model this transformation process as “scaling” identity over time through Markov blankets. Let’s go back to the tangible example of our hydroid.
In these terms, the transformational process of the hydroid can thus be seen as a process of self-similar growth and transformation which breaks - death - and subsequently expands - resurrection - the identity of the initial Markov blanket to a higher integrated whole.6 This “scales up” the nested hierarchical Markov blanket resulting in further complexity.
A similar intuition for this is the metamorphosis of a Butterfly from larva to pupa - Death - to butterfly - Resurrection. We could then potentially think of Level IV as the process that occurs “inside” the pupa to self-organize into a butterfly.
This abstraction becomes potentially powerful as it allows us to interpret this evolutionary cycle of complexification throughout the process using the full power of mathematics - which we will save as potential follow-up research.
A potential initial interpretation of this transformation process to higher levels of self-transcending wholeness in terms of Markov blankets:
Initial Markov blanket [Starts as One Cell]: A single, self-contained system with minimal internal complexity, representing a unique perspective or expression of the universe. For a plant analogy, a seed.
Markov blanket breaks [Becomes Multicellular]: Here the Markov blanket breaks, leading to an increase in chaos and free energy. In our butterfly terms we can think of this as becoming a pupa. This phase is a "transition" process where the uptick in free energy will be leveraged to drive increasing complexity. For a plant analogy, this can be seen as starting to growing roots.
Defined and stable Markov blanket [Acquires a Shape]: Here the chaos collects itself with increased internal complexity and organization, allowing for consolidation and refinement of internal processes. This can be considered the “self-organization” phase. For a plant analogy, this pertains to grow a larger root system.
Markov blanket transition [Fastens to Ocean Floor]: This stage represents the “Turn” - max compression and diversity-in-unity in the process of altering its identity, relationship with the environment, and future trajectory, ultimately enabling novelty. This can be considered a "compression" and “phase-transition” phase. A seed popping out of the dirt, if you will.
Nested Markov blankets expansion [Grows in Plant Like Fashion]: Here we have the fruits of the novelty. Following the phase transition, a growth transition takes place of fractal self-similarity where where creative potential is realized in the previous stage manifests as more complex, multi-scale structures. For a plant analogy, trunk and branch growth.
Breaking and reforming of Markov blankets [Flower Breaks Off Into Mobile Jellyfish]: Here we have Monad differentiation & individuation - an emergence of a new Monad and development of independence. This detachment allows for diversification and expansion of identity and exploration of new niches or adaptive landscapes. This drives diversification and complexity. For a plant analogy, branches sprout leaves and flowers.
Higher-level Markov blanket emergence [Fertilization]: The combination of elements from separate systems, involving the merging or synthesis of distinct monads to form a new, higher-level monad, completing the cycle and self-instantiating a new process of Markov blanket expansion and identity formation. In animal terms, sperm and egg; in plant terms, seed from flower going into the dirt to start the process again.
3.4 Human Psychology: Integrating Jungian Psychology with our Perception-Action Loops
Let’s refine the mappings between the theory of process and the action-perception loops but now shift our focus to their relevance to human psychology to get a felt-sense of this experience in our daily lives and make the connection to our process arc more established.
As we can see above, a full perception-action loop (Top) maps isomorphically to each step of our Arc (Bottom). Let’s break out this “cycle of action,” where the “Turn” becomes the point of “Conscious Action”:
Unconscious action [Level I]: This stage involves actions that are not yet incorporated into the generative model. These actions are spontaneous, instinctual, or habitual, occurring without conscious awareness or deliberation. In terms of the Markov blanket, these actions can be seen as the system’s initial interactions with its environment, prior to the formation of a stable boundary.
Unconscious perception [Level II]: At this stage, sensory information is received but not yet consciously processed or integrated into the generative model. This raw sensory data is the system’s first encounter with the environment’s feedback to its actions. In the Markov blanket formalism, this represents the initial ow of information across the blanket boundary, before it is actively interpreted or modeled.
Conscious perception [Level III]: Here, the sensory information is consciously processed and integrated into the generative model. This is where the system starts to build an internal representation of its environment and its interactions, updating its beliefs and predictions based on the feedback received. In the Markov blanket framework, this corresponds to the active inference process, where the system refines its model to minimize surprise and maintain its integrity.
Conscious action [Level IV]: At this critical juncture, “The Turn”, the system uses its updated generative model to guide its actions consciously. Having learned from its previous interactions and built a more accurate internal representation, it can now proactively influence its environment to achieve desired outcomes. This is the point where the system “uses” the learned laws of cause and effect to effect change. In the Markov blanket formalism, this represents the system’s ability to maintain its conditional independence by actively shaping its interactions with the environment.
The clockwise return in this second half of the cycle represents the system’s ability to understand and manipulate its environment based on its learned models. It represents the shift to Conscious Evolution. We flip to “using the law” - deploying our updated model to guide our actions and shape our environment. This is a shift from modeling the world to actively intervening in it. It’s the point where our understanding becomes practical know-how, where our insights gain traction in the real world. We can view this as the process in which consciousness - active awareness - expands via a more complex (i.e. diversity-in-unity) integrated model of the world.
To give an felt-sense of this process in modern psychology terms, these steps can be mapped to Jungian psychology:
Intuition (Level I - Point - Fire - Final Cause): Intuition represents the realm of pure potentiality, unconscious impulses, and instinctive action. It provides the source of meaning and direction for the psyche.
Emotion (Level II - Line - Water - Material Cause): Emotion represents the realm of subjective, unconscious perception and the flow of psychic energy. It provides the raw material of our psychological experience.
Intellect (Level III - Plane - Air - Formal Cause): Intellect represents the realm of conscious thought, logic, and rational understanding. It provides the mental structures and forms that shape our conscious experience.
Sensation (Level IV - Solid - Earth - Efficient Cause): Sensation represents the realm of concrete, conscious perception and the direct apprehension of the physical world. It represents the actual manifestation of our psychological processes in the world of matter.
This mapping suggests that “action” at the Turn - hence the name “active” inference - is indeed the fundamental unit of human psychology. Our actions shape our perceptions which condition further actions in an ongoing & self-referential loop. In a meta way, by bringing conscious awareness to this process, we can gain increasing control over the cycle, reprogramming ourselves in alignment with our highest aspirations.
We will review the implications of the self-referential loop (i.e. Level VII becoming Level I, post-evidence posteriors becoming the new priors, Recognition Model becoming the new Generative Model) in more detail in our Organic-Computational Christianity section. As a hint - we hypothesize this self-reference has to do with the nature of “consciousness” itself.
Note the focus on action has an Aristotelian ethics ring to it too (i.e.“we are what we repeatedly do”). One could argue, to tie our ancient philosophical enterprise with modern Markov blankets - that Aristotle’s focus on immanent realism and how to appropriately act and live in Nicomachean Ethics (horizontal in School of Athens which mirrors the horizontal part of the Cross) reflects the focus on Active State-Internal loops while Plato’s focus on transcendent idealism in Timaeus (vertical which mirrors the vertical part of the Cross - top “Heaven” and bottom “Earth”) reflects the focus of External States-Sensory loops.
3.6 Conclusion: On The Free Energy Principle, Bayesian Brain, and Markov Blankets
In this section, we have discovered parallels between the patterns of involution and evolution in nature and the latest theories of brain function and cognition. The free energy principle and the predictive processing account of the Bayesian brain reveal how the mind’s hierarchical architecture around pivotal feature vectors mirrors the nested & self-similar structure of unfolding nature centered around a pivotal turn.
Perception-action cycles at the atomic level can be seen as micro-scale enactments of the involution- evolution arc, with Recognition Model-Generative Model at the model level. Taking an abstract step back to the level of physics-based metaphysics, Markov blankets provide a powerful mathematical formalism for understanding scale-invariant formation and transformation of systemic boundaries and identities. Applying these insights to human psychology, we can map the learning cycle onto the four stages of process philosophy and the archetypal functions of Jungian psychology to bridge to our modern nomenclature. Similar to how nature complexifies over time through an increase in unity-in-diversity, so too does humanity via our internal generative model of the world.
This integrated framework suggests that action - similar to the “turn” in process philosophy and the “center of the chiasm” in the Biblical narrative - is the fundamental unit of the psyche, shaping perception which in turn guides further action in a self-referential spiral of increasing complexity and consciousness. By bringing awareness to this process, we can participate more creatively in the cosmic dance of involution and evolution and reimagine our role in the grand adventure of cosmogenesis.
Next Section: UPTM #4 | A Deep Coherence: Organic Process and the Biblical Meta-Narrative
This research is preliminary and we're publishing this in the spirit of building in public. Feedback, open dialogue, and collaboration is very much encouraged! I will most certainly be wrong in places as I have developed these ideas rather independently. If you are exploring similar ideas, please reach out or expect at a DM as I plan to connect with scholars and folks more knowledgeable & experienced then myself to continue to refine & better articulate the thesis. Please also feel free to follow along or get in touch at @tomer_solomon or by subscribing to my Substack!
Follow-Ups: Can we use the free energy principle to provide us insight in the evolution of process per our natural philosophy? How does the evolution of random fluctuations of external states through increasing scales effect internal states? In free energy terms, what happens at the turn and update of the generative model?
See here for a more research focused overview on the Free Energy Principle for a summary.
Uncertainty also is a unifying theme in quantum mechanics & general relativity, and while out of scope for this exercise, physicist Arthur Eddington denoted uncertainty as a common denominator to assist in bridging the two fields where the curvature in relativity is the same as the uncertainty in quantum theory.
We can think of this as the physics / statistics of thermodynamics applied to the physics of sentience & beliefs.
This would be from the work like Mark Miller on Predictive Dynamics of Happiness & Wellbeing.
To tie in one of the latest branches of consciousness research, “Integrated Information Theory”, this specifically would be the level of “integrated information” which we can roughly equate to complexity.
Pertinent research paper here: Sentience and the Origins of Consciousness: From Cartesian Duality to Markovian Monism.
Markov blankets are especially powerful & versatile. Below are a few examples:
Liebniz: Markov blankets can be seen as the modern “Leibniz monad” but with mathematical might and a mental (perception) <> physical (action) duality similar to what Whitehead intuited.
Charles Taylor: As a commentary on sociology, to answer Charles Taylor’s musings on modernity in one line: we can argue that the modern enterprise as replaced the “External State” with “The Internet” instead of “God”. One slight issue: only one of them is imbued with transcendental ideals.
Pythagoras: A potential though experiment to tie it to the OG Monad - the Pythagorean Monad. While Internal <> External States are intuitive in relation to our everyday perception as embodied agents in an environment , External States refer to everything external. Could we then reframe External States as having “0 dimensions”? With this, External States become an “Invisible Dot” in the center of the Internal State. So more of a “foreground / background” vs. “co-planar” dynamic. This then potentially echoes the “a circle with center everywhere and circumference nowhere” aphorism - an infinite Markov blanket, perhaps?
David Bohm: We can also tie this Markov Blanket construct to physicists’ David Bohm’s concepts of “Implicate” (Internal) and “Explicate” (External) Order.
Rene Girard: We may also be able to make more rigorous Girard’s “Internal” and “External” mediation.
Plato: In the first part of Timeaus, Socrates recounts to Timaeus on the composition of the state. At the beginning he alludes to the role of warriors maintaining city walls: “we spoke of those who were intended to be our warriors, and said that they were to be guardians of the city against attacks from within as well as from without, and to have no other employment; they were to be merciful in judging their subjects, of whom they were by nature friends, but fierce to their enemies, when they came across them in a battle”. This broadly interpreted can be an allusion to maintenance of a Markov boundary.
To make the concept of death and resurrection less daunting - this is also what occurs on a micro-scale when we go to sleep - which can be also seen as a “death” and mini-loss of body - and subsequently wake-up - “resurrection” and re-gain of bodily function.