philosophy

In the moment, life resides somewhere between order and chaos. Moments transform continuously, but not randomly. Their change is determined by the integration of every imaginable factor into one cohesive ∆. But, as all things change, ∆ too changes. It defines the rules and is part of the rules.

So that means no outcome can be predicted 100% accurately 100% of the time, an amount of change is always present. 2 + 2 = 4 of course, because that math doesn’t describe the whole universe. It describes one dimension: conceptual quantity, and yes we’ve quite found that math can reliably predict quantities of conceptual things. But when math is applied outside of it’s controlled dimension, the numbers aren’t 100% reliable, neat, or clean.

The numbers are doing their job just fine, though. They’re describing the quantities of the dimensions they’re applied to. It’s just that the dimensions are always changing. Over time, however, we may notice patterns (order) and lack thereof (chaos) in those numbers. Maybe at that point we could say 2 + 2 = (between 3 and 5) and that would reliably predict that dimension. Then we have a math, moreover a logic, that works with the universe; logic that takes into account degrees of change.

To do this we must understand change so it can be reliably modeled and taken into account. One way this could happen is through vast interdisciplinary collaboration to study the patterns of change common or unique to disciplines. This is valid and should happen over time, however not all challenges can afford so much time.

Thankfully, we can measure patterns of change that have been much more successful at sustaining themselves all around us. “Nature” as we call it, is apt, because nature mirrors the nature of sustainable change. Astronomy, for instance, shows us patterns along *astronomical* timescales, giving us a glimpse into what patterns emerge as time→ ∞. Let's pause here to note here that yes, there are *patterns* to change.

Though there are patterns to the nature of the universe, they are not 100% deterministic. Nonetheless, understanding that there is some level of reliability to the nature of change across systems is fundamental to our navigation or adaptation across systems.

A hallmark of natural systems is dynamic equilibrium, harmony. Harmony, not in the sense of flutes and rainbows, but in the sense that nature is continuously reaching a balance between all intersecting dimensions. That balance lends itself to consistent changes, actions and reactions, relative 1’s and -1’s. Oscillation between points over periods of time: waves.

Waves, loops, or cycles are ubiquitous in nature, standing as symbols of sustained change. They’re but one pattern that appears across systems and disciplines, indicating there’s value to be gleaned from understanding how spirals, thresholds, ripples, and webs manifest in reality.

It’s undoubtedly complicated, as many systems appear to have no discernible pattern for us to recognize and are intricately intertwined with other systems. Thus the starting point for analyzing ∆ in terms of patterns is: wherever we can. Wherever we can find a pattern, start there. These existing patterns are our ever-changing reference points.

We must remember, though, it’s all relative. For instance, we know that Earth is rotating around itself, it’s also orbiting the sun, and both are in motion in terms of the galaxy, and further in terms of the universe. We also know that we are much less observably affected by the speed of expansion of the universe than we are by the rotation of the Earth. All of these ways of changing are happening to full effect simultaneously, but we only notice when the sun rises and falls.

This pattern of local sensitivity can be observed across systems, but it’s important to note that sensitivity to change is not the change itself. We may not sense how fast we are orbiting the sun, but that doesn’t mean we aren’t ourselves moving at the same rate as the earth. Again, it’s relative. So as we process and model the universe, we must vitally understand that our perspective is limited, and our findings are only relative to our perspective and lenses of bias and culture therein.

This understanding of relativity also applies to how systems perceive and interact with each other. What is local to a system is what is sensed to a higher degree than what is distant, and change at multiple scales are happening regardless of if they are sensed or reacted to by the system. This makes it imperative to examine a system from multiple perspectives and scales in order to understand in multidimensional space (reality) the nature of the change occurring.

Examination may take on the shape and form accessible by those equipped with the tools available for the given field of study. Each field is its own system, with truths and observations that are completely valid from the perspective they capture. This presents a challenge for cross-examination, as translation between concepts would need to occur at every intersection. However, the usage of a universal language to describe change, integrating that of patterns and degrees of change, would necessitate only one translation per field and would substantiate a common ground from which to perceive change itself.

Mathematics at first appears as a suitable foundation for our language, as it provides reliable mechanisms for working with conceptual and real dimensions. Physical science has long been translated with math, where math provides the semantics and symbology to understand the dynamics of real things. However the difference between quantitative and qualitative becomes readily apparent in fields other than the physical sciences. When numbers don’t make sense or aren’t accessible. Here we must rely on the language of dynamic complex systems and category theory.

Concepts such as feedback loops (when the output of a system ultimately influences its own input) or emergence (when non-obvious patterns arise from simpler patterns) provide helpful classifications on the types of change affecting ∆. Concepts such as morphisms (translations between objects) and functors (mappings between categories that incorporate system connections and dynamics) provide a formal mechanism to describe change in and across disparate systems.

Even then, describing how art changes the minds of individuals eludes simple categorization. Until we conceive of ways to systemize even the most subjective, qualitative aspects of ∆, we must track and account for this notion of unquantifiables with language or math as best we can. Maybe it’s through proxy, metaphor, or narrative — however we must, knowing that the lack of quantization is a temporary state to be later interpreted once the mechanisms are available.

This understanding of complete incompleteness is fundamental to the ∆ frame. This framework will forever be a work in progress, as everything is subject to change. New patterns will emerge, new methodologies will arise, new language will come to exist. We know this from understanding the nature of ∆, and embrace it in this framework.