I do not present a model of reality but instead a way of generating realities. Not a set of answers, but a set of parametric generators. The purpose is to predict and explain the behavior of real-world systems across time, scale, and domain.

This model is not a philosophical metaphor but grounded in mathematics, physics, and information theory. It is built from first principles. And from those principles, it generates a reality as a dynamic computation—one that is stochastic, emergent, and perpetually rebuilding itself. This reality is not intended to be a fixed strucutre nor intended to represent any sort of underlying reality. It is just a model.

My hope is that this forms the baseline for a wide range of future analyses. I will use it to explore power, agency, complexity, economics, psychology, culture, and systems of belief. But we start with the core framework.

Basic Architecture

Objects

We start by stating that things exists. In order for things to exist, we state that the first-person object, or self, exists. Then we move on to acknowledging that other objects exists, these would be third-person objects. The first person object is never separate from the model and is always acted upon and acts upon other parts of the model.

An object may be any distinct thing or grouping. In our model, we may treat these as objects: a person, corporation, social group, country, river, mountain, planet, galaxy, chair, clock, shool of fish, eco-system, algorithm, and a model. Generally, if you are able to conceptualize it as distinct, it can be an object.

Properties and Attributes

Every object has a set of properties or attributes that give it a more defined form. These properties may be transient or they may remain static for long periods of time. The important things to note about these properties is the effect they have on the actions of the object within the model, and that they are descriptive and often inherent to the object.

An important property an object may have is memory. I intend to use a few variations of object memory, including: an event ledger, state-based deformation of the object, statistical memory using a Bayesian inference, and relational deformation and inference memory.

Agency

Most objects have agency, the capacity to act on or influence other objects or systems. There is also a sense of distributed or system agency. Not all objects have the same degree of agency. We could describe this as a hierarchy:

1. free will
2. autonomy
3. mobility
4. responsiveness
5. influence

Some illustrative examples:

• A person has the free will to choose a path of action or inaction.
• A virus can influence biological systems.
• A thermostat has senses and acts, ie is responsive.
• A road sign influences driving patterns.
• A flock of birds turning in sync shows distributed autonomy.
• An AI making stock trades acts with system autonomy

Relationships and Forces

I’ve already indicated that there is a first-person object and many third person objects. These objects do not go about there existence in isolation. Every object is in a relationship with other objects. In fact there develops complex webs of relationships. How do we understand that a relationship exists between two objects? We witness the result of one object influencing the actions of another object. In other words, via forces. No object influences another object without a relationship path.

Relationships between two objects may be simple; singular, wth linear and uni-directional force between them. They may also, more commonly, be complex; with multiple relationships and non-linear or stochastic asymmetrical relationships.

Types of forces include:

• Physical (gravity)
• Emotional (love)
• Financial (puchase)
• Political (vote)
• Social (party)

Examples of relationships:

• One way sign directs you is single, binary, uni-direction.
• My relationship with my spouse is multiple, non-linear, bi-directional, and includes both symmetrical and asymmetrical.

Context

Context is a means of capturing the overall influence of the system on an object. It may be understood as the background or the environment within which all object interactions occur. We do not require that the contexts remain static, rather, we expect it will be a dynamic component of the model. Distinct contexts may be modelled as objects that have universal relationships with all objects within its domain.

Nearest Neighbor Interaction Principle

We base the effects on objects via their relationships on principles of local interaction. Both the forces exerted upon and by an object are primarily mediated through its nearest neighbors. Nearest is not strictly defined in a spatial sense but includes other relationship aspects such as time, affiliation, memory, and other object properties. Instead it is most clearly defined as having an adjacent or k-step relationship between two objects, where k is a small integer.

This significantly reduces the complex web of relationships we need to maintain to predict model behavior. Instead, we only need to consider the current state and active agency of a limited group of objects.

Examples:

• A person’s beliefs are shaped not by the global population, but by the few peers, platforms, and authorities they interact with.
• In fluid dynamics, the behavior of a molecule is shaped by collisions with nearby molecules.

This principle is essential for scalability, stability, and tractability. It also sets the foundation for emergent phenomena.

Engines of Creation and Change

Time

We recognize that objects, contexts, relationships, and applied forces do not remain static. Instead, when snapshots of the model are taken and compared, we clearly see that change has occurred. The main dimension of this change is time.

Time is not considered as a universal metronome with everything in lockstep. Instead, we view time as having its own set of properties which are dynamic and emergent.

• Time is Relativistic and Uneven: As proven by Einstein’s theories of relativity, the rate of time’s passage is relative to an Object’s velocity and its position within a gravitational field. Time flows slower for a particle in a particle accelerator and for an observer at sea level than it does for a satellite in orbit. This is not a perceived difference; it is a physical reality.

• Time’s Arrow is Entropic: The unidirectional flow of Time—its arrow—is an emergent property of the universe’s tendency toward states of higher probability. This is described by the Second Law of Thermodynamics (increasing physical disorder) and Information Theory (increasing uncertainty, or Shannon Entropy). The deep physical link between these two entropies confirms that time’s direction is inseparable from the system’s evolution toward a state of greater information and disorder.

• Time is Stochastic: At the quantum level, the timing of fundamental events, like the decay of a radioactive atom, is irreducibly random. This shows that the landscape of Time contains inherent, probabilistic uncertainty.

Stochasticity

I use the phrase “randomness is endemic to the system”. Basically it means that unpredictability is a natural, inherent, and persistent characteristic that originates from within the system itself. Probabilities do not represent noise or a limitation of knowledge but instead are fundamental to the system itself. The forces it applies and the effects those forces have are both stochastically determined, which yields an outcome distribution rather than a determined result. Additionally, we may expect areas of instability or even discontinuity within our system.

Some examples include:

• Mutations occur stochastically during DNA replication. Natural selection acts on random variation.
• The brain operates with noisy neuronal firing patterns; two identical inputs may lead to different decisions.
• Stock prices are influenced by thousands of interacting agents with feedback loops, delays, and emotional responses.

Stochasticity is a core feature of complex and adaptive systems. Without that randomness the system quickly becomes brittle.

This stochasticity:

• Exists at quantum scales: radioactive decay, quantum jumps.
• Exists at macro scales: weather, mutation, market fluctuation.
• Exists in cognition: noise in memory, unpredictability in choice.

While the current model treats stochasticity in classical probabilistic terms, this is not a model requirement. Instead, we may readily accommodate quantum probability, where system state evolution occurs via amplitude functions and measurement is governed by contextual collapse. This would substitute complex-valued amplitudes and Hilbert space logic where entanglement or interference patterns are relevant.

Force Aggregation

Force aggregation is the crucial process that determines how multiple, simultaneous forces acting on an object combine to produce a single, net outcome. It’s the specific function that translates complex inputs into a singular vector force. By changing our aggregation function, we aquire the ability to model a wide variety of complex systems. Force aggregation drives emergence.

1. From simple local rules, force aggregation applied via nearest neighbors creates complex, unpredicatble behavior.
2. The aggreation function may determine the type of system interaction that will occur. Using the proper function provides us with the means to build cooperative, competitive, and other system types.
3. The overall pattern of the system combined with the localization of force aggregation provide us with our emergent complex systems, and their unique abstract object rules.

Exampless:

• Additive function: Gravity, social consensus, cumulative pressure.
• Multiplicative function: Communication chains, system bottlenecks, interdependencies.
• Threshold function: Tipping points, political revolutions, percolation.
• Set-based function: Cultural identity, language, legal systems.

The Law of Emergence

The Law of Emergence states that in complex systems composed of interacting parts, higher-order objects and relationships arise that are not explicit to the component parts themselves. These emergent properties are governed by nearest neighborhoods, shaped by context and probability, and often display patterns, functions, or intelligibility that are unavailable to the component objects.

Examples:

• An ant colony that farms, wars, and navigates. No ant understands these activities.
• A flock of birds that moves as one. No bird controls the pattern.
• A human brain produces consciousness. A single neuron is a basic biological cell. It can fire an electrical signal or stay silent. It has no awareness, thought, or feeling.

Emergent Context and Time

Time and Context are not just background information upon which a system develops, evolves, and interacts. Instead, they are acted upon and dynamically generate as does every other component of this model. They exhibit emergent behavior generated by the computation of the model interactions.

Uneven Time

Time is not a constant.

Physics confirms this:

• Special Relativity: Time slows down as velocity increases.
• General Relativity: Time is warped by mass and gravity.
• Quantum Mechanics: Time is probabilistic at the smallest scales.

In this model, time is emergent—a result of computation and entropy. It is the system’s unfolding record of change.

It is uneven:

• Civilizations experience centuries of stasis, then decades of revolution.
• Biological evolution may pause, then jump.

It is discontinuous:

• A quantum state collapses in an instant.
• A stock market crashes in a day.

The model tracks not clock time, but event time—the rhythm set by systemic change.

Uneven Context

It is non-uniform:

• A single room may contain areas of heat and cold, trust and suspicion, wealth and poverty.
• A digital platform may have local cultures, echo chambers, regulatory zones.
• A planetary landscape is not a smooth sphere, instead it has valleys and mountains and cliffs.

It is dynamic:

• The context of “freedom” shifts during war.
• The context of “truth” shifts across scientific paradigms.
• While planetary systems may remain stable for millenia, they are not static and may be worn away by geological processes.

It is uneven:

• Small changes in input may cause no shift—or a dramatic reconfiguration.

Context is the frame within which all Objects compute their next state. In our model, we may view them as a field, super-object, or other mathematical convenience.

Phase Changes: The Moments of Transformation

Because Time and Context are emergent and uneven, they may suddenly and radically shift. This is a phase change, the mechanism by which systems experience dramatic evolution.

A phase change occurs when the system crosses a critical threshold. A slow accumulation of pressure or a small change in a key variable can trigger a feedback loop that causes a catastrophic, system-wide transformation. The landscape of the Context is instantly reshaped, and the pace of Time may lurch from slow to fast.

These phase changes are not just physical but can be social, economic, or political, representing events like market crashes, scientific revolutions, or the outbreak of war.

Examples:

• Melting/Freezing: A substance transitioning between a solid and a liquid state. The critical threshold for water is 0°C (32°F) at standard pressure. Below this, water is a solid (ice); above it, a liquid.
• Market Crash: A slow build-up of speculative overvaluation (the “stable” phase) suddenly gives way to a rapid, self-perpetuating sell-off and panic (the “crashed” phase) when investor confidence crosses a critical threshold.
• Traffic Jams: On a highway, traffic can flow smoothly. But if the density of cars crosses a critical threshold, the system’s state can abruptly flip into a “jam,” where cars move in a collective stop-and-go pattern. The behavior of the whole system has fundamentally changed.

Information and Thermodynamics

Ours is a model for the universe that is computational it relies on the strong connection between information and the physical. This connection is unlocked using entrophy.

1. Thermodynamic Entropy: This is a measure of physical disorder in a system. The Second Law of Thermodynamics states that this disorder always tends to increase, providing the physical basis for the “Arrow of Time.”
2. Information Entropy (Shannon Entropy): From Information Theory, this is a measure of uncertainty or “surprise” in a system.

• A predictable system with only one possible outcome has zero information entropy.
• A chaotic system with many possible outcomes, each equally likely, has maximum information entropy.

Information is Physical

These two entropies are deeply, physically intertwined.

• Landauer’s Principle: states that any logically irreversible operation, such as erasing one bit of information in a computer, must dissipate a minimum amount of energy as heat, thereby increasing the thermodynamic entropy of the universe.
• Maxwell’s Demon: A famous thought experiment with a “demon” that could lower the thermodynamic entropy of a system by sorting fast and slow molecules, violating the Second Law of Thermodynamics. Instead, the demon MUST store and later erase information to perform this sorting. Thus by Landauer’s Principle energy is used and no paradox exists.

Applying Information Theory to the Model

Information Theory provides a powerful new descriptive language:

1. An Emergent Context can be described by its information entropy.

• A stable, ordered, and predictable Context is a low-entropy state.
• A volatile, chaotic, and rugged Context—as you’ve described it—is a high-entropy state. The system is generating a high degree of uncertainty about its future state.

2. Forces are Information Transfer

• The Forces between Objects are the transfer of information. An object acts upon its neighbor by providing it with new information, which causes the neighbor to update its state. The Nearest Neighbor Principle means that information coherence is highest among adjacent objects and attenuates over distance.

3. Time Revisited In addition to the previously defined Arrow of Time points in the direction of increasing thermodynamic entropy. We now add that the “Arrow of Time” is also the direction of evolving information states. The past is a set of fixed, low-entropy records (“memories”), while the future is a high-entropy landscape of possibilities and uncertainties. The universe is constantly processing information from the present to generate the uncertainty of the future.

4. No Ultimate Truth as an Information Principle An “ultimate truth” would be a final, permanent state of zero information entropy. A state of perfect order and absolute certainty, forever. Instead, the model declares that the system is an information-generating engine. It never reaches a final state of zero entropy because its own operations perpetually create new possibilities and new uncertainties.