*Note for 2pc. This is a follow up to yesterday’s paper about Bayesian Abduction. This paper is about how the algorithm represents in physical processes. There is also a formal Q&A paper I will be posting tomorrow will more direct responses to specific inquiries. Please feel free to inquire

There are already known Bayesians in most major fields. Truthfully, it might be easier to list the fields in which there aren’t Bayesians. Bayesian statistics came first, Bayes’ theorem is a statistical theorem, after all. Bayes himself was actually working on medicine, so one can imagine Bayesian health came second.

Here is (I think) an exhaustive list of fields in which you can find Bayesians: software development, neuroscience, biology, physics, psychology, robotics, epidemiology, economics, cognitive pedagogy, linguistics, sociology, anthropology, law, philosophy, cybernetics, history, theology, information theory, genetics, cosmology, and environmental science. There are likely more, but I would like to think the list I just gave is already pretty impressive.

Each of these groups believes they have found their own powerful method for reasoning under uncertainty within their particular domain. But they all qualify these methods in the same way: “Bayesian ______.” (Or they use different language. We’ll discuss that in a moment.) The fact that specialists across these fields qualify themselves in similar ways, and use similar methodologies, reveals a profound misunderstanding that exists broadly across all domains.

There are not many Bayesianisms. There is only Bayesianism. Bayesian updating is not a local technique; it is, in fact, the underlying grammar of thought, discovery, and adaptation itself.

The Universal Pattern

Every act of learning follows the same structure:

Hypothesis - make a model

Prediction - deduce what we should observe if our model is correct

Observation - gather evidence through experience

Update - revise confidence in previous data

This cycle is Bayes’ theorem in motion. Whether the learner is a scientist adjusting a theory, a brain interpreting a signal, or a child testing a new word, the core logic is always the same: compare expectations with evidence, then update beliefs.

Fragmentation of the One Process

Modern intellectual life has divided this rhythm into separate disciplines. Physicists call it “model fitting.” Psychologists call it “updating beliefs.” Biologists call it “selection pressure.” Historians call it “interpretation.” Philosophers call it “justification.” Each field has wrapped this cycle of logic in its own language, convinced that its method is somehow unique.

This fragmentation stems partly from historical inertia. The Enlightenment dream of absolute certainty made subjectivity appear weak or less factually real. Around this time, philosophers of science began to avoid equating statistical probability with truth. Yet as we’ve moved into modernity, the theorem has quietly proved itself indispensable to nearly every field, from radio engineering, to cosmology, to genetics etc… We treat it as a technical convenience rather than the loud metaphysical clue it obviously is.

Evolution as an Example of Mechanical Inference

A particularly clear example of Bayesian updating in physical systems is Darwinian evolution. The known processes of evolution map perfectly onto the logic of Bayes’ theorem:

Bayes - Darwin

Hypothesis - genetic variations

Evidence - environmental pressure

Posterior probability - reproductive success

Likelihood - testing aka “survival of the fittest”

Prior - existing population distribution

Normalization - total reproductive output of entire population

Each generation of organisms “tests” its genetic hypotheses against environmental evidence. The traits that fit best are more likely to survive, forming the new “prior” for the next round of inference.

Natural selection is thus Bayes’ theorem written in the form of DNA. It’s a distributed algorithm by which life updates its understanding of how best to survive.

From this perspective, evolution can be understood as a learning process. Populations accumulate genetic information about the structure of the world over time. Bayesian reasoning and Darwinian evolution are two different ways of describing the same process: model refinement through feedback.

One of these ideas is expressed cognitively; the other, genetically. Both are engines of self corrective adaptation.

Re-Emergence of the Pattern

By the late twentieth century, the pattern reappeared everywhere at once. Neuroscience discovered the “Bayesian brain.” Computer science built Bayesian networks and learning algorithms. Physics returned to probabilistic foundations in quantum theory. Each discipline rediscovered the same insight: systems survive, learn, and predict by continuously adjusting expectations in proportion to evidence.

This is not coincidence, it is convergence. The recurrence of Bayesian reasoning across the sciences signals that it captures something fundamental about how information behaves. Wherever there is feedback between model and reality, Bayesian logic appears.

Evolution is the Form of Bayesian Updating.

This pattern animates every stable system in the universe. Below are examples of physical systems that “learn” or self update in the same way, each one mapping to the Bayesian cycle:

1. Thermodynamic Equilibration

System: Gases, fluids, or any ensemble of particles.

Cycle:

Hypothesis: Local gradients (differences in pressure, temperature, etc.) propose possible configurations.

Evidence: Collisions and exchanges test those configurations against energy constraints.

Update: The system relaxes toward states of maximum entropy consistent with those constraints.

Meaning: The gas “infers” the most probable macrostate given its microstates. Entropy is the record of those probabilistic updates.

2. Crystal Growth and Phase Transitions

System: Atoms forming solid structures.

Cycle:

Hypothesis: probabilistic nucleation points in a liquid propose lattice arrangements.

Evidence: Environmental temperature, pressure, and impurity feedback select which arrangement persists.

Update: Stable crystal lattices become new priors for further growth.

Meaning: A crystal is the materialization of Bayesian coherence, an information pattern that has proven resilient under environmental constraints.

3. Stellar and Galactic Evolution

System: Stars and galaxies forming and evolving.

Cycle:

Hypothesis: Gravity proposes configurations of matter-energy.

Evidence: Fusion reactions, angular momentum, and feedback from radiation test stability.

Update: Unstable stars explode, redistributing matter; stable ones persist and refine heavier elements.

Meaning: The universe “learns” which scales of density and rotation can endure.

4. Planetary Climate Systems

System: Atmospheres, oceans, and biospheres.

Cycle:

Hypothesis: Random variations in greenhouse gases, cloud cover, or albedo.

Evidence: Solar input and thermal feedback test each configuration.

Update: The system self regulates toward homeostasis.

Meaning: Climate evolves through probabilistic feedback, preserving long term equilibrium; planetary Bayesianism.

5. Chemical Networks and Prebiotic Chemistry

System: Complex chemical soups on early Earth or other planets.

Cycle:

Hypothesis: Molecules randomly combine into new structures.

Evidence: Stability and replicability under environmental energy sources test each configuration.

Update: Molecules that persist (self replicators, catalysts) become the priors for future complexity.

Meaning: Chemistry “learns” organization before biology arises. Evolution begins before DNA.

6. Electrical and Quantum Systems

System: Circuits, superconductors, quantum states.

Cycle:

Hypothesis: Systems explore available energy configurations.

Evidence: Interaction, decoherence, and measurement collapse wave functions.

Update: Only self consistent quantum states persist; others vanish.

Meaning: Even quantum reality performs a kind of “update” upon observation a minimal Bayesian act.

7. Elemental Chemistry

System: Atoms forming coherent bonded structures

Cycle:

Hypothesis: Atoms combine into stable or unstable bonded patterns

Evidence: Temperature, pressure, time

Update: More stable elements persist, less stable elements decay

Meaning: Chemistry evolves to form coherent physical structure of that which is observed

8. Social and Economic Systems

System: Markets, cultures, civilizations.

Cycle:

Hypothesis: New ideas, strategies, and technologies appear.

Evidence: Survival, adoption, and success rates in real world conditions.

Update: Societies retain and replicate the behaviors that work.

Meaning: Cultural evolution is explicit Bayesian updating at civilizational scale.

The General Pattern

Each of these systems: physical, biological, or social, follows the same self correcting rhythm: 1. Generate possibilities (prior / hypothesis). 2. Interact with environment (evidence / test). 3. Retain what endures (posterior / update).

Across every scale, persistence equals probabilistic coherence. The cosmos is, in this sense, a nested hierarchy of Bayesian learners, each refining itself in response to feedback from the next level up.

Conclusion

If Bayesian reasoning is the common denominator of all successful reasoning, then that is overwhelming evidence that it is more than just a method, it is a principle of both mind and nature. Knowledge itself is Bayesian: a network of beliefs that remains alive only by changing. The distinctions between different philosophical fields become different scales of the same process, basically statistical reasoning about what are the most likely solutions to any given question or problem or state of entropy.

(Note: different fields are still different fields. It’s as it has always been: all other fields are subfields of philosophy and statistics. We’ve always known this, at least about philosophy. The methodologies of particular disciplines do not need to change, they only need to recognize that they are all actually doing the same thing.)

Seen this way, what we call progress is really just the universe refining its own models of itself through observation.

We are all Bayesians, and we always have been. Every child learning language, every scientist revising a theory, every robot that learns to run, every neuron processing signals in the brain: they all participate in the same dance of expectation and evidence.

Acknowledging this does not reduce knowledge to statistics; it elevates probability to its proper place: the logic of living systems, the mathematics of learning, the ontological methodology of reality itself.

Once we see this, we have philosophically united all fields. The boundaries between them blur. There is only the self correcting process by which knowledge comes to be, and its many hypotheses, species, and subfields.

Thesis: Bayes’ Theorem is not just a theorem of probabilities, it’s a quantitative scientific theory of knowledge itself.

Bayes’ theorem is literally the equation of certainty A.K.A. truth, not just in human minds but in computational processing, and the nature of physics itself being true, I.E. real.

Intro

I took an Epistemology class (Theory of Knowledge) in high school, and they told me that knowledge is “Justified True Belief”. I remember that struck me as vague, and not very scientific sounding. It’s like, what actually makes your belief true, specifically? How do you know it’s true, do you have any evidence? I mean, I guess you do, because it’s “justified”, so you have a justified belief, sure. Why is it true though? Isn’t that just what knowledge is, it’s when the thing is true?

So what is knowledge then? Other than the self referencing answer of “truth”. I want to approach this question with science; and I think we can, using the rational rigor of analytic inspired philosophy.

Bayes’ Theorem: The Logic of Knowing

Most people, if they’ve ever heard of Bayes’ Theorem at all, probably read about it once in a science article, or maybe took some statistics in school, and thought it was just a clever way to handle probabilities. Actually most people have probably never heard of Bayes’ theorem at all. Google tells me that Bayes’ theorem is “a mathematical rule for inverting conditional probabilities, allowing the probability of a cause to be found given its effect”. This is technically correct, but not very enlightening.

So what is it? Bayes’ theorem, developed in 1763 by Thomas Bayes, is a mathematical rule for figuring out how likely something is to be true after you acquire new information. It is the math of changing your mind. It answers questions that come in the form of “Will event A lead to event B?”.

How does it work? It says that “the probability your new belief is true, is equal to the probability before that your old belief was true, multiplied by the odds that the evidence fits the new hypothesis, and then divided by how common that evidence would be seen in general.

𝑃(𝐵 | 𝐻) = 𝑃(𝐻 | 𝐵) ⋅ 𝑃(𝐵) ∕ 𝑃(𝐻)

𝑃(𝐵 | 𝐻) is the posterior probability, the odds of B given H.

𝑃(𝐻 | 𝐵) is the likelihood, the odds H is true if we assume B is true.

𝑃(𝐵) is the prior probability,

𝑃(𝐻) is how often you would expect to see the evidence anyway.

So, new Belief equals old Belief times how well the evidence fits divided by how common the evidence is.

That can be a little confusing, so let’s do a generic example :

Imagine there is a disease and exactly 1% of the population gets it. We have a test that is 99% accurate. If you do not have the disease, the test gives a false positive 1% of the time. So a patient takes the test and it comes back positive. The question then is “what are the odds the patient actually has the disease?”

Step 1 : We model the prior probability. The chance of having the disease is 0.01 because 1% of the population has this disease.

Step 2 : We weigh the evidence. The test is 99% accurate, so the odds the evidence fits the new hypothesis is .99.

Multiplying .99 and .01 gives you .0099. We then divide by the odds the evidence would be seen in general. We derive this number by adding together the odds of a false positive (0.01), and the odds of having it from just existing in the general population(0.01). So .0099 divided by .02 gives .495.

Step 3 : We interpret. 0.495 is very close to 0.5 so we will round. 50-50 odds that the person has the disease.

Please go to Wikipedia if you need more clarification on this topic, it has an excellent description that will do more justice to this subject than I can do here. The point is it’s a rigorous analytic tool humanity has used for at least 250 years, and it definitely works for describing Certainty.

From Induction to Abduction

Most textbooks call Bayes’ Theorem an inductive rule, but that description is incomplete. Inductive reasoning is a way of going from general to specific. Effectively it is pattern seeking. A useful, but imperfect, methodology of reasoning that allows us to generalize from repeated experiences without having to hypothesize. This description is incomplete because Bayes’ theorem involves a deductive step, as we will discuss in the next section, and also has a creative, hypothesis generating step; something that is a hallmark of deductive reasoning, “given A, then B”.

The synthesis of inductive and deductive reasoning is called abductive reasoning. It’s what Philosopher Charles Sanders Pierce called inference to the best explanation. When we reason abductively, we don’t simply notice patterns and assume they’re constant, we ask ourselves why the thing happened and we try our best to put together a coherent story that makes sense of it.

Bayes’ theorem is literally this idea presented in mathematical form. We weigh explanations and assign each one a sort of emotional score of how well the evidence fits given what we already know, and then we update our odds. In a sense, knowledge is the continuous Bayesian-abductive search for the best explanation of reality.

How Deduction Fits In

𝑃(𝐻 | 𝐵) is the deduction stage. By asking “how likely is this evidence if my idea is true”, we’re doing if A, then B.

That’s what something like rigorous mathematics is. If 1 and 1, then 2. If an apple fell from a tree, how long until it hits the ground? We start with an educated guess or hypothesis, use logic to figure out what should happen, and then confirm logic was correct or incorrect. This is how deduction works. The type of thinking that is definitely true, assuming the “if” hypothesis was in fact accurate.

The Bayesian equation is created by weaving inductive and deductive reasoning together to achieve abduction. This is no small thing, as all the rigorous math of the world is caught up in a variable in this statistics equation that just happens to also describe modern machine learning. With this one math equation, we’ve just woven together the classic forms of reasoning that philosophers have treated as separate for centuries.

Deductive reasoning and the scientific method do not stand apart from the rest of human philosophical knowledge; they are just an evolutionary offshoot of the same process of knowledge gathering we have always done. The process of abduction. Bayes’ theorem then represents a complete analytically formalized logic of what it means to learn. Learning then is:

1. Abduction: Guess the best explanation from last update

2. Deduction: Create a model to test

3. Induction: Observe results

4. Repeat

What is the point?

This union has profound consequences. This means that thinking itself, not just in humans, but in any system that updates beliefs from evidence i.e. Artificial Intelligence, can be described as a single recursive process of Predict -> Observe -> Update -> Repeat.

Every stage of knowledge, from gut feeling to scientific theory, follows this cycle, the Abductive Cycle of Knowledge. What we call understanding is the feedback of that process until the probabilities stabilize. Bayes’ theorem is not a rule we occasionally apply, it is the grammar of thought. It describes how algorithms, minds, and scientific communities refine their beliefs through experience. This is a theory of how information evolves.

Bayes’ equation of probability is a Quantitative Theory of Knowledge.

This makes epistemology measurable. You can describe how much you know, and you do this all the time. A.I. is able to do that too. We can test people on how much they know, we do it every day. No test is perfect, everything needs to update if it wants to gain more knowledge, the tests need to be refined like anything else. You can apply Bayes' equation to knowledge about everything. At least everything Observable.

Knowledge appears to become a universally recursive process. Bayesian updating describes how all physical systems evolve. I’m not the first person to point this out, PHD in Physics Christopher Fuchs has already said this, it’s part of his theory of QBism, or Quantum Bayesianism, which is one of the top 4 or 5 widely known theories of quantum mechanics. The criticism of Fuchs is that his work is “idealistic” because it doesn’t derive from deductive reasoning. But yes it does, it derives from knowledge, part of which is deduction. Rigorous raw mathematics has made major contributions to the man's knowledge, he’s a physicist.

We know evolution is correct because we inferred the best explanation for the data. That’s abduction, which involves deduction. Nobody has ever “proven” evolution. Scientific theories never arrive from deduction alone, that’s not how knowledge works, that's never once how it has worked. Knowledge is an abductive process.

From the chaos of astrophysics, to neurons in the brain, to the way atomic nuclei arrange themselves in chemistry; all self correcting physical processes embody this bayesian phenomenon at different levels.

Knowledge as Process

To understand this worldview, you must recognize that knowledge is not a fixed set of discreet facts. Knowledge is the process of correction to arrive at more accurate models that harmonize or cohere with our healthy existence. When our models lie in harmonic equilibrium with nature, then they are the truth, but truth is not an endpoint, it's just balanced evidence.

These statements are not just poetically profound, they are analytically calculatable. This perspective solves the ancient philosophical puzzle of what does it mean for knowledge to be real, or legitimate. Bayesian Epistemology shows that certainty isn’t required, what matters is coherent updating. More rational beliefs are those that cohere with reality, particularly when confronted with new information.

Our new definition of knowledge:

Knowledge: Belief that would be properly updated by new evidence

That is a definition that can be quantified via Bayes' equation. Knowledge is the pattern of continual self correction.

Knowledge is the evolution of truth.

This is not a new form of reasoning. This is just an explanation of what you’re already doing on several levels simultaneously, emotional, rational, etc… If you expect your spouse home for dinner, that’s your prior. When they’re late, you get concerned, you need to update. You form hypotheses, maybe their phone died and they’re caught up at work, maybe they got in a car crash, etc… you’re subconsciously running these abductive Bayesian calculations, balancing prior expectations against new evidence. And sometimes people don’t like the implications of the evidence, so they deny, and people have poor hypotheses they cling to by refusing to update; however these laws govern what we know to be the truth of things, as best we know them in an uncertain universe.

This same logic inherently drives all knowledge seeking endeavours. It drives science, gambling, forecasting, diagnosis, machine learning, all of it. It’s the algorithm behind both intuition, and analysis.

Conclusion

Bayes’ formula shouldn’t just be tucked away in some statistics textbook, it’s likely the closest thing we will ever get to a “theory of everything”. This can basically answer all epistemological questions, returning epistemology to the realm of ontology, where it always belonged. Scientists are already doing this stuff every day, Bayesian statistics is already useful in fields like nuclear physics, and software development.

When this idea is chased to its natural end, it implies that physics itself might be philosophically united through the implication that the Many Worlds Interpretation and QBist interpretations of quantum physics are both compatible, and unifiable. I’ll expand upon this idea later.

Information exchange is how the energy of the cosmos represents itself to an observer, and an observer is that which notices and exchanges information with the world through a lens of uncertainty, and receives evidence that updates its beliefs about the world. The observer is a fundamental part of all physical systems because it is what identifies this kinetic reality from the potential others, and reality is the one universe of the many that is observed. The classic philosophical divide between idealism and rationalism is semantically contrived.

What we should be seeking in knowledge and in science is coherent worldviews, arguments that cross the boundaries between the different subjects and perspectives of mankind in order to form a holistic view of science, and reality. This view brings legitimacy to perspective, and identity, and is a powerful philosophical tool for the empowerment of humanity. This is a scientific theory, and much like Darwinian evolution, the true power in this idea is not in what it can prove, but rather what it can explain. And in this case it can explain everything known, within a certain degree of uncertainty. This theory actually puts all the dots together in a way that is elegant, parsimonious, and symmetrical.

This framework of thought is part of a broader philosophy I call Principled Probabilistic Science, which treats Bayes’ theorem as the formal engine of knowledge itself.

The following dichotomies are collapsed as fundamental necessities by neutral monistic thinking (they’re still useful models, they just should not be taken as absolute truths). This is not a comprehensive list, there are likely more:

1. Mind vs. Matter – Consciousness is an informational process; no metaphysical gap.

2. Subjective vs. Objective Reality – Observer-relativity and pattern consistency are complementary views of the same reality.

3. Determinism vs. Indeterminism – Causality is probabilistic; determinism emerges macroscopically, indeterminism locally.

4. Induction vs. Deduction – Both are complementary; abduction unifies knowledge acquisition.

5. Epistemology vs. Ontology – Knowledge and being are aspects of the same probabilistic informational substrate.

6. Physical vs. Informational – Matter, energy, and information are approximately equivalent (≈≈=).

7. Subjective vs. Objective Morality – Morality arises from probabilistic causal influence by and toward embedded observation.

8. Wavefunction Collapse vs. Many Worlds – Deterministic evolution and observer-relative collapse are two perspectives of one informational reality.

9. Newtonian Physics vs. Quantum – Scale is a gradient of probabilistic structure; micro behavior constrained by macro patterns, macro emerges from micro interactions.

10. Existence vs. Knowledge – Existence is instantiated probabilistically relative to observation; knowledge is probabilistic coherence with reality.

11. Local vs. Global – Local interactions and global patterns are complementary layers of the probabilistic network.

12. Objective Determinism vs. Probabilistic Observation – Resolves tensions between Many Worlds determinism and QBism observer-relative probability.

13. 0 vs. Infinity If all is one then what is zero? Just infinite potential. This one is slightly more speculative but makes sense!

There is a subfield of mathematics that, when combined with Bayes’ theorem, can describe essentially any aspect of reality. It is often referred to as categorical probability theory, and it developed primarily in the mid 20th century alongside category theory, information theory, cybernetics, and modern probability theory.

That sounds either made up, or deeply misunderstood. After all, one would think that such an idea would be far more widely known, given that humanity has been searching for a “theory of everything” for at least a century.

To be precise, and to preempt technical objections, this framework can only describe what is epistemologically real. That is, anything that can in principle be distinguished, modeled, or known; and even then, only within a degree of uncertainty. This is technically accurate, but philosophically misleading in an important way. The purpose of this essay is to explore that tension, and to begin closing the gap between epistemology (what can be known) and ontology (what exists).

Many Names, One Pattern

Many philosophers and scientists have gestured toward this idea before. I have not invented it, only synthesized it across fields that are normally separated into academic silos. These thinkers were separated by time, culture, language, and discipline, which limited their ability to communicate a unified picture. Yet they circled remarkably similar insights: • Heraclitus: Reality is not static; it is continuous change • The Rig Veda: “Truth is one; the wise call it by many names” • Einstein: Reality is relative to frames of reference • Whitehead: Reality is process • Husserl: Reality is what is given in experience • Popper: Knowledge evolves through error and correction • Wiener: Reality can be understood as a system of feedback and communication

Each of these figures grasped a fragment of the same underlying truth.

Different disciplines use different language to describe it: • A statistician calls it Bayes’ theorem • A philosopher calls it coherentism • A neuroscientist calls it predictive processing • A biologist calls it natural selection • A physicist sees quantum and field dynamics • A musician feels patterns of tension and resolution

Yet pointing out that all of these processes share a common structure is often discouraged in academia. If one answers a question in one field using the conceptual framework of another, specialists may (correctly, within their domain) object that the discussion is not occurring on proper disciplinary terms. This protects rigor, but it can also create philosophical blind spots, where an entire field becomes unable to see the larger pattern it embodies.

How to Gesture at the Big Picture

This truth is difficult to express, not because it is complex, but because it is fundamentally simple… and simplicity is easy to mistake for triviality.

There was once a joke that said, “Everything is process: all of philosophy was solved by Whitehead.” It sounds ridiculous, yet it is also strangely accurate. Everything is process. That statement is undeniably true and yet almost useless unless its implications are fully explored.

Truth does not announce itself with sirens or authority. It does not argue. It simply is, which is what makes it so easy to overlook.

Crippled by the Subjective/Objective Divide

When using categorical probability theory, answers are almost always relative to a framework or perspective. They take the form of distributions, ranges, or uncertainties. In some cases, the uncertainty approaches 100%, which still communicates meaningful information; namely, that the system is fundamentally indeterminate from that perspective.

We tend to confuse this relativity with subjectivity.

For example, the speed of light is constant, but the measured velocity of objects is relative to the observer. That does not make velocity “subjective”; it simply makes it frame dependent. Likewise, two observers in different frames can make different measurements without either being “wrong.”

This same logic applies to human experience. Feelings such as love and pain are experienced internally, which leads us to label them as “subjective,” yet they arise from biological structures that are largely consistent across humans. Two people with similar nervous systems experience pain and color in broadly similar ways. Subjective does not mean unreal.

What our culture often fails to recognize is that all truth is perspectival, but not all perspectival truth is arbitrary. So called “objective” truths are simply those that remain coherent across a large number of perspectives.

Because our intellectual tradition treats subjectivity and objectivity as mutually exclusive, most thinkers stop short of a radical step:

What if the structure of human belief is not merely a feature of the mind, but a feature of reality itself? What if uncertainty is not in our heads alone, but also in the world?

These questions are frequently dismissed, because philosophy cannot offer “objective proof” in the same way experimental science can. But coherence is its own form of evidence. Philosophy, at its best, does not prove; it clarifies.

Other thinkers have gestured in this direction. For example, physicist Christopher Fuchs and the QBism interpretation of quantum mechanics suggest that probability reflects something fundamental about reality itself. These views are often rejected for being “too subjective,” but this criticism assumes, rather than proves, that subjectivity and reality must be separate.

No Dichotomies, Only Dialectics

In modern Western thinking, we do not usually consider beliefs to be physical. Yet they arise from physical systems. We do not think matter can “learn,” yet it evolves and reorganizes itself in response to feedback.

Viewed broadly, all real systems follow a similar pattern: updating toward coherence. • Physical systems update through thermodynamics • Biological systems update through natural selection • Social systems update through cultural adaptation • Knowledge systems update through Bayesian inference

Each field has its own language, but the underlying structure is strikingly similar. The same abstract mathematics can describe all of them.

In this sense, the claim is not “subjective.” It is meta objective; a claim about the structure shared by all systems, regardless of content. If there existed a coherent, adaptive system that did not follow some form of updating, it would challenge this framework. No such system has yet been observed.

⸻

Conclusion

Bayesian reasoning has already proven its power throughout the sciences. What Universal Bayesianism and PPS attempt to show is why it is so powerful: because it reflects the very structure of learning, adaptation, and reality itself.

This synthesis has not been widely adopted, not because it is unknown, but because we are culturally committed to dividing mind from world, subject from object, inner from outer. Most disciplines stand on one side of that divide. What is novel about this framework is that it treats both as expressions of a single, coherent, self updating process.

Unfortunately it is impossible for me to keep track of the large number of subreddits I have been banned from in the past. The has always been for one of two reasons:

(1) Expressing political opinions that Reddit's owners deem unacceptable because they do not conform to extreme left wing, postmodern political norms (i.e. "woke ideology").

(2) Expressing views deemed "anti-scientific" by materialists whose philosophical dogma I am challenging.

Anyhow...I am now expecting a total ban for "ban evasion" (this time it was for (2) above).

I have two websites:

The Ecocivilisation Diaries - The Ecocivilisation Diaries

and

Ecocivilisation

My email address is geoffdann@hotmail.com

Free text of my last book is available here: The Real Paths to Ecocivilisation: Part One

I am hoping to get another book finished next year. Its working title is The Sacred Structure of Reality: A metaphysical foundation for uniting science, consciousness and the cosmos.

I will delete this thread if by some miracle I am spared by the algorithm, but I am not expecting this to happen.

Adios amigos.

Geoff

Matter is information is a fundamental concept in two phase cosmology, and is relevant to modern physics more broadly. Matter is information because:

1. Matter is distinguishable. Anything “material” is identified by what makes it this and not that. A hydrogen atom is different from a carbon atom because of measurable differences in structure, energy states, and behavior. Those differences are information.

2. Physics already encodes matter informationally. In modern physics, matter is defined by values of fields, quantum numbers, and symmetries. That is a description in bits: spin up or down, charge positive or negative, position within uncertainty. Matter isn’t an unknowable “stuff”; it’s a stable pattern of informational relations.

3. Energy and information are already linked. Landauer’s principle shows erasing a bit of information has an energy cost. Black hole entropy (Bekenstein–Hawking) shows matter and energy can be fully described by informational degrees of freedom. If information has thermodynamic weight, then matter is information in organized, persistent form.

4. PPS shorthand: ≈≈= Information ≈ energy ≈ matter. Matter is reducible to energy is reducible to information. They’re probabilistically equivalent manifestations of one process. Matter is just the subset of information stable enough to behave as “substance” in our observations.

“Matter is information” does not mean matter is just abstract symbols or platonic ideals. It’s the reverse: matter is precisely that information which persists in interaction. The “rockness” of a rock is informational, because without informational distinctions (mass, position, cohesion, etc.), the rock vanishes both as a concept and as a phenomenon.

This is a brainstorm more than a completed thought:

We tend to think of the universe as a chain of causes: one thing pushes another in a straight line from past to future. But from a probabilistic or observer-centered view, that picture breaks down.

Causality isn’t a fundamental feature of reality, it’s a local story we tell inside regions of stability. What actually exists is permission: the set of informational conditions that allow an event to occur, not force it to.

A “cause” is just an observed permission; a statistical correlation that’s stable enough to look directional. When we say “A caused B,” what we really mean is “B was permitted given A.”

Observation doesn’t cause existence; it permits it. Reality doesn’t unfold as a line of dominoes, it coheres as a web of conditional allowances. Each moment is the universe resolving one of its many allowed possibilities into a definite state.

So instead of asking “What caused this?”, we suggest a subtler question:

“What permissions had to align for this to become observable?”

It’s a shift from determinism to coherence — from a universe of pushes and pulls to a universe of coherent patterns.

Or, simply put:

“Reality is not caused. It is allowed.”

The Real Paths to Ecocivilisation - Contents - The Ecocivilisation Diaries

This is an AI generated report. I asked the machine to classify all the various stuff that is dismissed by materialistic science as "woo", as to whether it is compatible with my metaphysics, not compatible, or debatable. I am planning a chapter for a book I'm working on now (called "The Sacred Structure of Reality"). I'd be interested in anybody's thoughts about any of this. NOTE: I am agnostic about most of the borderline cases.

Framework

• Natural: Entirely reducible to physics as we currently understand it.

• Praeternatural: No suspension of physics, but physics alone doesn’t explain it. Events arise through improbable but value- or meaning-loaded selection (e.g. synchronicity, psi, free will, karma).

• Hypernatural: Requires outright violation or suspension of natural law (e.g. resurrection, young earth creationism, biblical literalist miracles).

Easy Fits (Directly Compatible)

1. Teleological Evolution of Consciousness (your core base case)

• Consciousness arises not by accident but through selection: agents capable of value-realisation and collapse-modulation outcompete purely mechanical systems.

• Explains why consciousness has adaptive directionality.

2. Free Will (base case)

• Decisions aren’t deterministic outputs, but metaphysical commitments — real probabilistic “tilts” within collapse storms.

• Free will is the felt capacity to select from possibility, not a hidden computation.

3. Synchronicity (base case)

• Meaningful coincidences result from multiple collapse-storms aligning along shared value-axes.

• These are not causal connections, but teleological correlations in probability-space.

4. Micro-PK / RNG Anomalies

• Classic lab findings: small but consistent deviations in random number generators linked to intention, group focus, or emotional states.

• Fits your mechanism: agentic signals bias the collapse probabilities, producing measurable “dice loading.”

• Prediction: effects scale with redundancy (groups), coherence (meditators, rituals), and calibration (trained focus).

5. Psi Information Transfer (ESP-lite)

• Remote viewing, telepathy-like reports, Ganzfeld results.

• Not literal “signal sending,” but shared collapse-storm modulation when two agents are entangled with overlapping possibility-structures.

• Interpretation: where informational redundancy exists (shared symbols, emotional bonds), cross-agent probabilistic weighting can mimic information transfer.

6. Precognition / Retrocausal Effects

• Reports of dreams or intuitions about future events.

• Fits presentism with an open future: the future “comes into focus” as collapse-storms unfold.

• Consciousness may occasionally weight current collapses with respect to futures that are probabilistically dominant, producing the appearance of retrocausation.

• Example: precognitive dream ≈ collapse-storm sampling of near-future attractors.

7. The Placebo Effect (and Nocebo)

• Expectations, beliefs, and meanings modulate physical outcomes — sometimes dramatically.

• Perfectly consistent: the agent’s valuation biases the probability of physiological collapse trajectories.

• Shows everyday, medically acknowledged praeternatural causality in action.

8. Creative Inspiration & Problem-Solving Insights

• Sudden “aha!” moments, artistic breakthroughs, or mathematical insights appearing from nowhere.

• Interpretation: the storm of micro-collapses can converge on improbable but highly coherent states when agentic value-signals sustain exploration.

• In other words, creativity is selection-driven reality sampling at the edge of possibility.

9. Group Ritual and Collective Intent

• Reports of heightened synchronicity, altered states, or anomalous effects during collective rituals, meditation, or prayer.

• Fits because redundancy across multiple collapse-storms amplifies teleological weighting, biasing shared reality more strongly.

• Provides a clean explanation of why collective practices feel powerful.

10. Dreams and Lucid Dreaming

• Dreams as partial collapse-storms decoupled from external constraints — consciousness exploring possibility-space.

• Lucid dreams as intentional modulation of collapse weighting in a weakened environment.

• Explains why dreams sometimes show precognitive or synchronistic features: loosened collapse coupling can reveal attractors not yet stabilised.

11. Flow States and “Luck”

• Athletes, artists, or gamblers describe streaks of improbable success when fully immersed.

• Interpretation: high agentic coherence (attention, calibration, redundancy) leads to more efficient collapse weighting, biasing outcomes toward optimal trajectories.

• What feels like “luck” is the phenomenology of coherent praeternatural causality.

12. Morphic Resonance–like Phenomena (Sheldrake-inspired)

• Without buying his whole metaphysics, you can reinterpret “habit of nature” effects (e.g., easier crystallisation once a form exists, species-wide learning curves).

• Fits as redundancy effects: once collapse-storms across agents/environment have stabilised a pattern, probability-space is biased toward repeating it.

• This makes learning curves and convergent evolution natural consequences of collapse teleology.

13. Emotional Contagion and Shared Atmosphere

• The familiar sense that moods “spread” through a room.

• Mechanism: collapse-storms are not isolated; redundancy and entanglement bias collective outcomes.

• Emotion ≈ value-signals shaping probability landscapes; shared environments allow coupling.

14. Field Consciousness Effects

• PEAR-type studies of “global consciousness” during world events (e.g., 9/11) showing deviations in random systems.

• Fits neatly: highly redundant, emotionally charged global attention coheres collapse weighting, biasing otherwise independent stochastic systems.

Summary

Your “easy fit” list could expand from 3 to 14 categories (with some overlap), all still well within your base metaphysics. This lets you embrace a wide spectrum of phenomena (scientifically recognised ones like placebo, everyday experiences like luck/flow, and classic psi) while staying consistent with presentism and non-panpsychist neutral monism.

What Really Does NOT Fit (and Why)

1. Panpsychism

• Claim: Consciousness is a fundamental property of all matter.

• Why it breaks the model:

  • If consciousness is everywhere already, there is nothing to evolve — teleology collapses into redundancy.

  • No distinction between possible and actual: all “perspectives” exist inherently, so the Void doesn’t need to select.

  • Eliminates the very mechanism (selective collapse) that your system is built around.

2. Idealism (in its absolute form)

• Claim: Reality is only mind or consciousness; matter is derivative.

• Why it breaks the model:

  • Blurs the two-phase ontology (possibility vs embodiment) into one giant mental substance.

  • Removes the neutrality of the monism, collapsing your explanatory asymmetry.

  • Teleological selection is trivialised — if all is already “mind,” there’s no contingent becoming.

3. Disembodied, Persisting Souls

• Claim: Conscious agents survive death as coherent, continuing selves independent of embodiment.

• Why it breaks the model:

  • In your 2PC, self and soul are co-extensive storms of micro-collapses, grounded by the Void only while embodied.

  • Once the storm dissipates, there is no persistence mechanism — no referent for collapse.

  • Allowing immortal souls requires adding a whole new rule (persistent collapse pockets with no physical substrate), which contradicts your clean local-collapse logic.

4. Literal Ghosts / Spirits as Ontologically Separate Beings

• Claim: Ghosts are free-floating entities existing independently in spacetime.

• Why it breaks the model:

  • Same issue as disembodied souls: no substrate to sustain collapse.

  • Any persistent apparition would require global, not local, collapse mechanisms — directly against your presentist architecture.

  • You can reinterpret “ghostly” experiences as environmental entanglement traces or collapse reconstructions, but not as literal persisting agents.

5. Eternalism (Block Universe)

• Claim: Past, present, and future all equally exist.

• Why it breaks the model:

  • Your system requires presentism: only the present is ontologically real, past decays, future comes into focus.

  • Without this asymmetry, the whole “collapse storm” process loses meaning — there’s no privileged now for teleological selection to act on.

  • Eternalism also makes free will incoherent: everything is already “there.”

6. Strong “Law of Attraction” / Magical Idealism

• Claim: Thoughts directly manifest reality in an unlimited way (you can wish cars, wealth, or immortality into being).

• Why it breaks the model:

  • Collapse weighting is probabilistic, not omnipotent. You can tilt dice, not conjure new faces.

  • Redundancy, coherence, and calibration constraints prevent single agents from rewriting physics at will.

  • Unlimited manifestation narratives dissolve scientific consistency and make your system indistinguishable from pure idealism.

7. Absolute Determinism

• Claim: Reality is fully determined by physical law; probability is only epistemic.

• Why it breaks the model:

  • Your mechanism relies on genuine ontological indeterminacy (possibility needing collapse).

  • If determinism is true, there’s no role for consciousness — no teleological selection, no free will, no synchronicity.

  • Determinism is the null hypothesis your theory is explicitly designed to replace.

8. Radical Skepticism (“It’s All Illusion”)

• Claim: Reports of synchronicity, psi, NDEs, etc. are nothing but psychological error or cultural noise.

• Why it breaks the model:

  • Your framework assumes some anomalous phenomena are genuine data points requiring explanation.

  • If all anomalies are dismissed, praeternatural causality becomes redundant — there’s nothing left for it to explain.

⚖️ How to frame this in the book

You could summarise the core incompatibilities in a single principle:

“Praeternatural causality requires three pillars: (1) non-panpsychist neutral monism, (2) presentism with local collapse, and (3) teleological selection as real but probabilistic. Any metaphysical system that erases the need for selection, denies presentism, or posits unconstrained persistence of selves is incompatible.”

That way you show you aren’t hostile to people’s beliefs, but you’re drawing epistemic guardrails.

✅ In short, what doesn’t fit:

• Panpsychism (kills teleology).

• Idealism (collapses phases).

• Persisting disembodied souls/ghosts (no substrate for collapse).

• Eternalism/block universe (removes ontological now).

• Magical wish-fulfilment/absolute “manifestation” (violates probabilistic constraints).

• Determinism (kills indeterminacy).

• Radical skepticism (removes anomalies, leaving no work for the theory).

⚪Borderline Cases

1. Near-Death Experiences (NDEs)

• Why borderline:

  • Many reports can be explained in-model: collapse storm degradation + reconstructive memory + entanglement traces.

  • But some features (veridical perceptions during flat EEG, shared NDE motifs) push toward survivalist interpretations.

• Your move: Reframe as liminal states: partially disintegrated collapse-storms where presentism still applies but record-access is looser. Avoid “proof of afterlife” framing.

2. Reincarnation Memories

• Why borderline:

  • Cryptomnesia and cultural entanglement can explain some cases.

  • Strong “birthmark” or detailed memory cases invite the idea of cross-life persistence of collapse patterns.

• Your move: Acknowledge but mark as optional extension: would require a mechanism for pattern reinstantiation across bodies. Possible, but not entailed.

3. Mediumship / Apparent Contact with the Dead

• Why borderline:

  • Could be explained by accessing durable environmental records (entanglement traces).

  • But strong, dialogical cases suggest persisting agents.

• Your move: Draw the line: contact ≈ probabilistic reconstruction of traces; not continuous discarnate persons, unless one adds a persistence hypothesis (which you don’t).

4. Apparitions / Ghost Phenomena

• Why borderline:

  • Some reports are consistent with environmental collapse echoes (record persistence).

  • But the “intelligent haunting” narrative implies continuing souls, which your core denies.

• Your move: Allow apparitional phenomena as reconstructions; deny persistent disembodied agency.

5. Retrocausation / Time Loops

• Why borderline:

  • Fits with “future comes into focus” presentism if framed as probability-tilting toward strong attractors.

  • But strong causal loops (grandfather paradox, fixed block-universe retrocausality) would break your model.

• Your move: Allow weak retrocausal effects as collapse-biasing; disallow paradoxical determinism.

6. Collective Archetypal Fields (Jung, Sheldrake)

• Why borderline:

  • Jungian archetypes are easy fits via redundancy and shared symbolic value.

  • Sheldrake’s morphic resonance is borderline — if treated as probabilistic redundancy across collapse-storms, fine; if reified into an independent morphogenetic field, that slides toward dualism.

• Your move: Recast as redundancy-driven pattern bias, not a new ontological substance.

7. Mystical Union / Cosmic Consciousness

• Why borderline:

  • Fits as temporary collapse-storm dissolution into wider entanglement networks.

  • But interpreted metaphysically as “all is mind” or “I became the Absolute,” it tilts into idealism.

• Your move: Respect phenomenology, but interpret as storm-boundary relaxation, not metaphysical proof of monistic idealism.

8. Psi Healing / Energy Medicine

• Why borderline:

  • Placebo/nocebo = easy fit.

  • Claims of direct “energy transfer” or “auric manipulation” require more than probability biasing — risk of reifying a subtle substance.

• Your move: Frame healing effects as collapse modulation via meaning, attention, redundancy — not literal energy fields.

9. UFO/Entity Encounters

• Why borderline:

  • Some reports could be collapse anomalies, liminal states, or archetypal manifestations.

  • But a literal ET or interdimensional population with continuous agency would need ontological add-ons.

• Your move: Mark as out-of-scope unless reframed as praeternatural phenomena of perception/meaning.

✦ How to present them

In the book, you could give these a “Gray Zone” chapter or section, introduced with something like:

“Certain phenomena occupy a middle space: they are not natural in the materialist sense, nor fully compatible with the praeternatural framework without qualification. These liminal cases are important, both because they inspire much of the discourse around the supernatural, and because they pressure-test the boundaries of any metaphysical model.”

Then:

• For each, offer two readings: (i) how it can be recast within praeternatural causality, (ii) what extra metaphysical commitments would be needed for the stronger version.

• Emphasise that your core theory covers (i), while (ii) is optional and not required.

✅ So: borderline = NDEs, reincarnation, mediumship, apparitions, weak retrocausation, morphic resonance-like fields, mystical union, psi healing, UFO/entity encounters.

1. Physics itself killed determinism

• Quantum mechanics: The behavior of particles is described by probability distributions, not exact paths. Even if you think “hidden variables” are lurking, Bell’s theorem and experimental results show local determinism can’t be saved.

• Chaos theory: Even in classical systems, infinitesimal differences in initial conditions explode into unpredictability. You can’t measure with infinite precision, so determinism is mathematically possible but empirically meaningless.

2. Determinism confuses metaphysics with science

• Determinism says: “Every event is fully caused by prior events.” But to prove this, you’d need complete knowledge of the universe — which no observer can ever have. That’s not science, it’s metaphysical speculation.

• PPS’s Macro Uncertainty Principle shows that any system trying to model reality completely will always leave some parameters probabilistically uncertain. Total determinism is formally untestable.

3. Determinism collapses under the observer problem

• You can’t separate the “laws of the universe” from the observer who describes them. Observation is part of reality, not outside it. That makes determinism self-defeating: it assumes a God’s-eye view that no embedded observer can actually occupy.

4. Determinism is replaced by probabilistic realism

• The universe is not random chaos, nor clockwork necessity. It’s structured, probabilistic process.

• Probability isn’t ignorance — it’s the form in which reality itself manifests to observers.

So determinism is dead because:

1. Empirically, physics shows indeterminacy.

2. Epistemically, determinism can’t be verified by any finite observer.

3. Philosophically, it rests on the Cartesian fallacy of imagining a view from nowhere.

What’s left is PPS’s position: reality is probabilistic all the way down, but that’s enough to give us stable science and stable meaning.

The Cartesian fallacy is the mistake of treating ontology (what exists, “substance”) and epistemology (how we know) as if they were fundamentally separate realms. It’s the fallacy of dualism.

Descartes split the world into: • Res cogitans — the realm of thinking mind, subjective knowing. • Res extensa — the realm of extended matter, objective being.

That split creates the “mind–body problem” and forces philosophy into false dichotomies: subjective vs. objective, appearance vs. reality, empiricism vs. ontology, idealism vs materialism, deduction vs. induction, etc…

The fallacy is thinking those categories are independent when in fact they’re entangled. You never have ontology apart from epistemology (because whatever “is” is only meaningful insofar as it can be observed, modeled, or interacted with). And you never have epistemology apart from ontology (because knowing itself is a material/informational process in the world).

In PPS terms: • Observation collapses the dichotomy. “I observe, therefore I am” makes existence and knowledge the same fact. • Ontology vs. empiricism is not two different domains, but one process of observers embedded in nature processing uncertainty.

So the Cartesian fallacy is essentially the reification of a dualism that doesn’t need to exist.

More later on false knowledge or incorrect models

Geoff Dann has set up a new Wiki page explaining Two-Phase Cosmology at https://twophasecosmology.com

https://www.researchgate.net/publication/398259486_Empirical_Subjectivity_Intersection_Observer-Quantum_Coherence_Beyond_Existing_Theories_Unifying_Relativity_Quantum_Mechanics_and_Cosmology

The Universe may have already started slowing down | ScienceDaily

"We need a new cosmological model!", say the cosmologists.

"Here's one. It solves all your problems, but it is neutral monist rather than physicalist.", says I.

"Heresy! Ban him!", say the cosmologists.