Musings on the Architecture of a Thought

A curious thought has occupied me recently. It revolves around the fundamental unit of our written work: the paragraph. We often treat a paragraph as a singular, atomic thing, but what if it’s more like a composite structure? What if a single collection of facts or ideas could be assembled in fundamentally different ways to achieve entirely different effects? It seems to me that the form of the delivery is just as crucial as the content being delivered, and I wanted to document some of my own thinking on how one might begin to formally separate these concerns.

A Functional Model of the Paragraph

To get my own head around this, I started thinking about a kind of three-tiered functional model. This isn’t meant to be a rigid prescription, but rather a way of organizing the different jobs a paragraph does.

  • Level 1: The Structural Role (The “Where”). This seems to be the highest level, defining the paragraph’s job within the entire document. Is it an Introductory paragraph, setting the stage? Is it a Body paragraph, doing the heavy lifting of the argument? Or is it a Concluding paragraph, bringing things to a satisfying close? Its position dictates its overarching function.

  • Level 2: The Communicative Purpose (The “Why”). Once we know where it is (e.g., a Body Paragraph), we can ask why it exists. What is its core intent? I see four main purposes here. Is it Expository, aiming to explain or inform? Is it Persuasive, built to convince the reader of a claim? Is it Descriptive, trying to paint a sensory picture? Or is it Narrative, recounting a sequence of events?

  • Level 3: The Method of Development (The “How”). This is the most granular level, the internal architecture. How is the paragraph built to achieve its purpose? This is where we find the familiar rhetorical strategies: Compare and Contrast, Cause and Effect, Problem-Solution, Exemplification, and so on.

The interesting part, for me, is the dependency. The choice of Structural Role (Level 1) constrains the Purpose (Level 2), which in turn strongly suggests a Method (Level 3). It’s a cascade from general architecture to specific implementation.

An Experiment in Generative Composition

As a playful exercise to see this model in action, I put together a small Python script. The idea was simple: could I take a set of “core content”—just raw notes and facts about a topic—and use this three-tiered model to instruct a generative AI to assemble that content into different paragraph forms? The script simply takes the core content, a chosen purpose from Level 2, and a chosen method from Level 3, and asks the model to write the paragraph accordingly. It’s a way of operationalizing the theory.

Observing the Model in Action

The results were quite striking. Watching the same set of core concepts get refracted through different purposeful and methodological lenses is fascinating. Here are two examples generated by the script, both using the exact same source material but with different instructions.

Shared Inputs

  • Core Concepts:
    • Concept: Human systems can have multiple points of equilibrium, representing different long-term steady-state behaviors.
    • Equilibrium Points: Can be stable (system tends to return) or unstable (system moves away). They generally correspond to a system’s lowest energy state.
    • Linear vs. Nonlinear Dynamics: Linear systems typically have one equilibrium state, whereas nonlinear systems can have multiple possible “comfortable” long-term states.
    • State Switching: Perturbations can cause the system to shift from one equilibrium state to another.
    • Examples in Humans: This applies to psychological states (focused vs. unfocused), psychophysiological states (relaxed vs. stressed), and behavioral states (cooperative vs. independent).
    • References: (Allen & Carifio, 1995; Chow et al., 2015; Moulder et al., 2023).

Example Generated Paragraphs

Aspect Generation 1 Generation 2
Instructions Method: Expository + Problem-Solution Method: Expository + Definition
Opening Frame Frames the topic as a “significant challenge” in understanding behavior. Begins with a direct definition of the core concept.
Generated Output A significant challenge in understanding human behavior lies in the limitations of linear models, which often fail to account for the existence of multiple stable equilibrium states within complex human systems. While linear systems typically exhibit a single equilibrium point representing a system’s lowest energy state, human systems—encompassing psychological, psychophysiological, and behavioral domains—demonstrate nonlinear dynamics, capable of existing in multiple “comfortable” long-term states, such as focused versus unfocused attention or cooperative versus independent behavior. The existence of both stable and unstable equilibrium points means that perturbations, rather than simply causing temporary deviations, can trigger transitions between fundamentally different steady-state behaviors. This multi-equilibrium perspective, supported by research on nonlinear dynamics (Allen & Carifio, 1995; Chow et al., 2015; Moulder et al., 2023), offers a more nuanced understanding of human behavior by acknowledging the possibility of substantial, long-term state shifts in response to seemingly minor disruptions. Human systems, unlike many simpler linear systems, often exhibit multiple points of equilibrium, representing distinct long-term steady-state behaviors (Allen & Carifio, 1995). These equilibria, generally corresponding to the system’s lowest energy state, can be either stable, whereby the system tends to return to the equilibrium point following a perturbation, or unstable, leading to a departure from the initial state. The existence of multiple equilibria is a defining characteristic of nonlinear dynamics, contrasting sharply with linear systems which typically possess a single equilibrium point. In the context of human systems, this translates to the possibility of multiple “comfortable” long-term states, such as focused versus unfocused psychological states, relaxed versus stressed psychophysiological states, or cooperative versus independent behavioral states. Significant perturbations can induce state switching, causing a transition from one equilibrium point to another (Chow et al., 2015). The precise nature and stability of these equilibria are complex and context-dependent, making their identification and analysis a significant challenge in understanding human behavior.
Analysis The paragraph is structured as a solution to the problem of linear models. It emphasizes the why before the what. The paragraph is structured like a textbook entry, defining terms sequentially (equilibrium, stability, nonlinear dynamics) before applying them.

Broader Musings: Towards a “Clean Architecture” for Research

My first reaction to seeing this work was, well, a sense of amusement. It’s quite something to see a machine play with argumentation structures so fluidly. But it quickly led to a deeper line of thought. It really forces one to question the nature of authorship in an era where the framework that delivers information can be generated so easily. It seems less realistic, and perhaps less important, for individuals to focus on the fine-grained mechanics of paragraph construction. The real skill might be shifting to a higher level: a deeper understanding of argumentation itself, of rhetorical architecture, and of the audience one is trying to reach.

This is where my mind wanders to ideas from software engineering, specifically the concept of “clean architecture.” The principle there is to separate the core business logic—the truly essential stuff—from the delivery mechanisms like databases or user interfaces. I wonder if a similar pattern could be applied to research. The final output—the formal paper—is really just one possible delivery mechanism. The more fundamental “core” is the research project itself: the data, the core concepts, the key findings.

If one can successfully extract that core and hold it as a distinct entity, one could then, in theory, pipe it into any number of frameworks for dissemination. The same core research could be rendered as a formal academic paper for a peer-reviewed journal, a persuasive opinion piece for a broader audience, a series of blog posts, or a conference presentation. Each output would simply be a different “view” of the same underlying entity, tailored to a specific medium and audience.

This little script and the model behind it are, of course, just a bit of experimental fun. But they feel like a small, playful step toward conceptualizing this kind of “clean architecture research environment.” It feels like a way to spend less time on the syntactical details and more time on the deep architecture of our work, which, to my mind, is a fascinating possibility to consider.