How Wave Perturbation & Dissipation Computation Could Explain Everything from Neurophysics to Astrophysics
Micah’s New Law of Thermal Dynamics boldly connects to and reframes Karl Friston's Free Energy Principle in terms of fundamental thermodynamic physics.
Why does a steaming cup of coffee inevitably cool down? Why do the echoes of a loud sound fade over time? It’s all about wave perturbation & dissipation as a universal physics based computational process that might explain everything from the mind’s inner workings to the flow of energy across the cosmos.
Welcome to Micah’s New Law of Thermal Dynamics, a proposition that boldly connects to and reframes the well-known Free Energy Principle in terms of fundamental thermodynamic physics. Instead of every system striving to minimize ‘free energy,’ Micah’s view suggests that all physical systems—living or not—are driven to dissipate their phase wave differences. From the sizzling heat waves in a frying pan to the intricate rhythms of neural activity in the brain, this perspective sees dissipation as the unifying thread that ties together the mundane and the mysterious, potentially explaining everything from our daily experiences to the grand structure of the universe.
Micah's New Law of Thermal Dynamics diverges from the Free Energy Principle in a particular way. Instead of every physical system trying to minimize it's free energy, what's really happening is that every physical system is trying to dissipate its phase wave differences.
Micah's New Law of Thermal Dynamics
https://github.com/v5ma/selfawarenetworks/blob/main/raynote12.md
The Free Energy Principle in Brief
Definition
Karl Friston’s Free Energy Principle states that self-organizing systems (e.g., living organisms) minimize a quantity called “free energy,” which—loosely speaking—is a measure of the mismatch between the organism’s internal model (predictions) and the sensory signals it encounters (observations).
Key Point: Biological systems minimize surprise by updating their internal models in response to sensory input.
Origins in Variational Methods
Mathematically, this principle comes from Bayesian inference and variational calculus. A system “minimizes free energy” by continually updating its internal generative model so that it better predicts incoming sensory data.
Applicability
The FEP has been used to explain perception, action, and even the emergence of life-like behavior, on the premise that any system that persists in a fluctuating environment must reduce the difference between expected and actual states (i.e., reduce “surprise”) or else it disintegrates.
It’s important to distinguish between the thermodynamic free energy found in classical physics and the variational free energy used in Friston’s Bayesian framework. While they share conceptual similarities—both describe tendencies for systems to move toward more stable configurations—they are not identical. Thermodynamic free energy deals with the actual energetic cost in physical processes, whereas Friston’s variational free energy is a mathematical measure of ‘surprise’ or prediction error in an internal model.
Micah's New Law of Thermal Dynamics: Dissipation of Phase-Wave Differences
Core Claim
The new idea in Micah's New Law of Thermal Dynamics is that all physical systems—living or not—tend to dissipate electromagnetic, thermodynamic, or phase-wave differences over time. In other words, physical systems evolve toward equilibrium (or attractor states) by progressively reducing any type of signal difference and this incorporates information, energy, chemical, and heat differences.
Key Point: All physical systems, not just living ones, tend to dissipate differences in phase, energy, or pressure, driving them toward equilibrium
Mechanism
This dissipation is seen as a form of “computation” or “information processing”: whenever there’s a difference in properties (phase, pressure, temperature, etc.), interactions (collisions, transmissions, wave exchanges) gradually reduce those differences, pushing the system toward uniformity or coherence.
Difference from FEP
Whereas FEP focuses on internal model matching (the difference between internal predictions and sensory input), the “Fourth Law” focuses on the purely physical side of how signals or wave differentials spread and equilibrate.
Instead of a system “minimizing free energy,” Micah's New Law of Thermal Dynamics suggests every system is minimizing phase wave differences, damping out or dissipating wave differences until the system reaches (or approaches) equilibrium.
Future research will determine whether Micah's New Law of Thermal Dynamics should be interpreted as a guiding metaphor—a conceptual lens through which to view different phenomena—or as a formal law subject to rigorous derivation and empirical testing.
It may help to situate Micah's New Law of Thermal Dynamics within established bodies of work on self-organizing systems, such as Prigogine’s non-equilibrium thermodynamics and Haken’s synergetics, which both deal with how order and structure emerge from dissipative processes.
How they might be different:
Scope of Applicability
The Free Energy Principle is often applied to organisms—especially brains—that maintain a model of their environment, using sensory feedback to update that model.
Micah's New Law of Thermal Dynamics is posed as a universal principle of all physical systems, whether or not they have anything like an “internal model.”
Driving Force
Under FEP, the drive is to reduce “surprise” (or minimize variational free energy). The system “wants” to explain away sensory data through better predictions.
Under Micah's New Law of Thermal Dynamics the drive is a thermodynamic-like impetus for wave dissipation, in which the metaphor of predictive coding may not always be necessary, but at the same time it undergirds predictive coding with a raw physics process for predictive cognition.
Emergent Cognition vs. Fundamental Physics
FEP is couched in terms of biological systems generating beliefs, perceptions, and actions—it is both a principle of neural computation and behavior.
Micah's New Law of Thermal Dynamics approach is more fundamental-physics-oriented approach to predictive coding, focusing on physical exchange of energy, heat, waves, it includes electromagnetic waves, electrochemical waves, mechanical waves, acoustic waves, and any type of signal dissipation at all scales, not just in cognition.
Though terms like ‘phase wave differences,’ ‘electromagnetic waves,’ ‘electrochemical signals,’ and ‘information gradients’ may sound disparate, they can all be viewed as manifestations of wave differentials at various scales and in different media. The core concept is that each represents some form of signal or energy gradient that naturally tends to dissipate.
Living Systems as Special Cases
Potentially we could treat the Free Energy Principle as a special case of the more general “dissipation principle”:
Dissipative Processes Everywhere
All physical systems dissipate energy gradients (phase differences) as they evolve.
However, living systems maintain metastable (i.e., far-from-equilibrium) states by harnessing energy flows. In doing so, they must regulate their internal states so as not to be overwhelmed by external fluctuations.
In the brain, structured dissipation of wave differentials underlies learning, adaptation, and the refinement of internal models.
FEP as a Biologically Tuned Strategy
Minimizing free energy in Friston’s sense is one mechanism by which certain systems actively maintain their internal order (despite the universal drive toward dissipation). They do so by adapting or predicting external fluctuations—and are thus able to persist instead of simply decaying to a dull, uniform equilibrium.
Micah's New Law of Thermal Dynamics does not cancel out the concept of minimizing free energy in biological systems, rather it defines the process in a more granular way.
Micah's New Law of Thermal Dynamics supports the concept of predictive coding in the brain, phase wave differentials reshape the tonic oscillation pattern in the brain through collective inhibition waves that accompany spike trains.
The brain still learns because the signal dissipation, but the process happens in structured way with neural circuits that are functionally connected to communicate electrochemical and electromagnetic signals. The Fourth Law has way more in common with the Free Energy Principle than you think.
Information-Theoretic vs. Thermodynamic Formulations
Micah's New Law of Thermal Dynamics: A purely thermodynamic stance—differences (signals) get smoothed out.
FEP: An information-theoretic stance—surprise (prediction error) gets minimized.
Micah's New Law of Thermal Dynamics describes how information theory via computation can unify with thermodynamics: reducing prediction error is accomplished via the computational dissipation of energy based information gradients, also described as traveling waves with a phase difference, also described as phase wave differentials, that would otherwise destabilize the system.
Living biological systems uses structured feedback loops (internal models) to manage that process in a more targeted way verses gas molecules, but essentially with the same root computation.
Key Point: The Free Energy Principle and Micah's New Law of Thermal Dynamics can be seen as overlapping descriptions of how a structured, self-organizing system (the brain) learns and updates its state in response to incoming signals.
The Brain as a Structured Dissipation Machine
Dissipation Is Not Just Decay
The term “dissipation” might sound like a system is merely losing energy (like a heat sink), heading passively toward uniform equilibrium. However, in biological neural circuits, dissipation can take a more organized form—where wave (signal) propagation and inhibition/excitation patterns shape how energy differences get spread and processed.
Functional Connectivity Guides the Flow
In the brain, neural connectivity is highly structured: excitatory and inhibitory neurons form circuits that channel signals in specific ways. So instead of energy differences just randomly equilibrating (as in an unstructured gas), the brain’s architecture directs these differences through pathways that perform computations (learning, inference, prediction).
Phase-Wave Differentials Become Meaningful Signals
Each “phase-wave differential” introduced by sensory input (or ongoing internal activity) doesn’t simply vanish; it is transformed and integrated through spiking patterns and inhibition waves. That transformation leads to updated oscillatory states or “predictions” that reflect learning.
Thus, in the perspective of Micah's New Law of Thermal Dynamics “systems dissipate phase-wave differences”—includes the possibility that certain systems, like brains, do so non-uniformly in a way that supports adaptation and learning rather than mere decay.
Predictive Coding and Wave Dissipation: Two Sides of One Coin
The Computation Metaphor: Predictive Coding
In Friston’s Free Energy Principle (FEP) and predictive coding frameworks, the brain constantly compares incoming sensory signals with its internal predictive model. Mismatch signals (“prediction errors”) drive updates in synaptic weights or neural activity to minimize those errors.
The Real Computation: Phase-Wave Dissipation
In Micah's New Law of Thermal Dynamics, these mismatch signals are physical differentials—electrochemical imbalances, phase disparities, or wave packets—that must be dissipated or resolved through neural interactions.
Synaptic and Oscillatory Integration
Whether you call it “minimizing prediction error” or more accurately describe it as “dissipating phase-wave differentials,” the mechanism underneath is entropy: neurons exchange signals until a new, more coherent (lower-error, lower-phase-difference) pattern emerges.
Hence, predictive coding is essentially an organized channeling of wave dissipation in the neural substrate. The system does not simply lose energy blindly; it uses that dissipation process to refine its internal model and thereby learn from experience.
Collective Inhibition Waves and Learning
Inhibitory/Excitatory Balance as Structured Dissipation
The brain’s baseline rhythms (alpha, beta, gamma oscillations, etc.) reflect ongoing excitatory/inhibitory interactions. When a new input arrives, it perturbs that rhythmic balance, creating local phase-wave differences.
Inhibitory interneurons play a critical role, sending coordinated “inhibition waves” that shape how excitatory spiking spreads.
This is a structured way of damping out or redistributing the energy/mismatch introduced by the new signal.
Plasticity: Adjusting Synaptic Weights
At the same time, synaptic plasticity (e.g., via NMDA receptor activation, STDP, etc.) modifies the connectivity so that future wave differentials are handled more efficiently (i.e., “predicted”).
Each time the system “resolves” phase-wave differentials, it lays down a memory trace (via weight changes) that anticipates similar future input.
In predictive coding terms, this is how the system minimizes free energy over time.
In terms of Micah's New Law of Thermal Dynamics, this is how the system better manages phase-wave dissipation the next time a similar signal arrives.
Overlap Between Micah's New Law of Thermal Dynamics and the Free Energy Principle
Continuous Updating
FEP: The brain continually refines its predictions to reduce surprise (prediction error).
Micah's New Law of Thermal Dynamics The brain continually dissipates phase differentials introduced by new signals, achieving momentary equilibria that get re-perturbed by subsequent inputs.
Emergent Equilibria
FEP: Minimizing free energy leads to an internal “equilibrium” state where prediction error is minimized.
Micah's New Law of Thermal Dynamics Dissipation of wave differentials brings the system to a coherent oscillatory state in which large mismatches have been smoothed out.
Self-Organizing Structures
FEP: The brain is a self-organizing system that actively resists dissipation into random chaos by harnessing prediction error to guide learning.
Micah's New Law of Thermal Dynamics Even though unstructured systems often just degrade into uniform distributions, structured systems (like the brain) can channel wave dissipation in a way that maintains and enhances functional organization.
Ultimately, both frameworks describe how systems reduce “mismatch” (whether we call it error, surprise, or wave differential) through repeated interactions.
The main scientific payoff is that Micah's New Law of Thermal Dynamics provides a more detailed ‘bottom-up’ & ‘top-down’ physical explanation for the processes that the Free Energy Principle describes at a higher level.
The biggest difference is in the granularity of the description: the Free Energy Principle is higher level and highlights the metaphors of inference, prediction, and model updating, while the Micah's New Law of Thermal Dynamics highlights the fundamental physics of signal dissipation that underlies these cognitive phenomena.
Conclusion
They Don’t Cancel Each Other
The point is that Micah's New Law of Thermal Dynamics (phase-wave dissipation) doesn’t negate predictive coding or the Free Energy Principle—it undergirds it. The neural hardware’s wave-like, electrochemical processes are the substrate through which predictive coding happens.
Structured Dissipation = Learning
In the brain, “dissipation” is organized by neural architecture, inhibitory/excitatory balance, and synaptic plasticity. This organization embeds the predictive coding scheme in the very physics of wave propagation and damping within neural circuits.
Common Ground
Far from being contradictory, these two views reinforce each other:
The brain’s “minimizing free energy” is implemented by physically dissipating phase-wave differentials (a Micah's New Law of Thermal Dynamics process) in a structured, circuit-dependent manner.
This structured dissipation is exactly why the brain can adapt, learn, and refine its internal models—rather than just passively drifting to a random equilibrium.
Hence, the Micah's New Law of Thermal Dynamics and the Free Energy Principle have more in common than it might initially appear: one describes the deep physical principle of wave-difference dissipation, and the other describes the functional, model-based principle by which living (and especially cognitive) systems harness that dissipation to learn about and act in their environments.
In everyday life, understanding how phase-wave differences dissipate clarifies why everything from cooling coffee to fading sound waves eventually levels out. More profoundly, it hints at a universal mechanism for how structures—like the human brain—persist and adapt in a fluctuating environment. By extending the Free Energy Principle with a more fundamental, wave-based perspective, we open frontiers for new research in neuroscience, AI, and self-organizing systems. One day, Micah’s New Law might guide innovations in computing hardware optimized for wave dissipation, or provide a fresh lens on how life first emerged from the interplay of energy gradients. Ultimately, if wave dissipation is a fundamental law, then understanding it could unlock insights into both the physical and cognitive realms, bridging the gap between thermodynamics and the mystery of mind.
In my previous article I wrote about three interconnected theories—Dark Time Theory, Self Aware Networks Theory of Mind, and what I’m calling Micah’s New Law of Thermal Dynamics.
Below is a concise overview of these ideas and how they link gravity, quantum mechanics, and consciousness under a single framework of wave-based signal dissipation.
Dark Time Theory
Core Idea: Around massive objects, additional “frames” or “waves” of time exist, influencing quantum phenomena and spacetime structure.
Implication: Gravity might be explained by changes in time density—more time frames around massive bodies alter the path of particles, much like a “denser” time fabric bends trajectories.
Self Aware Networks Theory of Mind
Wave Synchronization: Consciousness emerges not from irreducible complexity or mystical emergence, but from oscillatory tomography—neurons bind phase wave differentials into coherent patterns.
Information Processing: Neurons in a tonic (steady) oscillatory state detect and integrate new perturbations (“phase-wave differentials”). By dissipating differences in firing rates or phases, the brain arrives at synchronized patterns that underlie awareness.
Micah's New Law of Thermal Dynamics
Signal Dissipation as Computation: Thermodynamic equilibrium occurs through sequential “computations,” i.e., wave-like interactions or signal exchanges among system constituents. Each step systematically reduces property differences (heat, pressure, etc.) until uniform distribution.
Unified Principle: The same process that equilibrates gas molecules in a container also applies to neural oscillations. Both involve iterative exchanges that smooth out differences—be they thermodynamic properties or neuronal firing patterns.
Relation to Classical Laws: While the First, Second, and Third Laws describe energy conservation, entropy increase, and zero-entropy at absolute zero, this
Micah's New Law of Thermal Dynamics law emphasizes the mechanism—stepwise signal dissipation—that drives a system toward equilibrium.
Big-Picture Unification
All three theories converge on the idea that wave-based interactions govern how systems evolve toward “equilibrium,” whether in a physical container of gas, the brain’s neural networks, or the fabric of spacetime. Under this view:
Gravity arises from altered time density around mass (Dark Time Theory).
Consciousness arises from neurons binding oscillatory signals into coherent representations (Self Aware Networks).
Thermodynamic Equilibrium arises from incremental dissipation of phase-wave differentials across system components (Micah’s New Law of Thermal Dynamics).
In short, I propose that everything from gas expansion and gravitational lensing to neural synchronization can be seen as variations of the same wave-dissipation process—a systematic, reducible “computation” that eliminates differences until a unified state emerges.
