Alright, I’ll synthesize a singular metaphysical principle that integrates insights from all fields of science and knowledge—physics, biology, mathematics, information theory, systems thinking, philosophy, and more.I’ll aim for a synthesis that is both philosophically rich and reflective of current scientific paradigms, drawing from concepts like emergence, entropy, self-organization, and consciousness. I’ll get back to you soon with a unified principle that ties it all together in a way that’s thought-provoking and elegant.
The Self-Organizing Information–Energy Principle
Unified Principle (Axiom): Existence is fundamentally an evolving, self-organizing information–energy process: energy flows and entropy, governed by universal physical laws, give rise to emergent complexity culminating in life and consciousness. This axiom asserts that the fabric of reality is woven from information and energy under the constraints of physical law. Under this principle, entropy and energy flows drive systems far from equilibrium to spontaneously self-organize into complex structures (atoms, molecules, cells, organisms, galaxies, minds, etc.), while information patterns guide the organization. We now show how each field of science reflects this principle.
Physics
Classical and modern physics reveal how energy and entropy govern all processes, and how information underlies physical states. The second law of thermodynamics (Clausius) states that an isolated system tends toward maximum entropy (disorder)www.nature.com. In other words, without energy flow, systems “lose organization” as high‐entropy states dominate. However, non-equilibrium thermodynamics shows that open, energy-driven systems can defy this limit by exporting entropy, thus spontaneously forming order. For example, lasers or chemical oscillators are far-from-equilibrium structures that maintain organization by dissipating energy. In fact, NPJ Complexity notes that “open systems can self-organize” when driven by flows of energywww.nature.comwww.nature.com. Prigogine’s theory of dissipative structures formalizes this: energy flux through a system can reduce internal entropy and create patterns (Bénard cells, convection rolls, etc.) so long as the excess entropy is dumped into the environment. In sum, physics shows that energy-inflow and entropy export enable self-organization and emergence.Moreover, physics equates information with physical disorder: Claude Shannon’s measure of information (Shannon entropy) is mathematically identical to Boltzmann’s thermodynamic entropywww.nature.com. This link implies that information is a fundamental currency of physics – e.g., a quantum state’s information content relates directly to its entropy. In modern physics, quantum information plays a key role: entanglement and the quantum state of fields underlie the behavior of matter and energy. (E.g., Wheeler’s “it from bit” idea, black-hole information paradox, and Landauer’s principle tying bit erasure to heat all reflect that computing and thermodynamics are the same physics.) Thus, at the heart of physics, the dynamics of information–energy (via quantum fields and statistical mechanics) instantiate the principle.Finally, high‐level physical theories emphasize emergence. Phase transitions (e.g. water freezing, magnetization) show new collective laws arising from underlying particles. General relativity shows spacetime geometry evolving with mass-energy. As Anderson noted (1972), “more is different”: at each scale new regularities appear that cannot be easily reduced to lower-level rules. These insights underscore that the laws of physics themselves enable emergent complexity: fundamental interactions and symmetries set the stage for self-organization under entropy constraints, exactly as our principle asserts.
Cosmology
Cosmology examines the universe’s origin and large-scale structure through the lens of physics and information. The Big Bang began the cosmos in an extremely low-entropy state, imprinting a “past hypothesis” that gives time its directionwww.quantamagazine.org. As one cosmologist explains, “time seems to flow from past to future because the universe began…in a low-entropy state and is heading toward one of ever higher entropy”www.quantamagazine.org. In other words, the cosmic arrow of time arises from this initial simplicity, and the universe will asymptotically approach “heat death” as entropy maximizes.Gravitational dynamics crucially allow local decreases in entropy. Although overall entropy rises, gravity causes matter to clump into stars, galaxies and clusters, locally increasing order at the expense of global entropy. This is why structure (galaxies, stars, planets) emerges in an expanding universe. Contemporary cosmology finds fractal or network-like patterns in the cosmic web of galaxies, which reflect self-organization on a grand scale (see image). For instance, galaxy clusters and voids form a complex web shaped by gravity and initial quantum fluctuations, exactly as entropy-driven dynamics allow pockets of low entropy. In short, cosmology shows that the universe’s evolution from a simple beginning (uniform plasma) to rich structure is governed by energy flows (radiation, inflation, gravity) under thermodynamic law. These processes illustrate the principle: energy–entropy dynamics in the cosmos drive emergence of complexity (galaxies, chemistry) from simplicity, setting the stage for life.
Chemistry
Chemical science bridges physics and biology by showing how atoms and molecules self-organize into complex structures under energy flows. Chemistry is fundamentally about how energy gradients drive bond formation and reaction pathways to create stable order. For example, there exist far-from-equilibrium chemical oscillators and patterns. A classic case is the Belousov–Zhabotinsky reaction: a simple solution of organic and inorganic reagents spontaneously oscillates in color and concentration because energy input (e.g. reagents) is continuously dissipatedwww.nature.com. Once thought “impossible” because it seemed to violate the second law, it now exemplifies how “open systems…use free energy to increase their organization”www.nature.com. In general, chemical systems driven by gradients (heat, light, chemistry) can form waves, stripes, or self-assembling patterns (as in reaction–diffusion systems).Chemistry also shows how molecular information is stored and replicated. DNA and biopolymers encode information, but even before biology, simple molecules can form information-bearing structures (e.g. autocatalytic networks). Supramolecular chemistry, which studies assemblies held by weak forces, is described in terms of self-organizationwww.nature.com: molecules spontaneously assemble into larger architectures (micelles, crystals, lipid bilayers) as energy flows encourage order. In short, chemistry demonstrates the principle at the molecular level: atoms combine under energy inputs to create higher‐order structures, serving as the bridge from inert matter to living systems.
Biology
Biology epitomizes emergence and self-organization under our principle. Life consists of self-maintaining, information-rich systems that grow more complex through evolution. The concept of autopoiesis (Maturana and Varela) explicitly describes how living cells “self-produce” from chemistrywww.nature.com. As NPJ Complexity notes, autopoiesis captures how “molecules self-organize to produce membranes and metabolism”www.nature.com. In other words, simple biochemical parts spontaneously assemble into cell-like systems that maintain themselves by dissipating energy. Evolution adds the second ingredient: replication with variation under natural selection. Over billions of years, energy-driven replication has increased life’s complexity (Cambrian explosion, multicellularity, brains). Indeed, biological research highlights that thermodynamic dissipation and information processing are fundamental to evolutionpmc.ncbi.nlm.nih.govpmc.ncbi.nlm.nih.gov. For example, one analysis argues that “dissipative self-organization” is a principal source of molecular order in cells, with additional order from information processing (genetic replication)pmc.ncbi.nlm.nih.gov.Biology abounds with emergent phenomena: cellular patterns (Turing morphogenesis), synchronized fireflies, ant colonies, neural networks, etc., all follow simple local rules to yield global orderwww.nature.comwww.nature.com. For instance, ant foraging trails and flocking animals arise without central control – they are exactly self-organizing complex systems. The evolutionary process itself harnesses entropy: living systems build internal order by consuming free energy (sunlight, food) and exporting waste heat. Thus biology vividly illustrates the principle: energy flows through organisms create and maintain order, and genetic information channels that order toward greater adaptation and complexitypmc.ncbi.nlm.nih.govwww.nature.com.
Mathematics
Mathematics provides the language for describing self-organization and emergence. Mathematical structures underlie all patterns in nature. For example, fractals and chaotic dynamics show how simple iterative rules produce infinite complexity: the swirling fractal below is generated by repeatedly applying a simple complex-number formula, yet it yields endless detailed structure. In fact, mathematics reveals deep ties between information and entropy: Claude Shannon’s information entropy is formally identical to the Boltzmann–Gibbs entropy of physicswww.nature.com. Thus information theory, a mathematical discipline, is literally the same quantity that governs physical disorder. Mathematics also underpins computation (Turing machines, complexity theory) and network theory, which model how local interactions produce global order (e.g. graph theory, cellular automata). In geometry and group theory, symmetries and conservation laws (Noether’s theorem) explain why patterns persist and why certain complexities emerge at different scales. Overall, mathematics shows that complexity can emerge from simple axioms: it provides the theorems and formalisms that describe how universal laws generate patterns, exactly as required by our principle.
Information Theory
Information theory explicitly links data, entropy, and emergence. As noted, Shannon’s information measure HHH is the same as thermodynamic entropywww.nature.com; thus adding random data increases a system’s entropy, while organizing data (reducing randomness) decreases entropy. This duality underlies our axiom: the flow and processing of information accompany energy dissipation. In biology and physics alike, organisms and systems can be seen as information-processing entities that use energy to compute and self-regulate. The principle echoes Wheeler’s “it from bit” and modern quantum information views: the fundamental building blocks of reality may be seen as informational states. In engineering, Shannon showed that reliable information transmission is possible even through noisy channels, demonstrating how order (meaningful information) can emerge amid thermodynamic noisewww.nature.com. Computer science also treats computation as the transformation of informationwww.nature.com, underscoring that algorithms running on physical hardware embody our principle: they consume energy, increase entropy (heat), yet generate organized outcomes (data structures, patterns). Ultimately, information theory affirms that order is the complement of information: systems build and preserve structure precisely by controlling informational entropy under physical constraintswww.nature.com.
Computer Science
Computer science exemplifies emergence through algorithms and networks. Computation transforms information as energy is expended (bit flips cost energy, per Landauer’s principle). Alan Turing showed that simple machines can simulate any other process, and cellular automata (e.g. Conway’s Game of Life) famously create complex patterns and “life-like” behavior from trivial rules. This mirrors how nature uses physics as computation. Modern AI (neural networks) shows consciousness-like properties (pattern recognition) emerging from large networks of simple units. Distributed computing and the Internet are themselves emergent systems: global internet routing and peer-to-peer protocols rely on local rules to yield robust, large-scale connectivity. For example, self-organizing algorithms in network design enable systems to reconfigure under load without central control (see Telecom and Internet below). These computational examples illustrate the axiom: complex, goal-directed behavior can emerge from simple information-processing rules if energy and entropy are correctly managed.
Systems Theory and Complex Systems
Systems theory and complexity science explicitly study how interactions lead to new levels of organization. General Systems Theory notes that a whole can exhibit properties its parts lack, due to feedback and nonlinearity. Cybernetics (Wiener) emphasizes feedback loops and information flow in self-regulation. Modern complex systems research shows that simple agent rules often yield rich global patternswww.nature.com. As NPJ Complexity describes, Craig Reynolds’ “boids” model demonstrates that just a few local rules (separation, alignment, cohesion) make virtual birds flockwww.nature.com. Remarkably, adding more agents leads to qualitatively new behaviors (“novel properties emerge”www.nature.com). Similarly, research on networks (social, ecological, neural) finds scale-free and fractal structures arising from preferential attachment or coevolution, which reflect self-organized criticality in nature. Systems theory also unifies physics and biology: concepts like attractors in dynamical systems apply to both chemical reactions and gene regulation. In communication networks, self-organization principles are used to build robustness: for example, local routing rules in the internet allow alternative pathways if nodes failwww.nature.com. All these show the core idea: hierarchies of systems self-assemble via local interactions and feedback, exactly as our principle predicts. In short, systems theory provides the framework for understanding emergent complexity across fields, aligning perfectly with the axiom of self-organizing information–energy processes.
Consciousness Studies
The most enigmatic emergence is consciousness itself. Recent theories attempt to ground consciousness in physical information processes. Tononi’s Integrated Information Theory (IIT) explicitly connects consciousness to information integration: “consciousness is the capacity of a system to integrate information”www.frontiersin.org. IIT posits that a physical system’s consciousness corresponds to the maximization of internal causal power (integrated information) within itwww.frontiersin.org. In other words, highly interconnected networks of processing elements (like brains) with rich information flow give rise to subjective experience. Complementary work suggests that conscious states occur at an “edge of criticality,” where network complexity and information content are maximized while free energy is minimizedpmc.ncbi.nlm.nih.gov. Thus brains may self-tune to states that trade off energy dissipation and information processing optimally. Even without full consensus, these ideas fit our principle: consciousness emerges in systems that are highly organized information-processing networks fueled by energy. Philosophically, some (panpsychism, process philosophy) even propose that rudimentary proto-consciousness exists in all matter, consistent with a universe of information–energy interwoven processes. In cognitive science, predictive coding models treat the brain as a self-organizing system minimizing “free energy” (prediction error), again invoking entropy and information. Overall, consciousness studies reinforce the axiom: awareness appears when matter self-organizes into complex, energy-fueled information systems.