Understanding Systems: A Journey Through Chaos, Meaning, and Observation

“We do not ‘come into’ this world; we come out of it, as leaves from a tree.” – Alan Watts

The world is built on systems. Some are physical, like the weather or a Kubernetes cluster. Others are abstract, like the algorithms behind a recommendation engine or the thoughts racing through your mind during a late-night coding/anxiety* session. Systems surround us, often unnoticed, until they break. Then, they demand our attention.

*same thing in my eyes

But systems don’t work the way we expect—they work the way they do. Learning to understand them means stepping back, observing, and letting go of the urge to impose our pre-existing, oft faulty, frameworks on their behavior. Whether it’s a distributed system, a social dynamic, or our own thought patterns, systems reveal their truths to those who pay attention. Observing without interference or judgement is the first step.

Watching Systems: The Art of Non-Interference

Alan Watts once talked about letting the system show you how it works. This idea is as relevant and important to debugging software as it is to understanding the broader world. When something goes wrong, the temptation and instinct to act immediately is strong. But rushing to fix a system without first observing it often leads to blind spots and unnecessary complexity. We cannot impose fixes on systems we don’t fully or correctly understand. You had one problem before, congratulations; you’ve now created another!

Consider a production system throwing errors. Logs are piling up, the team is tense, and solutions are being tossed around like hot potatoes. It’s tempting to jump in and start tweaking, but this knee-jerk reaction can obfuscate the real issue. By letting the system run, watching carefully, and gathering data, you’ll start to spot patterns—specific inputs triggering failures, timing issues, or resource bottlenecks. Observation often reveals what frantic action cannot.

And here’s the connection: systems evolve over time, often in unexpected ways. To truly understand them, we need to move beyond the urge to fix and toward the patience to observe their states as they unfold.

Time and State: The Illusion of Progress

What we call "time" is often just the progression of states in a system. A server doesn’t move through time—it processes requests, updates its state, and responds. A database doesn’t age—it stores snapshots of information, and its configuration changes based on the queries it processes. Time, as we perceive it, is nothing more than the reconfiguration of a system over and over again.

Imagine debugging a data pipeline. Yesterday, everything was fine, but today, a downstream process is failing. It feels like time has introduced the issue, but it hasn’t. Instead, the system’s state has changed—an upstream dependency was modified, or an input file had unexpected data. The illusion of time can distract from what really matters: understanding how the current state came to be.

This same idea applies to life. Rather than seeing ourselves as "moving through time," it’s more accurate to think of life as a system constantly reconfiguring. And like any system, the patterns of these configurations hold the key to making sense of what appears chaotic.

Patterns in Chaos: The Laws of Systems

What looks like chaos is often just a lack of pattern recognition. Richard Osman’s observation that "Deal or No Deal" reflects humanity’s discomfort with chaos captures this perfectly. When faced with randomness, we instinctively attempt to impose meaning to create a sense of control (by picking random boxes in some sort of "meaningful" order). But systems don’t need our imposed narratives—they already operate by their own internal rules.

Take distributed systems, for example. Logs from asynchronous microservices can look like a chaotic mess: out-of-order events, retries piling up, errors surfacing sporadically. But dig deeper, and you’ll see patterns emerge. Maybe it’s a service broadcasting events twice due to a race condition, or a misconfigured load balancer throttling requests. Chaos starts to dissolve when you identify the patterns underpinning and governing the system.

Once we see that chaos is just order we don’t yet understand, we can begin to analyze systems more effectively. This sets the stage for the next crucial insight: how the act of observing a system changes it.

The Observer Effect: How Perception Shapes Reality

In quantum mechanics, the double-slit experiment revealed that observation changes outcomes. The same principle applies to software systems. The moment you attach a debugger to a running application, it alters the behavior—perhaps slowing execution or changing timing-sensitive operations. Bugs that are easy to replicate in production may disappear in a debug environment. Observation itself becomes a factor in the system’s behavior. No two operations have ever played out under the exact same configurations and conditions (good luck debugging!)

The same dynamic plays out in broader systems. Monitoring tools, for instance, can inadvertently add latency or consume resources. In social contexts, simply watching someone can alter their actions. Systems respond to being observed, and this response must be factored into our understanding.

By recognizing the influence of observation, we can better account for its impact. From here, the focus shifts to how we interpret the events we observe.

Neutrality: The Stories We Tell About Events

Events in a system are neutral. A server crash, a deployment failure, or an unexpected spike in CPU usage doesn’t carry inherent meaning—it simply is. The meaning arises from the narrative we impose on it. In programming, a 500 error isn’t a catastrophe; it’s a breadcrumb pointing toward a misconfigured service or an edge case that wasn’t handled. In systems, events are clues, not judgments; they’re pieces of a larger puzzle we sometimes haven’t stepped back far enough to see the whole of.

When we treat events as neutral, they become opportunities to learn rather than failures to avoid. This mindset makes it easier to approach problems with clarity. By detaching meaning, we gain the freedom to explore the system on its own terms, without the baggage of imposed interpretations.

This detachment naturally leads to the next layer of understanding: removing resistance to how systems behave.

Resistance: The Barrier to Understanding

A system has no obligation to align with our expectations. It operates based on its design, its inputs, and the interactions of its components. Resistance to this reality often leads to misunderstanding. The harder we cling to assumptions, the more likely we are to miss the real story.

In software, resistance looks like stubbornly believing the bug must be in the service you’re most familiar with, even as logs point elsewhere. In distributed systems, it’s assuming linear causality in a system inherently built for asynchronous operations. Resistance is the mental friction that keeps us from seeing what’s actually happening.

By releasing resistance, we open ourselves to the system’s truth. This shift in perspective also applies to the design and evolution of systems, which are often shaped by thought.

Systems as Manifested Thought

At their core, systems are the manifestation of ideas. Software reflects the logic and creativity of its developers. Society mirrors the values and priorities of its participants. Even our lives can be seen as systems, shaped by the stories we tell ourselves and the beliefs we hold.

This connection is a reminder of the power we have to influence systems. Flaws in a system often trace back to the ideas that created it. Simplifying a convoluted codebase or reworking a brittle process requires rethinking the foundational logic. The same applies to personal systems—revising a limiting belief can transform how you approach challenges.

By understanding systems as expressions of thought, we gain the ability to reshape them with intention.

Final Thoughts: Finding Comfort in the Discomfort

Systems don’t need to make sense to us to function. Their behavior, even when chaotic, is governed by patterns and rules waiting to be uncovered. Understanding them isn’t about imposing control but about observing, learning, and adapting. Whether debugging a flaky API or navigating the complexities of everyday life, the principles remain the same: let the system teach you how it works.

Great systems thrive not on rigid frameworks but on flexibility and resilience. The more we engage with systems on their own terms, the more effectively we can work with them, rather than against them.

Closing Notes: Listening to Systems

Every system has something to reveal. Logs, workflows, and behaviors all carry insights into how the system operates. By watching closely, without interference or resistance, those insights come into focus. Understanding follows patience, and clarity emerges when we’re willing to let systems speak for themselves.

“You are an aperture through which the universe is looking at and exploring itself.” – Alan Watts

Every system, every moment, and every interaction is part of that exploration. Systems reveal themselves when we watch, learn, and listen—without interference, without resistance, and with curiosity. The answers are always there, waiting for us to see them.