© 2025, Angela Madsen All rights reserved.

For Violet.

This book was written by a human mind with no AI assistance at any point in the process.

All confabulation is my own. All research gaps are my own. All interpretive thinking is my own. Annotations are the best I could manage with the timescapes involved, and will be continued to be worked for a while.

Three Buckminster Fuller structures are used in the Systems flow page; Manoogian & Benson's Cognitive Bias Codex is used on the Cognitive bias page; otherwise all images are created myself using either Affinity Design or sketching in Concept. This book has been through multiple versions, multiple software, and multiple structures through the five years I worked on articulating it. Tools have included Ulysses, Powerpoint, and Affinity Publisher.

• Practical information architecture
• Contextualization
• Architecture overtones
• Point of view

A note about information architecture

There is never only one solution

There is not a prescriptive path through an information architecture project. There is no one, perfect answer that will last forevermore.

Personally, I don’t think I’ve taken the exact same path twice.

Consider navigation projects. They look the same from the outside: developing a hierarchy. But the priorities change. The known issues follow a different tenor, the backbone of the content was developed with more or less fluidity, the mental models of the people in charge of content are more or less flexible. The subject matter can be more or less complex, and the designers and developers more or less experienced with the material. All of these impact the time, attention, and reactions of the sources. Break them, and they’ll never maintain the architecture devised and the user experience will devolve back to chaos faster. 

The existing infrastructure is a product of a mix of short and long term solutions. Sometimes t

Information architecture is itself a node

With an underlying infinite network

These have been the strata patterns I’ve leveraged across projects and industries, understood through the super-high-level movement of data. They are not meant to be used solo, but layered — just like a painting.

There will be someone who will want to add the combinatorial forms as a “known” version of baseline shapes. I hope that doesn’t find traction, because it pulls with it the cognitive bias “Curse of Knowledge”. That bias, especially, makes it difficult for those deeply embedded in a knowledge area to include the insights from those who are learning. Worse, it often makes the knowledgable disparage the learning, “too stupid for this work/subject/effort,” when in fact a high hurdle was set and learning is happening. It’s an unnecessary development in the sphere of understanding, where everyone needs to play to some degree.

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Failing information states

When information structures don't work

You'd think it would be easy to see when information structures fail. It's information, right? So when it fails, it should be filled with lies. Right?

Nope.

When information structures fail it's because of gaps, misaligned data focus, and/or skewed connectome. Gaps means there's missing data nodes that harbor highly relevant information. Misaligned focus means that you are including information that hasn't found it's meaning and function in the problem solving (potentially "yet"). Connectome are the conduits for information, so when they skew they make it so you can't get to the nodes. Each aspect tips the system of information into stress along at least one of the system connectome aspects: flow, expansion/contraction, or elasticity; and often, also, in terms of resource tolerance.

So when information structures are nearing a failure state, the easiest thing to see is that the information is no longer hanging together; or,

Connectome is the fundamental structure

People are pattern finders, so connectome gets tricky

Connectome connects data and builds meaningfulness. This in turn helps us better understand the consequences of change and how it spreads. The primary information of connectome is pattern, in my opinion. 

People find patterns where they exist, and also where they don’t. We build them into everything we touch, whether the form is concrete (e.g., a house) or abstract (e.g., software).  We take patterns we like and apply them scattershot, whether it’s drawing rainbows on everything or seeing the world as a nail and you with a hammer. By the end of the day, we have all worked with patterns, someway and somehow — if only to make it to an appointment and say hi to someone (calendar, time, roadways, location precision, culture, language, who-ness — just for the obvious patterns).

When patterns are recognized, we get a scission of pleasure — a little dopamine spark that rewards us.  An easy-to-see pattern in

Taxonomy and definition

The funky  variability of “I”

“I” is a singular first-person pronoun; as such, it is taxonomy. The variability is definition and contextualized meaning. Meanings shift over time and can be the source of confusion as different people use taxonomy with different meanings. 

So, taxonomy comes with potential issues:

• People using different words to mean the same thing
• People using the same word to mean different things
• The shifts to underlying meaning over time and through cultures

My most frequent use of “I” is to point out limited perspective

It is intended to ground, to say that, “this is not the sum of all reality, but a perspective of one that should be combined with other perspectives to create a complex and nuanced whole.”

Every word is an encapsulated node. Because it is ‘unpacked’ in the mind, the meaning can have different contextualization unless work is done to make sure everyone in a certain space is on the same page. In other words, the me

Why think about structure

First focus: practical introduction

There are many concepts to build from while practicing information architecture. I have two that delight me beyond all others: people using information, and the structures.

The first, people using information, is a very complicated subject, and is the bulk of this book. And frankly, information structures have become such an implicit part of how I look at things that it won’t be as understandable without the fundamental blueprint. 

So, the fundamental blueprint it is: the palette for building information structures. In my mind, they are truly just the basics: black, white, red, yellow, and blue pigments that can be mixed to form the shades needed. And, yeah, not everyone will be able to get particular shades succinctly, and some of the resulting shades might be loosely representative.

This is fundamentals, though. Through fundamentals we start understanding how to mix purple from red and blue. Lighten that purple to lavender wi

Nodes

One of the two most basic pieces

This is what I use as a symbol for a node most often

It is intended to either be an abstraction, or to help view more pointed data as an abstraction

A node can be a:

• Data point
• Data row/set
• Process point
• Nuggetized idea
• The encapsulation of a fuller structure

Connections

One of the two most basic pieces

Changing a line – dash, color, weight, straight or curvy – can also impact meaning. A connection can be:

• Part of the data set
• Flow of information
• Flow of process
• Change state of information or process
• Idea
• Dependent data
• Metadata

All together, the connections form a connectome. 

Complex structures will have multiple strata of connectome.

Connections, longform

Where a node is an endpoint for data transmissions or redistribution in a tech

Juxtaposition and placement, longform

Using space to affecting meaning in models agnostic to connections

In two-dimensional art, once we have the marks we’re intending to communicate, we generally start focusing on forms. Forms are powerful in and of themselves. Logos, especially, tend to use only form and color to make meaning — any additional nuance or too-fine details becomes noise at best. A good logo is incredibly meaningful all by itself, and so can a set of nodes set on a page with intentional juxtaposition and placement.

When we start modeling information, we don’t necessarily require the use of connections right away. We might even deliberately put off documenting connections to deepen discussions and uncover more nuance. Some models can be truly complex before they ever get to the point of interaction with outside data.

While we’re in an unconnected state, especially, the juxtaposition and placement of nodes in reference to each other is meaningful.

Near / far

The first fa

Basic information structures, longform

Information structure is understood by the connectome — the pattern of connections that form between nodes.

There comes a point in two-dimensional art where, if you want a form to read as three-dimensional, shade and light need to be added. These qualities are superimposed on form to help it feel like it has dimensionality, even if it’s a simple object on an otherwise-untouched white ground.

Information has many dimensions. And by ‘dimensions’ I’m using the dimensions that I learned as an artist: two dimensional formats of drawing, painting, etc., that through use of shade and light can take on the legibility of three dimensions, and expand from there to provide context, narrative, and distance. When I’m talking structure, I’m considering the structure on one particular dimension. I will get into the concept of dimensions more in encapsulated rich data, levels, and strata. But the structure, on one dimension, generally coalesces to form — like a ball now fl

Nodes as encapsulated rich data

The potential of a data point

A simple node

Detached from meaning, the number can still be a simple node. It could absolutely have no greater context.

(Although it’s rare. Even if it’s your current bank account total, or a bill, or how much length you need to add, it still bubbling up with meaning attached to it.)

A node with supporting metadata

Many people recognize this particular number and it’s underlying meaningfulness, which probably starts along the lines of:

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Nodes as encapsulated rich data, longform

The simplest additional dimension in information structures

If two-dimensional art is intended to be representational, additional details needs to be added. Shade and light can make an object look like an out-of-focus, distant, vaguely floral form; details (line, texture, color) pull it from vague to distinct. Where art focuses on additional details that are layered on the outside, information architecture sometimes tuck those details out of sight.

Did you ever play with a Paku Paku as a kid? It has many English names; the one I grew up calling it was “fortune teller”. It’s an origami object where people put one set of information on the outside and have another person provide a couple answers to dig into their “fortune”.

Think about this object as a node. On the surface, the object says “this means something”. Open it up, and there’s more information. Not only more information, but that information leads to another layer which could literally be anyt

Levels

Navigating to the details

Example: menu

Example: rich data

Information points like a website can be contained and lensed to facilitate navigation. Amazon can implement a system to manage metadata, search, and filters to support access to detail as well as having a more formal menu-style navigation structure. Amazon, despite its breadth and depth of product, is not trying to connect the world and make sense

Levels, longform

The mixed-bag dimensions in information architecture

To start developing narrative for an object in art, it needs context. A flower could be part of a bush, in a vase, behind a person’s ear, and in each of those contexts a different story starts evolving. Even the centrality of the floral form could shift from central focus to ambiance.

Context is a key consideration in information architecture, and can be shown and leveraged in many ways. Levels, though, is one of the places where context really shines through.

Levels tend to switch taxonomy between corporate cultures. I still stick to “levels” for several reasons:

• It’s used reasonably frequently
• Even where it’s not used, it tends to be understood without too much chaos
• In it’s primary use case — hierarchies — it’s intuitive once the structure is in front of people
• I have other uses for some of the other words that can pop up

The numbering of levels is cultural, too. Just like the floor differences between t

Strata

Managing interpretive data models

Strata are everywhere, but we don’t often think of them. 

Each layer of strata is intended to communicate with adjacent strata. This is another place where two dimensions fail us: the strata aren’t actually stacked. The adjacencies aren’t in space, but in data. Strata use some of the same data to build a model, and that data can help people jump to another layer of strata. 

Strata are intended to help people find meaning in accumulated data.

Strata, longform

Dimensional ecology in information architecture

So far I’ve aligned the basics of information structure to the marks (nodes and connections), form (juxtaposition and placement), and shading (information structures) that define an object in two-dimensional art, building towards a three-dimensional interpretation. Color and detail (encapsulated rich data) make it recognizable, and context (levels) provides narrative. A representational piece of art also includes perspective. Perspective sets a contextualized object in the world, providing not only more context but a deeper narrative. Perspective can make a flower monstrous, or disquieting, or lovely.

I consider perspective at least as important in information architecture. It helps us understand where and why information isn’t included in a particular model — and that it still exists and is relevant somewhere, somehow.

When we start talking about truly huge data stores, information structures, software and product ecologies, th

Lenses

The point of view in information architecture

I really did think I could get away without dipping a toe into the people-y aspect of information architecture this early by sticking to the fundamentals of information structure. I couldn’t because without lensing, we don’t know where to look, and the bets made at every other step falls apart. In fact, I usually lens so early and often in an information architecture project that it’s almost like the paper or canvas the structure is marked on.

It’s not. The lens is the reason we’re marking to begin with, why the time and effort is worthwhile, and what we’ve decided needs to have a focus. And while the lens incorporates many, if not all, of the narrative elements of a good story, the pre-eminent reason is who. The lens is the viewer — not the information architect, nor subject matter experts, but the person intended to spelunk in the architecture we’re developing.

If we gaze into vasty seas of data, do we start looking at orbital mechanics t

Information structures ripen

Information gets modified all the time. 

Think about sharing music. We’ve gone from traveling minstrels, to sheet music, to phonographic cylinders, to records, to 8-track tapes, to cassette tapes, to cds, to downloadable files, to streaming files. Each iteration not only increases the breadth of geography in which music can be shared, but increases the quantity of information available in each form factor and the legibility of the sounds.

Music is information. 

At the same time, we’ve developed new musical instruments. We’ve created novel rhythms and expressions, developed complexity, added music to other information to create richer communications. So while the ability to share information has changed, so has everything about what that medium can do and share and what tools are involved.

The information around music has ripened.

I think this happens to every information endeavor we do, and that it happens in multiple ways. 

• Ideas move towards truthiness

Metadata

Data supporting data

Data has metadata — data about the data. It helps the data be findable and acts as a low-grade navigation. It’s a data point’s own subset of context, enriching the meaning without formalizing the connectome.

When the data point is already a part of a connectome in use, metadata becomes supplemental information — additional context without having to go through the rigor of developing level, strata, or lens. It helps to triangulate the data and aim towards the quality of truth.

In a way, metadata can be seen as organic growth of data. It’s not formal, and it’s not kept in step with other data constructs to help develop information scent and pattern. Instead, it’s burgeoning where it needs to burgeon. It’s a tiny little interlude of responsible chaos, happy to help if it can help and waiting to see how other nodes of responsible chaos might unfold and work in sympathy to develop a new pattern.

![metadata.png](https://movements.angmadsen.com/u/metadata-D11dlH.pn

People as the primary information context

People touch it all: we are information beings, rooted in physical form

We continue every day to add to our breadth of information and data. We accrue, aggregate, categorize, disseminate, connect, and ultimately try to find meaning and progress.

The vast majority of people are primarily interested in their own cognitive information architecture. We survive by it, by our leverage of it and the perceptive truth we’ve been able to wring out of it. It is the source of our physical, financial, emotional, and spiritual wellbeing. 

More metaphorically, we survive better knowing to avoid tigers outright, how to manage tigers when we can’t avoid them, not being dismissed and laughed at for mistaking a domestic kitten for a tiger, and following our religious rituals and accepted meaningfulness of  how and when to apply the tigeryness symbolically and help us understand our tigery escapades.

Teaching our information is hit and miss for most of us. We rely

The implicit process

We build information architecture to use it

Orientation

Understanding where you are within a context.

Think of it like “you are here X” on a map, or “reading a room”.

Findability

Recognizing what you’re looking for.

Think of it like keeping “red” in your minds eye while looking for a red book.

Navigation

Getting from where you are, to where you want to go.

Think of it like following a scent, or walking towards a distinctive tree in the distance.

Information architecture has been around as long as we’ve been sharing information. It’s in how and why we map our environment, in our language, and even in our expressions. 

Information isn’t something we stumbled upon with the advent of science, or library organization, or information technology. It’s in the foundation of what we tend to mark as the beginning of human civilization: farming. 

We can’t understand that a seed grows into something we can eat without connecting dots of in

Story

The human-centered information architecture

Who

Anyone. But usually we try to aim at a certain population (e.g., personas), focused on orienting them with the information they intend to use.

What

With information, we can focus on the data, or we can focus on the goal. Data is hard to constrain, so usually we focus on the goal. Well constructed, the goal is based on the user, but the goal has often been the intent of the data purveyor.

When

With information architecture, “when” is now. Until it’s needed, it’s waiting. It was built in the past to support the moment it’s needed, it’s efficacy an everyday Schrödinger's cat.

Where

Where it’s at. Information is everywhere, in everything. Wherever it interacts there is connection forming, including in space.

How

Infinite possibilities. Every way in which information is passed – every potential change state — has some kind of underlying architecture.

Why

Infinite possibilities, so inst

Learning is hard work

We don’t actually learn according to the dictates of schedule

Repetition

Serves two masters: aligning cognitive information architecture, and developing memory. 

Easy to leverage on mass scale to sway minds.

Memory

Its primary goal is speed of getting things done and right.

Memory is efficiency. It also used to be our only constant access to information.

Pattern building

Insights come from patterns. If progress is a goal, patterns are a good foundation for studying, testing, and sharing.

People like patterns and can find them anywhere – like finding forms in clouds.

Process

Cognition that depends on the fluidity of change over time, and that by identifying key process points an outcome can be replicated.

When combined with memory, they can form a static, brittle edifice.

Personalization

Be able to visualize yourself in the midst of a problem and solving it – or actual experience. It helps to align it to your personal proces

Why look for something?

We use architecture to make decisions, which affect behaviors and actions

Understanding

Follow an information scent, whether for a use state or curiosity.

Generally, the goal is to scan and drill to the information. Long attention span – consider that a professional sustains for decades across innumerable architectures.

More likely to be scanning the full list and making sense of each categorization; balance memory standards with likely expertise.

Teaching

Repeat structures to trigger memory and support building more memory.

Generally, the goal is to elucidate context and create meaning. Short attention to find – don’t move my cookie – but longer attention to elucidate.

Balance between findability and understanding, with the latter being dependent on education ethos and/or level.

Maintaining

Robust, inclusive, and consistent update.

Mix of findability (get there quick) and understanding (expert level nuance). Needs to be pars

What are we hoping to accomplish?

Align with a goal

Task

• What needs to be done
• The technical skills of the maker
• How it can be accomplished
• Avoid unnecessary overuse of cognitive load

Who

• Personas, all variations
• Personas are strata. They are not intended to be robust or nuanced, but a reminder that there are people-not-you on the flip side of a use. Personas, just like people, are prone to change and periodically should be relensed. Personas can be helpful in contextualization, but as a strata are prone to developing outliers.

Try your best to approach this with humility, including not taking on your source’s certainty. Think about the cognitive load (yes, again).

Constraints

• The task, people, and constraints
• The data: enough to do the task without hitting TMI
• Time: what time can be spent now, compared to known project recalibrations, compared to the likely lifespan of the material. 
• And focus on the whys

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Why do we care about this architecture?

Align to how we manage information in a moment

Memory

• Is finite in the short term
• Long term memory is primarily based on interest 
• Short or long, it’s fickle and fungible
• We will confabulate ‘memory’ to build a better story
• Chunking aids memory
• Managing information-overwhelm aids memory

Meaning

• We want meaning so much that we’ll scent information from steps away and follow it through
• We will impress meaning when we don’t have enough context, and believe it wholeheartedly without further questioning its closeness to right or wrong

Cognitive bias

• We have preformed, well-worn pathways that we lean into for decisions that feel similar enough to be relevant
• They are so quickly engaged we rarely even realize it
• This field, especially, is constantly adding understanding and hypothesis

Network is hard

We’ve started with the internet

All the issues that we have with the internet are the issues inherent in network information architectures. Information is manageable by individuals to a certain scale. Network is far, far larger than that scale. 

To further complicate it, it is individuals who maintain the information, just like the internet today. The complication isn’t with coherence (we do not need a Big Brother application to change all the information at once — it will delete nascent ideas and understandings as non-compliant), but with the who-ness of people.

People manage information according to their own who-ness. Some will depend on data, some will trust emotion first, some will be a reflection of their environment — including the flip sides of abuse, denigration, brainwashing, misinformation, stability points, indoctrination emphasized by acceptance and respect, etc. All of them will be depending on their processing chain, over and over and over again.

Even peopl

Top-down hierarchy

Start at one, move down to many

Top-down hierarchy is intuitive to most of us because it’s the backbone of how we’re taught. We start with the simple. We then expand, back up, repeat, and expand again until we have a knowledge set, impressed on our memory through repetition and the stress urgency of testing.

What tends to happen when top-down hierarchy is the initiating architecture

• If people are filling in an architecture from the top-down, they usually start with an existing set of categories or a core precept. That set might expand, b

Bottom-up approach to hierarchy

Start at many, keep consolidating to one

With a bottom-up approach, someone(s) is taking all the data and forming categories from the data.

I’ve seen people look at the final representation and insist that the defining node is still the single one alone on its line. This is despite any visual signals or verbal/writte

Network overview

Edging closer to the quality of truth

Knowing everything in all the universes is unlikely, so absolutely truth is beyond our capability to ascertain.

Networks are getting closer to the quality of truth. They are capable of expressing the density of the connections within the data.

Networks are complex to learn and understand.  It’s easier to lose your way and not know how to backtrack, and even be surprised when you find the information through a novel approach.

Add different users, different perspectives, and different objectives, and even using the exact same data and underlying network can result in entirely di

Pulling hierarchical structures out of network

Intuitive development of strata

Networks are nearing the quality of truth, but complex to learn and understand. 

Over time, we’ve come to understand that a hierarchical structure to data is easier to consume and disseminate with a higher rate of success for a larger fraction of people, so we’ve leaned into that structure. It’s not the only way to learn, just the most successful for a decent chunk of minds in the quickest timeframe. It may not even be a majority for whom it works well, just the largest chunk we could find and develop a single-form answer

The repercussions of simplifying

Moving networks into hierarchies obfuscates existing connectome

One of the most impactful outcomes for pulling hierarchies out of network is that the actionable data can be got to faster, with fewer missteps, and replicable across a vast swath of minds.

Speed and efficiency comes at the expense of truthiness. A lack in information scent, a different paradigm or culture, and the hierarchy becomes inexplicable and confusing. That’s even before we get to just how much informat

System as resources

Example of complexity: the highly variable network

What I’ve done with the systems model on these two pages is very simplistic.

First, I represented resource tolerance. Lots of space, lots of tolerance. Little space, and it takes very little to make it oscillate and collapse.

All the factors in the baseline state are equidistant around the circle, whether it’s resource-rich or resource-tight. The tolerance is still indicated.

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Understanding system connectome

Overview

The way I’ve explained systems thinking was intended to help ground in a reasonably universal, physical medium that could be explored reasonably easy; then abstract it through a particular lens of information architecture; and then expand again to how that translates to social models.

• Systems flow
• Systems expand and contract
• Systems are elastic

I did it on purpose. It was a framework, pure and simple. Take what someone understands (mental model), and expand. I included information not only to help reframe minds and step into the complexity of our social models, but because how people and information interact is incredibly important to understanding our systems.

They are inextricably linked. People use all the information structures as they use and share information, including the complex dimensional structures. Most people do it to control and contain their own understanding and meaningfulness; and for most people it’s more like controlling

Systems flow

I frequently talk about how the push/pull of information is an indicator that we're dealing with a system rather than a network, but what that push/pull does is move the nodes as well as the data between nodes; which node it moves, when it moves it, where it moves it to are all fungible.

So what happens if a system stops flowing? It doesn't revert to a network. It's too interconnected; other systems in the wide system of our reality are affected by a plugged or flow-compromised system. What we call "system" is rarely the holistic beast, but a small part of the whole  —  even if it's unwieldy, hard to navigate, and complex to understand.

The flow, when diminished or negated, has knock-on scarcity-driven effects to the broader system. Healthy

Systems expand and contract

Why does the push/pull of systems happen?

There is volume involved between and within the nodes of a system. Inside the node, in the liminal space around the node that allows/constrains the potential for expansion, in nodes that will be supplying volume through flow, and in the nodes expecting volume to be passed.

It’s more than simple volume. It’s content and wiggle room, and the ability/need to expand and contract the contents. They don’t stay the same. The data can be reallocated out to a different location, or live in mirrored multiplicity, or dissipate into misinformation. But usually, more data are added. And added, and added some more, because our universe is in a state of expansion, and people are nowhere near catching up.

Systems are elastic

How systems rebalance is through and because of elasticity, and that elasticity in most prevalent in the connectome.

Information is everything. Everything. Our minds, our bodies, our environment, our world, our universe can be understood and shared through information. The most important part of that information is connection. We can’t touch it. Its physical representation is bound up in our node-focused understanding, in unique and (mostly) touchable forms. Connection is how those forms relate. It is borderline ineffable, overwhelming in all the possible connections, and filled with potential.

When elasticity is health

Have I mentioned variability yet?

Insert delighted cackle here. Heh.

Information architecture is not set in stone. Done well, it will live longer than the raw data, which is moving and accumulating through time at the speed of time and the amassing of a quantity of information driven by the quantity of people. But as the data shifts, as our data substrate and understanding grows, the information architecture changes with it. If we approach each information architecture project as the infrastructure it is, we have certain ideas engrained in our culture.

We tend to look at big infrastructure projects as one-and-dones. We built a glorious roadway system in the middle part of the 20th century that is crumbling around us. We built an energy transmission system that is allowed to keep chugging along until it fails, like in the California Camp Fire (2018) or the Texas cold snap (2021).  Old water mains break, and can failed more spectacularly through additional mismanagement like in Flint, Michigan (20

It’s a bet

Information architecture is not a singular state

The supporting data of a node can be expressed as metadata until and after a more patterned architecture is defined. The combination of metadata can have a pattern read into it that emulates something closer to story.

As data is organized, the organizer is making bets on the truthiness of various connectome and future states. These bets are based on the behavior and actions of the sources as well as the behavior and actions the information is trying to support, within the working parameters of the data structure, process, and tech

Hierarchy as a lens

Lensing into the underlying network

Networks are closing in on the quality of truth, but hierarchies are still a truth — a perceptual truth. Multiple hierarchies into the same network are truths about that network, even if no data point is shared. 

In my work, I look at hierarchies as windows or doorways or lenses. An opening or view is an overall good thing. It effectively chunks an informat

User perspective

Of hierarchy as a view into network

A user couldn’t care less about what isn’t in the architecture, unless it’s the thing they are looking to do or use. Missing what they expect to find will result in frustration and even anger, but weeding through too much can do the same thing. 

If they are on a search for a thing but not positive it should be in a place,  they’ll go somewhere else when they don’t immediately find it. 

In other words, if they are looking for pants and the architecture doesn’t include pants where they expect to find them, they’ll move on in the belief that this is not a pants-available place. They are on the in

Quantum physics of the mind

Attention is magic, yet not strong enough to delay expanding impact

When the mind deals with a network, there’s a solid probability that it will chunk that network into more navigable pieces, predicated on the questions being asked. 

The questions can be about the same data, separated from a different data set by an unacknowledged connection. But to the user, focused on chunking the data and navigating it as easily as possible, they are experientially two different data sets.

The true change is attention. The shift in the data is to accommodate attention, the parent of a hierarchy is set by attention, the con

Problem solver or researcher perspective

Of hierarchy as an opening into network

A person problem-solving or researching will consider what has already been folded into a hierarchically-delineated paradigm. When paradigm-knowns don’t solve the issue or explain what’s going on, they’ll expand first along the ‘unattached’ (to the hierarchy they are currently using) but networked data. Eventually they will even consider information that is not included in the hierarchy / paradigm to get what they are looking for.

A researcher

Excluded data still exists

“Junk data” is a misnomer

All this talk of hierarchy and network is about the constructed information architecture. Just because a sentence in a book isn’t in a Mark record or the ISBN information doesn’t mean the sentence is gone. It’s just not reflected in the pathways to find it.

Just because a specific data point isn’t allocated a place in an architecture doesn’t mean the data point doesn’t exist. It means the architecture is not up to the challenge of fitting the data point in. 

If there is a fault, it’s in the architecture. But it’s only a fault if the data meets the criteria of inclusion yet its not there, and the architecture is believed to be holistic. The behavioral aspects of data denial is complex.

There is no su

Perception difficulties of network

Things to keep in mind if network is being structured

It’s hard to use

Wayfinding

Make something findable AND finding it — how to simplify that when everything can be connected to everything else?

Mental mapping

We are attempting to make network from the bottom up with the internet. It’s hard to build a mental map of it — I certainly haven’t been able to!

Digesting

When the connectome is variable, it’s hard to understand where enough information has tipped into too much.

It's hard to support

Information scent

Network requires multiple forms of information scent to navigate, creating more levels in architectures in which to wayfind.

Updating data

It’s easy for information to get stale and still have repositories of old versions, or for misinformation to spread; additional strata develop to manage, adding more level(s) of wayfinding and orientation.

Truthiness scent

Stability points, various i

Why not discard network for hierarchy?

The tripping points of false simplicity

Diminished truthiness

You do not have a single access point to the rest of the world. You have a TV, a phone, the mail, the internet, and all of your friends, family members, and the strangers you will brush up against.

No datapoint is any less rich.

Culling data

Not all of the information will fit in a hierarchy. To make networked data fit, some data pieces will need to be removed. 

Vast swaths of connectome will be removed, and that’s where the bulk of context resides.

Invalidated without a fix

If an aspect of a particular network requires two things from two separate categories in a hierarchy to be set a certain way for the key aspect to work, the best case is heavily annotated hierarchies. The worst case is that you have a false negative solution.

Nonsensical exclusion

What doesn’t fit will be excluded, regardless of whether or not it exists and is in any way touched by the exclusio

Bridging between information and mind

Consider website navigation

To a certain degree, this is a scalability issue. Scalability even in our IT architectures can be a tough nut to crack. Scalability between IT and human cognitive load: massive. 

For instance, take a website with huge information density. It can be news, particular subsets of information, commerce, or even available captured passive data like weather. But if it’s in a website, the use case leans towards the fleeting, with diverse users.

To maintain scannability and memory in a hierarchical information architecture, probably around 60 items could be included, in two levels. This is the best case scenario for a fleeting contact — just enough information scent to land on the right page, not enough to exceed short term memory and scannability of a broad (not holistic!) set of people.

Categorizing 100 items into a hierarchical information architecture, leaving out 5 or 6, is common and not alarming. It will bend a scannability ru

Movements is a book in six parts

Part 1: Introduction

Part 2: Information

Part 3: Who-ness

Part 4: People and time

Part 5: Fractal implications

Part 6: Appendix

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An email drop has been set up at movements. I have no idea what my cadence for checking it will be, or how done I'll get how fast with the inevitable spam and trolls. It's still the best way to potentially get in touch with