Contents
Reality is a single, structured process. This ontology explores that process using two co-equal descriptive lenses. These are not claims about a duality in reality itself, but are different vocabularies for talking about the same underlying thing, in the way that a musical score and an audio waveform are two different but complete descriptions of the same piece of music.
The two lenses used throughout this work are:
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The physical lens, which uses the vocabulary of dynamics, forces, and conserved quantities like matter and energy.
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The informational lens, which uses the vocabulary of states, patterns, and the distinguishability between them.
Consider a single photon. Physics provides a unified mathematical description of this particle. The lenses are not looking at different parts of the photon, but are applying different descriptive frameworks to the whole:
Under the physical lens, we describe the photon by its dynamics—its energy, its momentum, and how its polarization affects its interaction with a magnetic field. The vocabulary is one of cause and effect, forces, and motion.
Under the informational lens, we describe that same photon by its state. The complete set of its properties—energy, momentum, polarization—defines a single, distinguishable pattern. The vocabulary here is one of information: this specific pattern is different from another possible pattern (e.g., a photon with a different polarization). The semantic significance of this pattern (e.g., "this polarization means a '1'") is not in the photon, but is assigned by an agent using it as a signal.
Neither lens is sufficient on its own because both are incomplete descriptions. The underlying reality is neither just classical "billiard balls" nor abstract "ones and zeroes." Much like how the old concept of wave-particle duality was an oversimplification for a reality that is fundamentally non-classical, these lenses are simplified models we use to grasp a unified process that transcends both descriptions.
The lenses are bookkeeping schemes for different kinds of questions. When this framework privileges informational vocabulary, it is a deliberate choice of descriptive language, not a claim that information is ontologically separate from matter.
Information Levels
The physical lens provides the foundational description of reality, but its vocabulary of forces and dynamics is not sufficient to describe the functional complexity that emerges from that foundation. The value of the informational lens is that it provides a necessary vocabulary for understanding how structure, function, and eventually meaning, arise from the physical world.
To help visualize this, this ontology borrows a conceptual metaphor from speculative physics: the idea of reality emerging from the interactions of dynamic patterns called worldsheets. This is not an endorsement of string theory, but a tool to illustrate how a single type of underlying process can give rise to a rich hierarchy of organization.
The informational lens uses this metaphor to organize patterns by their functional complexity:
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Fundamental Information: At the deepest level, the underlying process manifests as stable, quantized patterns. Each distinct, stable mode (visualized as a type of string vibration) appears as an elementary particle, forming a foundational alphabet for reality. This provides the stable components from which everything else is built.
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Organizational Information: This refers to the specific configurations and relational dynamics of those fundamental patterns (visualized as the specific shape and interactions of worldsheets). This is the domain of structure and complexity—the difference between a random assortment of carbon atoms and the highly organized structure of a diamond or a protein.
While physics can describe the organizational patterns, its vocabulary is not designed to capture their functional significance. This is where the informational lens becomes essential. It provides the language to describe the transition to:
- Semantic Information: This is information that carries meaning for an interpreting agent. The pattern of atoms in a DNA molecule has a meaning (a set of instructions) that is only realized in the context of a cell. This meaning is a real, causally effective property of the system, but it's a property of function, not of physics.
This is the value add: the informational lens provides the only vocabulary we have for describing the crucial transition from a world of physical patterns to a world of meaningful, functional, and agent-relative ones. It is the necessary bridge to the special sciences of biology, psychology, and sociology, as explored in the Pathway of Emergence.
Ontic Structural Realism
Thus, there is an inseparable link between the strings and the informational patterns they realise. A string is its dynamic pattern of existence; describing that pattern in energy units or in bits are alternate projections of the same object. Ontologically, we therefore lean toward what philosophers call ontic structural realism: reality is structured process; "material" and "informational" are two coherent but partial glosses on that structure.
The transition from basic organizational/structural pattern (e.g., the patterns of worldsheets underlying an atom) to semantic or processed information is therefore not a jump from "matter" to "information," but a climb up the same pattern hierarchy (Semantic vs. Organizational vs. Fundamental) toward configurations that model, predict, and act upon other configurations.
Pattern Stability and Adaptive Efficiency
A fundamental principle emerges from observing how patterns behave: stable patterns tend to propagate their structure. However, the nature of this stability undergoes a critical phase shift as systems increase in complexity. We must distinguish between two forms of stability:
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Static Stability (Persistence): In simpler physical systems, stability is characterized by resistance to change. An atom maintains its structure through fundamental forces, and a crystal lattice resists deformation. This is a passive, inertial stability. When perturbed, these systems tend to return to their original low-energy state.
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Dynamic Stability (Adaptive Efficiency): In complex information systems—from biological organisms to neural networks to social structures—stability is an active, dynamic process. It is not defined by passive resistance to change, but by the capacity for efficient adaptation. As detailed in Emergent Stability and Longevity, these systems maintain their integrity by evolving. This higher-level stability manifests as the ability to leverage existing internal structure to rapidly find a new, effective configuration when the environment changes.
This principle of outward stabilization through adaptation operates through several mechanisms:
- Structural Recruitment: Organized information patterns tend to recruit and organize previously unstructured information, extending their own stability into the environment.
- Template Effects: Self-stabilizing patterns serve as templates that guide the formation of similar or complementary patterns nearby.
- Boundary Stabilization: Stable patterns create well-defined interfaces that constrain neighboring patterns, reducing local entropy.
- Cascading Order: Local stability creates conditions that favor the emergence of higher-order organizational patterns.
This refined understanding of stability is crucial. It explains how complex hierarchical organizations can emerge and persist not by being rigid, but by being exceptionally good at changing. It provides a foundational mechanism for understanding both the spontaneous emergence of order and the preservation of organizational complexity in a dynamic, unpredictable universe.