Systema: The Six Hats, Six Coats Hypercube

Later in this post we will discuss this man:


The following table represents my interepretation of the Zachman Framework:


I have taken this framework and applied the following de Bono metaphor:


I also incorporated my own metaphor to differentiate the axes:


These two modifications produced the following table:


This is where I had an “aha” moment. I asked myself what the entities would be:


I also recognized that in each column these entities were related hierarchically allowing the creation of a six dimensional hypercube. In creating the hypercube it was possible to look at a variety of “slices”. For example:


The table above combines Motive with Person. We can see that Motive is verbal while Person is a noun.

Next we will combine Function and Data to create another slice:


Again, Function is a verb and Data is a noun.

Let’s look at one final slice:


Here we see that nodes and time have many possible states.

But, why am I doing this exhaustive analysis of the possible combinations in the Six Hats, Six Coats hypercube?

Let’s go back in time for a moment and look at this table:


When Dmitri Mendeleev created this table to describe periodic behaviour of the elements, many of the elements had not been discovered. However, the table projected what the properties of those elements would be making the search much easier.

The Six Hats, Six Coats hypercube is also a form of periodic table. Its entire collection of possible cells are called the framework space. Many of the cells in the hypercube do not yet exist, however their properties can be predicted. This makes their search and discovery of system components systematic instead of random or organic.

Related Posts:

Systema: Seven Hats, Seven Links

Science: Know “Why”


I am currently taking a break to read James D. Watson’s new book, Avoid Boring People.

Here’s a quote dear to my heart and core to effectively using the Six Hats, Six Coats metaphor:

Knowing “why” (an idea) is more important than learning “what” (a fact)

World Almanac facts, such as the relative heights of mountains or the names of British kings, go you nowhere at Hutchin’s college. The essence of its educational mission was the propagation and dissection of ideas, not the teaching of facts often best left to trade schools. Why the Roman Empire had risen and fallen was much more important than the birth date of Julius Caesar. And why the European cathedrals were built mattered much more than their relative sizes. Equally unimportant were the details about the French Revolution when contrasted to the philosophical ideas of its eighteenth-century Enlightenment, whose emphasis on reason as opposed to theological revelation greatly accelerated the development of modern science. Likewise, details of Linnean taxonomy paled in significance to the idea of biological evolution, whereby all life-forms have a common ancestor. Better simply to know which books hold the details you will need than to overload your neurons with facts that later will never need to be retrieved.”

I’d like to add the following corollaries:

  1. Knowing “why” is more imortant than learning “who”
  2. Knowing “how” is more important than learning “what”
  3. Knowing “when” is more important than learning “where”

All this considered, James recognizes the importance of new facts leading to new ideas. He gives as an example Darwin’s journey on the HMS Beagle that led him to discover the geographical patterns of the distribution of species and the fossil record that led to his theory of the evolution of the species. “Sometimes a new idea can flow from old facts rearranged, but more typically it comes when new things previously unknown and unaccountable for under the old theory are introduced.” Induction has its place.

Systema: CI-DIKW Hierarchy Definitions

I have been wanting to clearly define each of the terms Data, Information, Knowledge and Wisdom for some time. I have thought about Artificial Intelligence, Knowledge Bases, Knowledge Management, Data Management and other disciplines and have decided on the following simple definitions:

  1. Wisdom is the ability to model entities in a system. This is extrapolative.
  2. Knowledge is the ability to model relationships in a system. This is interpolative.
  3. Information is the ability to model attributes in a system. This is intrapolative.
  4. Data is the ability to model constraints in a system. This is extrapolitive.
  5. Intuition is the ability to model definitions in a system. This is interpolitive.
  6. Communication is the ability to model manipulations to and from a system. This is intrapolitive.

I have been forced to come up with the root “polite” to describe a single input value as opposed to “polar” which is a collection of input values. But what I want to point out is there is no automated tool capable of creating new models of communication, intuition, data, information, knowledge or wisdom, as simply defined as this is, that can be regarded as “intelligent.”
The above six perspectives affect the following focuses or modeling languages:

  1. Motivation Modeling
  2. Network Modeling
  3. Data Modeling
  4. Process Modeling
  5. Person Modeling
  6. Time Modeling

The perspectives CIDIKW and focuses MNDPPT make a thirty-six cell framework I call the Six Hats, Six Coats Framework. What I am pointing out here is that no system is simply one dimensional. Human systems are six dimensional at least. There is also a meta-layer, the model, and a data-layer, the database, for each dimension. The modeling systems and databases for all the dimensions are still very primitive and incompatible. Slowly, we are getting there, but there is more than enough work out there for anyone who wants to come up with a consistent modeling language. And if you do, you will have the foundation for a true AI.

Systema: Exteroception and Interoception


I was reading an article in Scientific American MIND this evening which discussed the research of Hugo Critchley on emotional intelligence and interoception. Interoception which is narrowly defined as the perception of stimuli inside the body. Interoception activates the brain’s right insular cortex and has lead Critchley to a broader definition of interoception. The reason being that not only perception of internal stimuli, but of emotion results in intense activity in the insular cortex. It has also been found that another center in the brain associated with ideas, motives and values often shows activity in conjunction with insular cortical activity. The right insular cortex appears to be the location where mind and body meet. Neurologically, you perceive hunger in the same way you perceive anger. You have a physical location for intuition, that “gut feeling”.

So, why do I bring this up? The main reason is I am thinking about Maslow’s hierarchy and the DIKW hierarchy. I have been struggling to substantiate a hexad as opposed to a tetrad for the number of layers of input and output and I think this provides another cornerstone for my argument. Interoception, the perception of stimuli inside the body, and exteroception, the perception of stimuli outside the body, fill the gap beneath data and divide Maslow’s physiological needs. I am proposing the following hexad:

  1. Green Hat: Wisdom. Self-Actualization. Conceptualization.
  2. Yellow Hat: Knowledge. Esteem. Contextualization.
  3. White Hat: Information. Belonging. Logicalization.
  4. Black Hat: Data. Safety. Physicalization.
  5. Red Hat: Intuition. Physiological. Humanization. Interoception.
  6. Blue Hat: Communication. Existential. Detection and Effection. Exteroception.

In my future discussions I am not going to talk about the movement of data or information or knowledge or stimuli. I am simply going to refer to input and output. Input ascends the hierarchy and output descends the hierarchy. From which level it originates is also irrelevant if it is ascending it is always input, if it is descending it is always output. The transformations are discrete. They are not increases or decreases in detail, but changes in perspective.

If you are following this lengthy thread you may notice my terms changing a bit each time. It is an iterative refinement of my understanding that is leading to these changes. Hopefully, I get closer to a final version with each change.

Six Hats, Six Coats and Knowledge Management

I was passed this link to a free Knowledge Management Course by a friend today.

I gave the entire course a read (it is not that long) and concluded that there was only one thing that the course covered that is not covered by the Six Hats, Six Coats as it has been explained so far. The issue is valuation, how do we know the cost/benefit of any fact. Otherwise, the authors wave the term “knowledge” around with little restraint to the point of its being meaningless. If they had it their way, everything would be knowledge. (I’ve been known to rant that everything is objects.)


To perform valuation of the Six Hats, Six Coats Framework, facts are each of the Six Coats columns: Motive, Locale, Object, Method, Person and Moment. Each of these can be reduced to their atomic granularity at the Blue Hat perspective row. One additional row can be added to the bottom, which is the benefit per manipulation. Each of the Six Hats is a row and can be accumulated in a seventh column, which is the cost per perspective. Each cell of the Six Hats, Six Coats Framework has a cost when it is created, but the benefit accumulates with each manipulation of its column at the Blue Hat level and is rolled up to the appropriate cell.

The rest of the Knowledge Management concepts are covered by the Six Hats, Six Coats Framework.

The Six Hats, Six Coats Framework provides not only knowledge. The Six Hats provide:

  1. Green Hat: Wisdom. Conceptualization. Creativity.
  2. Yellow Hat: Knowledge. Contextualization. Relativity.
  3. White Hat: Information. Logicalization. Optimicity.
  4. Black Hat: Data. Physicalization. Pessimicity.
  5. Red Hat: Regulation. Humanization. Anthropicity.
  6. Blue Hat: Conduction. Detectors and Effectors. Synchronicity.

The Six Hats, Six Coats Framework gives a clear definition of knowledge. Meta-Knowledge is the modeled relationships between the each of the entities within a system. This is the entity relationship diagrams for the facts. Knowledge is the actual references between each of the instances within a system. This is the actual database containing the facts. A rule relationship model, a node relationship model, a data relationship model, a function relationship model, a person relationship model and an event relationship model are meta-knowledge. Rule instance references, node instance references, data instance references, function instance references, person instance references and event instance references are knowledge.

“Mentifacts” and “Sociofacts” are obtuse terms. Person associations are extragroup, intergroup, intragroup, extrapersonal, interpersonal and intrapersonal. They are different perspectives a human takes to interaction and the adoption of facts from another system. Motive, locale, object, method, person and moment are all artifacts, better termed entities.

The definitions the course offers: “Know-what”, “Know-why”, “Know-how”, “Know-who” is incomplete and ill defined.

  1. Yellow Hat, Green Coat is Know-why. Contextual Motive.
  2. Yellow Hat, Yellow Coat is Know-where. Contextual Locale.
  3. Yellow Hat, White Coat is Know-what. Contextual Object.
  4. Yellow Hat, Black Coat is Know-how. Contextual Method.
  5. Yellow Hat, Red Coat is Know-who. Contextual Person.
  6. Yellow Hat, Blue Coat is Know-when. Contextual Moment.

Knowledge management is not simply Informal and Formal. Knowledge Management can be Implicit, Explicit, Tacit and Sonit. Implicit knowledge management handles knowledge that is documented and unchanging in the organization. Explicit knowledge management handles knowledge that is documented and changing. Tacit knowledge management handles knowledge that is undocumented and unchanging. Sonit knowledge management handles knowledge that is undocumented and changing.

The Six Hats, Six Coats Framework does not use the metaphor of a factory for knowledge processing. Instead the framework uses a system lifecycle of induction and deduction. The system repeats, refines, records, reports, relates and revises input; and revises, relates, reports, records, refines and repeats output. Only during the relate phase is input or output knowledge.

The concept of knowledge claims, I found intriguing, but confused between what is meta-knowledge and what is knowledge. I could only conclude that a knowledge claim is really a meta-knowledge claim. Validation of references, knowledge, is protected by referential integrity. A meta-knowledge claim would be validated by a corroboration of exceptions.

The quality of meta-knowledge is a question of how well the relationships for the dimensions handle input and output. If the probability of no exceptions is high the quality of the meta-knowledge is high. A change in context is a change in interacting systems and will affect the quality of an entire system’s performance not just one of its dimensions or of only its knowledge.

Validation of a system is not only knowledge validation. Validation of Conduction, Regulation, Data, Information, Knowledge and Wisdom are all necessary excercises. Because no system is completely Implicit, Explicit, Tacit or Sonit there will always be room for normal and exceptional input and output that has not been accounted for.

Knowledge has intrapolative predictive capabilities. Wisdom has extrapolative predictive capabilities. From this course Knowledge Management appears to know little about systems at all.

The course also attempts to use the three layer ANSI model of World, Knowledge, Meta-Knowledge to describe itself. I have no problem with that. However, because of the poor definition of knowledge in the first place the author begins fantasizing about endless additional layers. I have only found there needs to be three layers in every case I’ve tested. There is the world, the referential and the relational layers.

The sixth lesson of the course talks about innovation as the goal of Knowledge Management. I beg to differ. Innovation is a completely different perspective in the Six Hats, Six Coats Framework. Innovation is the Green Hat, conceptualization perspective. Knowledge assists conceptualization, however conceptualization is concerned with the entities of each of the fact dimensions, not the relationships. Relationships are interpolative, they can only exist between entities that exist. Entities are extrapolative, they can come into existence out of nothing and do not depend upon relationships to exist.

As far as the seventh section, Metrics, goes there is ultimately a cost/benefit ratio. All other metrics are irrelevant if the cost/benefit is done correctly. Cost is the expenditure required to build each cell, each model, of the system framework down to the atomic level. Benefit is the profit gained from each manipulation of the system at the atomic level.

“Knowledge Transfer” is the ability of your system to induct another system and then deduct with a profitable outcome.

You don’t need a Knowledge Management Team. You need a System Modeling Team and the Six Hats, Six Coats Framework. “Everything is a system” holds up to scrutiny better than any knowledge management claim.

Listening is Inductive; Speaking is Deductive

After going over the system models in an earlier post I had to revise my thinking and conclude that the Structured Thinking Lifecycle takes on the following character:


What this reveals is the lifecycle of a system is about communication. It also reveals that the Six Hats, Six Coats metaphor is actually a continuum from Repeating Moments to Revising Motives for induction and from Revising Motives to Repeating Moments for deduction.


This is Edward de Bono’s wisdom: “Analyze the Past, Design the Future”. That is all there is to communication. Listening is inductive; speaking is deductive.

Think about this from the perspective of the DIKW hierarchy:


listening is inductive; speaking is deductive listening is inductive; speaking is deductive listening is inductive; speaking is deductive

The Six Hats, Six Coats Framework

“You’ve come a long way baby.” — Virginia Slims

I have been attempting to come up with a means to communicate some of my insights without losing the heart of the Six Hats, Six Coats metaphor. I was sick of repeating the graphic without adding much more content. Finally, I have come up with the Six Hats, Six Coats Framework.

First, let’s refresh on what the Six Hats represent:


REVISE: Conceptualize. Expand Meaning. What are you enhancing or making right? Creativity.

RELATE: Contextualize. Focus on Uniqueness. What is your mantra? Relativity.

REPORT: Logicalize. Maximize Value. What are you normalizing to the limit? Optimicity.

RECORD: Physicalize. Minimize Cost. What is your business model? Pessimicity.

REFINE: Mechanicalize. Humanize Interaction. How do you lower the barriers to adoption? Anthropicity.

REPEAT: Operationalize. Synchronize. Increase Availability. How do you make yourself convenient? Synchronicity.

Second, lets refresh on what the Six Coats represent:


MOTIVE: Motivational. Why? Concepts affected.

LOCALE: Spatial. Where? Contexts affected.

OBJECT: Formal. What? Logics affected.

METHOD: Functional. How? Physics affected.

PERSON: Personal. Who? Humans affected.

MOMENT: Temporal. When? Synchrons affected.

Now, let’s look at some of our concepts in within the Six Hats, Six Coats Framework.

The first is Maslow’s Hierarchy of Needs (rows) and the Zachman Focuses (columns):


Second is McLuhan’s Laws of Media (rows) and the Zachman Focuses (columns):


Third is Moffett’s Universe of Discourse (rows) and the Zachman Focuses (columns):


The Data, Information, Knowledge and Wisdom Model hierarchy (rows) and Zachman Focuses (columns):


Now, we are going to break the rules. Perhaps we will see something we hadn’t considered.

Maslow’s Hierarchy of Needs (rows) and Moffett’s Universe of Discourse (columns):


McLuhan’s Laws of Media (rows) and Maslow’s Hierarchy of Needs (columns):


McLuhan’s Laws of Media (rows) and Moffett’s Universe of Discourse (columns):


Second last, “old reliable”, an abstract representation of the Zachman Framework:


Finally, one I call “Puzzles and Pieces”:


Hope you might see something new. It is sort of an ad nauseum excercise in search of a new pattern. Personally, I am reflecting on the similarity of multiple systems of thought about systems. “Puzzles and Pieces” was the outcome for me so far. The top three rows are the relationships above the individual entities (ie. Networks above Nodes) and the bottom three rows are the relationships below the individual entities (ie. Nodas below Nodes). I had to create some new terms for the focuses of the lower three rows.

See the latest version of the Six Hats, Six Coats Framework here.

Since I have created this framework I have made considerable progress and simplification you can see the result of this here.

relationary six hats, six coats framework relationary six hats, six coats framework relationary six hats, six coats framework