Universal Modeling

By Gary Dalton (@gfdalton) |

Introduction

At the heart of any robust analysis lies a model that can accurately represent the complexities of a given reality. The intent of a model is to make accurate prediction. The model I propose and its components are not merely abstract concepts but are the essential building blocks for understanding and predicting the behavior of systems, whether they be physical, social, or conceptual.

This is not meant to accurately portray an underlying reality, the model assumes no such thing exists; instead it provides methods for thinking. I reject the concepts of a static reality or an objective Truth that is attainable. The model describes a universe that is computational, stochastic, relational, layered, and forever reformulating itself.

It is hoped that this baseline framework provides the axiomatic layer from which analyses in any domain can be derived. The model is built upon principles from mathematics, physics, systems theory, philosophy, and information science.

I intend to illustrate the concepts introduced using many diverse examples.

THIS IS A DRAFT!

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 act upon other parts of the model.

An object may be any distinct thing of 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.

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 web 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.

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, 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.

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 often 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.

Force Aggregation

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.