Introduction: Beyond Binary Thinking

In the last episode, I critiqued the idea of clean information units—the discrete, tidy categories that underpin Western models of thought. While useful, these units fall short of capturing the complexity and fluidity of reality. The model’s flaws led me to explore a more nuanced framework: one that embraces gradients and flows rather than binary oppositions. Enter the concept of points of concentration.
Points of concentration provide a way to view the world without strict boundaries or rigid definitions. They challenge the assumption that tidy, discrete categories are the only way to understand reality. Instead, they emphasize relative differences in gradients as a more nuanced and accurate framework for capturing complexity. Instead of framing the world as a collection of distinct units, we can understand it as a landscape shaped by varying densities—zones of richness and activity interspersed with expanses of quiet diffusion. This approach rejects classical logic’s insistence on clear-cut divisions, replacing it with a model that recognizes the continuous spectrum between extremes.

Diffusion: A Universal Force

Using the language of concentration and dilution, I can describe different types of shapes or distributions in the natural world and draw comparisons to abstract concepts that behave in a similar way. This brings us to the first important element of points of concentration: diffusion. Diffusion gives us a tangible way to see these ideas in action. It’s a simple, everyday example of how gradients play out in the physical world, helping us understand the balance between concentration and dispersion. 
Diffusion is understood to be a fundamental property of matter. When things diffuse, they move from areas of high concentration to low concentration. As an example, imagine a room full of marbles and an identical room that is completely empty, with the rooms separated by a wall. The moment you remove the wall, you create one conjoined room that is half-full of marbles. This shows an even distribution of the room’s contents: if the pressure builds in one area, it will spread out and release when given the opportunity.

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Diffusion is everywhere. Anything concentrated will eventually spread out, showing how gradients naturally form in the natural world. Sugar will dilute in water, and what starts with a few chunks of distilled crystal will dissolve into an evenly distributed drink, with no one part any sweeter than the rest. The alcohol I drink will diffuse through my body, seeping through cell walls and spreading itself throughout the body and the brain. Given the opportunity, any dense concentration will have the tendency to spread out to all corners of its environment.

The Cosmos and Concentration

If you extrapolate this pattern, you might imagine that we are heading towards a world of absolute uniformity, where this blending and homogenizing effect will make the universe slowly become one unmoving mass. Across billions of years, hot and cold will blend to form a perfect lukewarm nothingness across all existence. In the meantime, however, we see the opposite. Things tend to be extremely one thing or extremely another, forming points of concentration in space where they cluster and condense, as well as zones of emptiness devoid of content.

As an example, consider outer space. Most of the universe is mind-numbingly empty, with an infinite canvas of expansive void. Among this emptiness, there are a select few focused points of concentration where matter decides to aggregate. Instead of a uniform cloud of dust blanketing all of existence, as you might expect from a diffusing universe, matter instead clumps into stars or planets, separated by vast stretches of space. These patterns of relative density are vividly illustrated by the clustering of galaxies and the vast voids of intergalactic space, giving the impression of a binary distinction between matter and space. 

That being said, my proposed perspective only sees relative concentration, not absolute units. There is no point where definite substance exists, nor where true void persists. Instead, the universe unfolds along a spectrum of relative density and movement, where gradients define the reality we observe. Our concept of ‘objects’ or ‘solidity’ is therefore a simplification: it’s our intuitive categorization for things that are firmer or more consistent than we are, while space or emptiness is the catch-all label for everything that isn’t. From the perspective of points of concentration, the clustering of galaxies is best described as a continually moving and evolving distribution of energy, 

This model of concentration defies the simplicity of clean information units, which attempt to categorize reality in consistent, absolute terms. Instead, it emphasizes the necessity of embracing gradients and irregular patterns to fully capture the complexity of existence. The universe is an interplay between somethingness and nothingness, but neither pole on the spectrum truly ‘exists.’ The thing we see and experience is just clumps of relative somethingness that endlessly group themselves together in the most interesting shapes and forms. The stars form galaxies, and the galaxies form superclusters, each one a web of tension and movement. These shapes hold themselves together in intricate webs that resist the entropic blur of diffusion.

This interplay of relative density isn’t limited to the cosmic scale. The same patterns appear in the intricate networks of life, repeating itself at every level of scale. The structural web of galaxies echoes the web of neurons within our brain. Both gravity and organic tissue find themselves organized by interconnected network structures. They aren’t some nondescript homogenous foam; they are honeycombs of concentrated nodes and hierarchies of complexity. This preference of matter to concentrate itself and resist the force of dispersion is evident everywhere. Even within the smallest microscopic folds of matter, the building blocks of all things choose to concentrate themselves in specific shapes and distributions. A spoonful of water seems like one contiguous fluid, infinitely continuous and undisrupted. But what happens if you zoom in?

The mass of the water, the chunk of matter itself, is held in the core of its atoms. These bits of matter don’t behave like a dispersed, evenly distributed cloud, as you might expect a smoothly diffused fluid to behave. They instead clump and concentrate in tiny clusters, tight spheres surrounded by huge expanses of space. These centers of mass (the nuclei) bend in such a way that they manipulate the surrounding electromagnetic field, pulling electrons to them across a distance many millions of times the nucleus’ size. From the largest to the smallest structures, matter congregates and solidifies in points of concentration that resist the equalizing spread of diffusion.

Abstract Systems: The 80/20 Rule
Matter might be the most concrete way to describe points of concentration, but it’s a phenomenon that applies itself to abstract concepts as well. Just as the universe is shaped by relative densities on a cosmic scale, human systems mirror these patterns in their distributions of influence and impact. A good example of this uneven distribution phenomenon is the 80/20 rule, which describes a formula where a few points of concentration have a disproportionate, dramatic impact. 

For instance, on tech platforms, 20% of influencers account for 80% of the subscribers. Similarly, a small portion of coral species contribute disproportionately to reef structures, and a few major earthquakes cause the majority of casualties. These examples reveal how a handful of concentrated points can dominate an entire system, leaving the rest to fill in the background. 

This distribution highlights how relationships in such systems are best understood through relative comparisons rather than absolutes. In this way, the 80/20 rule reveals the limitations of clean information units, which struggle to capture the disproportionate influence of concentrated points within complex systems. For instance, it’s not the exact number of followers or coral structures that matters, but the striking imbalance between the most influential and the least. This reinforces the idea that gradients—not strict categories—are a more accurate way to describe and analyze these phenomena.

Personal Reflections: Selectivity and Focus

These patterns aren’t just theoretical; they shape the way I interact with the world around me. Instead of relying on the black and white thinking that comes naturally to my intuition, I can look at points of concentration to help me to more deeply understand the patterns in the world. I’m averse to unitary thought or binary thought, which is the mode within me that assumes every interaction or experience can be categorized and measured.  I try to quiet that part of me, allowing experiences to flow through without conscious interference. I try to instead tap into the concentration gradient perspective, which means letting myself be pulled by specific points of interest among a sea of relatively less stimulating alternatives.

Not all experiences in life are the same. Some environments in life provide information-rich stimulus, while others feel devoid of meaning and beauty. I do my best to give everything a chance, but I learn quickly what satisfies me and I’m very selective when discerning what I surround myself with. For example, I focus on the people I love and ignore all the rest. I have a low tolerance for strangers because it costs so much to interact with them. This aversion is linked to the cognitive effort it takes to meet someone new, the effort I spend to widen my circle of attention and include a new subject in its sights. I do my best to commit entirely to soaking up the experience of someone else, giving anyone in my sights the full spotlight of my attention. It invigorates me if they reciprocate the effort but quickly drains me if I’m not getting anything back from it. This makes me very engaged when I interact with the world at large but very selective with the people who I habitually interact with.

From this perspective, I conceptualize a finely distributed web of meaningful events that cluster around centers of interest, whether people, cities, or topics, and these concentrated points of light are spread unevenly across an otherwise broad sea of homogenous, dilute boredom. Just as mass sorts itself into different densities and distributions, the elements of the world that pull my attention also sort themselves into a labyrinth of concentrated points of allurement. 

The perspective of points of concentration is not just a tool to conceptualize the exterior world, but also frames how I respond to it. I choose to push my will in a similar way, choosing to be universally tolerant and only insist in rare circumstances. I tend to be someone with few preferences: I’m okay with a wide variety of food or activities; I’m easy to bend in the majority of contexts. I’m very willing to accept things, to go with the flow and let others direct my path. This means I tend to relax my mental muscle of assertion in most contexts; it stays sharp for the parts of my life that require action or response. This gives me the confidence to be able to give my full focus in the moments that I choose to exert myself, such as when publishing my writing or when developing my game. In these moments of deliberate action, I embody the principles of concentrated effort, channeling energy into pursuits that align with my values and ambitions.

From Theory to Practice
The writing up until now has been focused on describing my philosophical foundation. The structure of information units summarizes what I see as our 'default intuitive model' here in the West, and 'points of concentration' is a first look at a perspective that is easily accessible to intuition but that goes contrary to classical logic. 
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Starting next episode, the writing will delve into practical elements, beginning with an exploration of the rules for my game. Rules are an intriguing reflection of points of concentration: they condense meaning and structure while allowing room for interpretation. Crafting these rules requires balancing clarity and ambiguity, finding language that is explicit and intuitive without being overly burdensome. By understanding the gradients of player preferences and expectations, I can craft rules that resonate deeply with my intended audience, reflecting the role of diffusion and concentration in a real-world context. Philosophical concepts will continue to appear regularly but will be woven more diffusely into the narrative of my personal challenges and creative process. By anchoring these ideas in the tangible aspects of game design and business, I aim to show how abstract insights can shape practical outcomes, starting with the interplay of creativity and structure in crafting rules.

Do you cook your own food? My own meals tend to be boring - I optimize for speed and efficiency when cooking for myself, but I become passionate when I do it for a loved one.  I’m occasionally creative, but I prefer to base my efforts off a recipe.  It can be passed down from my parents, from a cooking blog, or even generated by AI, but I like to have some kind of concrete list of ingredients and steps that guides the process.  If I ruin the dish, I can at least compare my actions with the instructions to get a clue of where things might have gone wrong. 

Recipes, like all abstractions, aim to distill complex processes into manageable steps, but they are never perfect reflections of reality.  They are a virtual analogue, an information unit that uses a system of information to represent something about the physical world.  By following the recipe’s steps, I can change objective reality to match the intended design.  
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Having a good recipe does not guarantee a good meal.  Even if I follow the recipe perfectly, I might still accidentally make inedible slop. There are countless variables that go unaccounted for, such as ambient temperature, humidity, or even my current mood.  Even measuring by volume instead of weight, for instance, introduces variability that cleaner systems like weight-based measurements aim to reduce.

Approximating reality

Like recipes, all systems of information simplify reality, capturing some aspects while inevitably losing others. The ‘cleanliness’ of a system’s information determines how faithfully it reflects the original.  The more ways that something can be interpreted, the less reliable the information becomes. 

In other words, the recipe becomes an abstraction of experience, and the original act of cooking is reduced down to words on a page.  The abstraction is composed of information units, such as the ingredients, the steps, temperature and time. If the recipe’s information is clean enough, it will result in only one very specific flavor experience: the meal intended by the original chef.  Most of the time, however, it’s an approximation of an experience, and the actual outcome varies significantly from the original vision. 

This degree of deviance between abstract information and objective reality is the focus of the ‘cleanliness’ metric in blob theory.  Although clean information is my preferred personal terminology, I’m not the only one to investigate it.  Scientists and logicians throughout history have been concerned with connecting abstract information to the physical world, and the methods of precision have evolved over the ages. Yet, as we’ll see, this pursuit of cleaner information systems reveals a surprising paradox: the very tools we use to define clarity are themselves imperfect.

Chasing precision

Recipes aren't very scientific.  They use some standards of measurement, like cups or grams, but they are ultimately written in simple English. English itself is a system of information units: it is composed of patterns of sound that mirror the objects and phenomena of objective reality.  As systems of information go, it's a relatively informal system. It’s not consistent or permanent enough to produce high purity information: words change meaning over time, and can have different meanings depending on context. 
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For example, consider how “gay” as happiness and “mad” as insane have fallen out of fashion. This inconsistency over time limits English’s ability to function as a clean system.  Words can also have their meaning change depending on the context, like how ‘mad’ can mean insane, but it more likely means angry and on occasion means smitten.  This inconsistency does not disqualify language from being useful - language is obviously the dominant strategy of information transfer we have.  That being said, English is not optimizing for precision because words are naturally ambiguous.

​The inconsistencies in natural language, such as evolving meanings and contextual ambiguity, highlight the need for systems like ‘syllogisms’ that aim to reduce such ambiguity and bring consistency to reasoning.  This tool can be traced back to Aristotle, whose formal logical systems are still present in our modern day technology. In order to clean up the system of plain language, his ‘syllogisms’ make statements with high standards for truth and cleanliness.  The syllogistic format is pretty straightforward: it proposes premises then draws conclusions based on the laws of logic.  You can think of it as an if/then statement.  

As an example, consider the following statement: 
If I have a brother and his mother is alive, then my mother is alive. 
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The premise does not explicitly state that I have a mother or that she is alive, but it provides enough information to easily come to the logical conclusion.  This ability to draw conclusions relies on clean information that satisfies Aristotle’s three laws.  This means the concepts of ‘mother’, ‘brother’, and ‘alive’ all being well defined, consistent, unambiguous and non-contradictory.  By avoiding most of the ambiguity found in language, it revolutionized how information could be treated to be more reliable and consistent. Aristotle's syllogisms were a groundbreaking tool for cleaning information, and their influence persists in everything from formal education to computer algorithms.

The path to clean information didn’t stop there.  The evolution of cleanliness took a fundamental leap with Boolean logic, which fused logical principles with symbols and equations.  Instead of expressing a statement with the natural language of syllogisms, you can use mathematical signs to manipulate information with greater rigor and precision.  This can mean using operators, like ‘>’ meaning ‘greater than’, or ‘+’ meaning ‘and’.  This symbolism gave rise to statements such as the following symbolic representation of the first law (the law of identity) =

A=A
Units have consistent identities that do not change over time

This equation effectively says something is equal to itself, using the symbology used in formal academia.  This example is one of many forms that the symbols took, and they continually evolved across separate disciplines and across history from Boole to Gödel to Turing.  The trend of cleaner and cleaner information units has continued through the modern age: the codes that power the content of your digital screen are built from binary bits of logic that have passed through the evolutionary pipeline of information purification.  

Yet, in this process, it was proven that perfectly clean information units can’t be achieved. Gödel demonstrated that no system of logic can be both complete and consistent. Even the cleanest systems contain truths they cannot prove, revealing the limits of absolute precision.  

Perfectly CleanJust as recipes aim to standardize cooking, syllogisms and Boolean logic aim to standardize reasoning. But like recipes, they simplify reality and can never fully eliminate ambiguity. Despite being relatively cleaner than English, they still all sit on the same spectrum that describes different degrees of cleanliness. There is no binary distinction between ‘clean’ systems and ‘dirty’ ones. 

All systems require some blurring or editing to translate objective reality into information units.  As an example, imagine building a perfect map of a territory, a map so detailed it includes every blade of grass. At some point, the map becomes indistinguishable from the territory it represents, defeating its purpose as an abstraction.The closer you get to including perfect detail, the less practical the map becomes. Similarly, logic seeks perfect cleanliness but collapses under its own weight when pressed to its limits.

If information can’t be described in binary terms, then it means that information is in some sense illogical, meaning that it doesn’t satisfy Aristotle’s 3 laws (despite being built upon them).  After all, logic fundamentally relies on binary validation. If cleanliness itself is a gradient, then it fails the expectation of classical logic’s rigid requirements. This contradiction suggests that even the foundation of logic itself rests on imperfect ground—a flaw baked into the very tools we use to understand the world.

This paradox is more than an intellectual curiosity—it challenges the foundations of how we process, categorize, and act on the information that shapes our lives. Despite being the foundation of the modern Western world, logic is a system that somehow doesn’t abide by its own rules. The concept of units themselves, or categories themselves, is contradictory. How does this make sense?

Rejecting Logic

This episode, I’ve explored how we use clean information units to reflect and capture objective reality, whether through recipes, language, or formal logic. I’ve also shown that, despite forming the guidelines for logical thought, the idea of ‘cleanliness’ itself can’t exist as a binary category. This contradiction aligns with insights from Eastern philosophies like Taoism and Zen Buddhism, which reject binary thinking as part of their core beliefs.

This rejection of binary thought and categorical thinking can feel deeply unintuitive to the Western mind. After all, our default intuitive model is built on clean information units. Still, in the next episode, I’ll introduce my own model of inverted logic: a conceptual system that uses gradients of concentration and solidity to describe reality without relying on classical logic. This approach has guided my decisions, and I hope to make its value accessible to you.

Picture standing in the middle of a bustling marketplace—a swirl of colors, voices, and movement clamoring for your attention. This is life: a constant flood of sights, sounds, and sensations. Our brains collect this stimulus in the form of electrical impulses and organize it in a way that gives us a sense of the world. We categorize experiences based on how they match with the past: consistent patterns are recognized as ‘things’ and are turned into meaning. As a result, the experience of life is not just a blurred blend of sights and sounds but rather perceived as a world of concrete physical objects set in a shared space.

This process is part of default intuition: there is no intentional thought required when generating these basic assumptions about objective reality. I don’t need to figure out every day what food or danger is—my organism has evolved over millions of years to effortlessly recognize the important stimulus and respond to it automatically. Our bodies have survival instincts engraved in our DNA, and our brains continue to shape our understanding of the world as we learn from new experiences.

To understand and dissect this process, we can use models to categorize and organize the factors that constitute different parts of the world. Different models focus on different parts of reality: scientific models like Newtonian Mechanics are used to simplify and predict physical bodies in motion, or social models like Game Theory are used to understand negotiation, cooperation, and economics. 

In Blob Theory, I use the term ‘default intuitive model’ to describe the sorting algorithm that our brain uses to process and understand reality itself. For example, when you see a chair, you don’t need to analyze its parts to recognize it as a chair—your mind automatically identifies its shape, purpose, and context based on prior experience. This mental shortcut allows you to navigate the world efficiently without constant re-evaluation. It generates the sensation of objective reality by assessing the consistency of patterns, and it does so by using building blocks that I call ‘information units’.

Information Units

An information unit is a mental building block used to perceive, categorize, or reason about the world. It is a distinct and stable conceptual "chunk" that can be used to predict or calculate reality.  Their role is to be a reliable, condensed summary of the external world in order to consciously calculate or predict complex truths. The units themselves, once established, go unquestioned: they are effectively the bedrock upon which further conclusions are made.

As a simple example, consider a “chair” as a unit of information.  It exists as a concrete concept in the mind, even if different chairs look different.  A more abstract example could be “1” in binary code, where “1” and “0” are corresponding to the concepts of presence and absence. 

As a more complex example, consider gravity. It’s the word we have for describing the phenomenon of how things are pulled downwards. As a rule of thumb, the fact that ‘things are pulled downwards’ is extremely consistent and broadly applicable. In an industrial context, this simple truth can be used to sort objects with different properties, relying on the heavier bits to separate from the lighter ones. It can be seen in mining, where heavy metals like gold can be separated from lighter silt, or in agriculture, where grains can be sorted depending on how dense they are relative to the less valuable parts of the plant. It doesn’t matter where you are in the world, the law of gravity will behave the same regardless of your specific conditions.

Gravity is a general example because it is very consistent, representing a physical phenomenon with observable effects. In contrast, information units are an abstract concept — they have no physical form but instead measure attributes like permanence, reliability, and truthfulness within mental or conceptual models.

Clean Units

In Blob Theory, information units have degrees of cleanliness, which is a measurement of how stable and precise the unit behaves.  The default intuitive model relies on using units that are as clean as possible, as to provide conscious thought with a simplified foundation.  The measurement of a unit’s cleanliness uses a system that has its origins in Aristotle’s rules for logic and dates back nearly 2500 years. His 'laws of thought' established fundamental principles for reasoning, which directly align with the criteria for clean information units. By defining rules for consistent identity, non-contradiction, and binary existence, Aristotle’s framework mirrors the qualities that make an information unit 'clean' in Blob Theory. These ancient philosophical principles continue to underpin modern science, mathematics, and computational theory. The laws look something like this:

1. The units have consistent identities that do not change over time.
2. The units cannot contradict themselves.
3. The units either exist or don’t exist, there is no middle option.

If you think about something concrete like a chair, it easily satisfies these conditions.  A chair doesn’t randomly transform over time.  It cannot be simultaneously ‘broken’ and ‘not-broken’.  It either exists in a room or it doesn’t, it can’t partially inhabit a space.

The original purpose of these laws is to measure the degree of something’s logic or reason, but in the context of the default intuitive model, I use the laws to refer to the ‘cleanliness’ of a unit of information. A clean unit is one that follows these laws and can be relied upon for further calculation. It is permanent, consistent, regular, and solid. By comparison, a dirty unit either has an inconsistent identity, it contradicts itself, or it only partially exists.  This isn’t to say that a clean unit is objectively superior to a dirty one!  Clean units have their flaws; nevertheless, they are the basis of the default intuitive model’s operating system. 

The limits of cleanliness

When comparing physics with game theory, it’s clear that physics has higher standards of cleanliness. Clean information is associated with having binary thinking and very strict laws, and it assumes that problems have only one correct solution. Binary thinking involves viewing things in absolute terms (such as true or false, present or absent) with no gray area in between. In the real world, when talking about social models like game theory or economics, there is an implicit messiness or inconsistency in human behavior that muddies the waters.

The hard sciences are cleaner but still imperfect. If you consider the earlier example of gravity, at first glance it seems to fit the criteria. It has a consistent identity that doesn’t change over time: it accelerates objects at a rate of 9.8 m/s². It doesn’t contradict itself: solid objects move down, not up. It clearly exists: there is no place on earth where gravity fails to pull.

That being said, Newtonian gravity is just an approximation of reality.  Perfectly clean units don’t truly reflect how the world works. Newton’s description of clean gravity was upset by Einstein’s magnum opus: his general theory of relativity. His model updated our understanding by pointing out the inaccuracy of calling gravity a force and instead showed it as a condition of spacetime. From this perspective, gravity shouldn’t be seen as having binary existence, but rather as a continuous gradient of varying degrees of spacetime’s curvature. This violates the third law of thought, because under a relativistic model, gravity operates as a ‘middle option’ between concrete existence and nonexistence.  Our modern understanding of gravity isn’t classically “clean”, but it is undeniably a true depiction of the way things work. 

This illustrates a key flaw in our default intuitive model, a concept that relies on dividing reality into clear, binary states. We intuit things to either exist or to not exist, it’s necessary for our most basic conceptualizations of the world. Objective reality is rooted in these terms, and our default assumptions are built on this foundational bedrock. Still, there are a plethora of examples of modern science where this simply doesn’t hold. We exist in a relativistic world, where light and gravity are now known to not exist in absolute terms. It extends far beyond just gravity: at every level of reality—physical, conceptual, and mathematical—our pursuit of clean, stable information units has run into unavoidable limits.
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These facts prompt the need to amend the default intuitive model and to propose an alternative to how we perceive objective reality. To do so requires a deeper understanding of how units of information operate, in order to build and describe an alternate model of the way things work.
Next time, we’ll trace the centuries-long pursuit of clean information, starting with Greek syllogisms and the symbolic logic that followed. But this story is not just about refinement, it's about the limits of refinement itself. The clean, bounded units of classical logic have driven science and computation, but they have also boxed us into a worldview of fixed categories. By revisiting the origins of symbolic reasoning, we’ll lay the groundwork for an alternative approach, one that embraces categorylessness, boundarylessness, and the fluid nature of reality, echoing ideas found in Taoism, Zen Buddhism, and the philosophies of the East. If classical logic gave us order, what lies beyond it?