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Knowledge Machine

Cooperative Knowledge working, Anti-Knowledge,

and Radical Knowledge Creation

By Bruce LaDuke

[email protected]

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I wrote a precursor to this book in 1991 entitled “Perpetual Renaissance, The Creativity

Question Answered” and wrote this more comprehensive version in 2003. Some of the material

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because the content is still very useful and fairly accurate. The next generation of this work can

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Table of Contents

License ....................................................................................................................................... ......... 112 Disclaimer .......................................................................................................................... ................ 112

Part II – Radical Knowledge Creation ................................................................................. ..................... 116

Chapter 6 – The Question/Definition Cycle ............................................................................. ..... 116 The Definition of the Question ............................................................................................. ........... 117 Definitions ....................................................................................................................................... . 117

(3+3)-4=2 ................................................................................................................................ ................. 119

Example ......................................................................................................................................... ... 120 Advanced Definition ................................................................................................ ...................... 121 Advanced Definitions .................................................................................................................. ... 123 Key Question .................................................................................................................. ................. 126 The Definition of Definition ............................................................................................................ . 128 Advanced Definition ................................................................................................ ...................... 132 Anti-Knowledge Key ........................................................................................................... .......... 133 Key Question .................................................................................................................. ................. 133 Anti-Knowledge Key ........................................................................................................... .......... 134 Anti-Knowledge Key ........................................................................................................... .......... 136 Example ......................................................................................................................................... ... 137 Key Question .................................................................................................................. ................. 138 Anti-Knowledge Key ........................................................................................................... .......... 139 Chapter 7 – Creativity Fallacies .................................................................................................... ... 142 Fallacy 1 -- Human creativity is divine ................................................................. ......................... 143 Fallacy 2 -- Creativity is seen from the perspective of the person, process or product ............ 144 Fallacy 3 -- Various disciplines utilize creativity and innovation in different ways ................ 144 Fallacy 4 -- Creativity, creative problem solving, innovation and creative methods are separate entities .......................................................................................................................... ....... 146 Fallacy 5-- An idea is an accident ............................................................................................ ........ 146 Fallacy 6 -- Knowledge is stagnant ............................................................................ ..................... 147 Fallacy 7 -- Mankind cannot control the rate of knowledge advance .................................... ..... 148 References: ....................................................................................................................... .................. 148 Chapter 8 - The Sum of Creative Method .................................................................. .................... 149

1.Association, connection, structure, stratification and problem definition .......................... 149 2.Question-related, problem solving ...................................................................... .................... 149 3.Directional or morphological ................................................................................................... . 149 4.Subconscious .................................................................................................. ........................... 149 5.Visual representation ............................................................................................................ ..... 149 6.Holistic .............................................................................................................. .......................... 149

References: ....................................................................................................................... .................. 155 Chapter 9 – Intelligence, Genius, Creativity, and Knowledge Creation .................................... 156 Key Question -- What is Genius? .............................................................................. .................... 156 Definition ..................................................................................................................... .................... 156 Key Question -- What exactly is intelligence? ..................................................... ........................ 157 Definition ..................................................................................................................... .................... 157 Advanced Definition ................................................................................................ ...................... 158 Definitions ........................................................................................................................................ . 159


Anti-Knowledge Key .............................................................................................................. ......... 161 The Creative Science View ......................................................................................................... ...... 161 Anti-Knowledge Key .............................................................................................................. ......... 163 Advanced Definitions .................................................................................................................. ... 164

References .................................................................................................................. .................... 166 Chapter 10 – The Knowledge Creation Engine ............................................................ ................. 167 Advanced Definition ................................................................................................ ...................... 168 Originated Concept ............................................................................................. ........................... 169 Originated Concept ............................................................................................. ........................... 170 Anti-Knowledge Key ........................................................................................................... .......... 171 Anti-Knowledge Key ........................................................................................................... .......... 172 Visualizing the Cutting Edge ................................................................................. ......................... 174 Originated Concept ............................................................................................. ........................... 174 Advanced Definition ................................................................................................ ...................... 175 Future .............................................................................................................................. ................... 176 Originated Concept ............................................................................................. ........................... 178 Advanced Definition ................................................................................................ ...................... 178 Originated Concept ............................................................................................. ........................... 179 Use familiar creative methods like brainstorming to help you exhaust all mental connections on the topic. The active agent in the brainstorming method is “exhaustion of mental elements. Brainstorming is effective because it helps to list/exhaust available options. ....... 180 You should force structure on everything you know about the problem or existing definition using categorization/taxonomy. In addition, one must break the problem down into the smallest mental elements within categories to begin to realize its structure. ......................... ... 180 Anti-knowledge Key ..................................................................................................................... . 180 Exhaustively Question ........................................................................................... .......................... 180 Originated Concept ............................................................................................. ........................... 181 Anti-Knowledge Key ........................................................................................................... .......... 181 Keep in mind that, as questions emerge, they need to be differentiated from known mental elements so that you will be able to visualize the cutting edge. This can be as simple as a matrix; or, for example, placing known information or definition in a square and questions or the unknown in a circle. .................................................................................................... ............... 182 Chapter 11 – The Knowledge Creation Enterprise ................................................. ...................... 187 Advanced Definition ................................................................................................ ...................... 187

.................................................................................................................................................... ......... 188 Advanced Definition ................................................................................................ ...................... 189 Key Question .................................................................................................................. ................. 189 Advanced Definitions .................................................................................................................. ... 189 Key Question .................................................................................................................. ................. 190 Where can an enterprise realize newly created knowledge? ..................................................... .. 190 The Model Knowledge Creation Enterprise ..................................................................... ............. 193 What exactly is management? ............................................................................................ ............. 194 Definitions ....................................................................................................................................... . 194 Enterprise Culture ................................................................................................. ........................... 196 Definition ..................................................................................................................... .................... 197 Advanced Definition ................................................................................................ ...................... 197


Knowledge Creation Embedded in Enterprise Culture ................................................ ............... 199 The Knowledge Creation Process ............................................................................... .................... 202 Historical Idea Collection and Processing ............................................................... ...................... 203 1. Weak Sponsorship, Communication and/or Change Management ................... .................. 204 2. No Link or an Unclear Link to Science and Technology ............................................... .......... 205 3. Insufficient Knowledge Context ..................................................................... ........................... 205 To understand this typical problem we need to better understand knowledge context. ........ 205 Definition ..................................................................................................................... .................... 206 Advanced Definitions .................................................................................................................. ... 206 4. Complaint Overload/Complaint Confusion ................................................................. ........... 208 5. Inability to Cope with Constructive Criticism ............................................. ............................ 208 6. The Voice Unheard Syndrome .............................................................................. ..................... 209 7. No clear processes for idea submission, collection and processing ............................... ........ 210 8. No real or perceived benefit for knowledge creation ................................................ .............. 211 9. Concept Confusion ............................................................................................... ....................... 212 10. A Breakdown in Supporting Management Areas (e.g., Metrics) ....................... .................. 212 11. External/Legal Barriers ..................................................................................................... ........ 213 The Knowledge Creation Cultural Framework Summary ................................. ......................... 213 Anti-Knowledge Key ........................................................................................................... .......... 214 Chapter 12 - Knowledge Machine ...................................................................... ............................ 215 Anti-Knowledge Key ........................................................................................................... .......... 215 Anti-Knowledge Key ............................................................................................................ .......... 217 Key Question .................................................................................................................. ................. 218 Anti-Knowledge Key ............................................................................................................ .......... 219 Definitions ....................................................................................................................................... . 222 Definitions ....................................................................................................................................... . 225 Definitions ....................................................................................................................................... . 226 The Question Machine ........................................................................................... .......................... 226 The Big Loop ...................................................................................................................................... 227 Originated Concept ............................................................................................. ........................... 228 Anti-Knowledge Key ........................................................................................................... .......... 229


Part II – Radical Knowledge Creation

Chapter 6 – The Question/Definition Cycle

The Question of the Question

"Why are we so much better at answering questions than at answering the right questions?

Is it because we are trained at school and university to answer questions that others have

asked? If so, should we be trained to ask questions?" [Or trained to ask the complete set of

right questions in the right way?] -- Trevor Kletz (Analog Science Fiction, January 1994,

p195)

The question has vast untapped intellectual power. Consider the fact that nothing was ever conceived or

ever invented as it relates to science and technology without first passing through the awesome power of

the question.

How can I...? When should we...? Where will it…? Questions are pioneers. Good questions tend to

make us terribly uncomfortable, but questions are little signposts to new knowledge.

Many questions flow from inquisitive young minds. It has been scientifically proven that the majority of all

that we will learn as human beings will be learned in the first few years of our life. It is no coincidence that

this is also the time that we ask the most questions.

Child: "What's a raccoon, Daddy?"...

Father: "It's an animal."

Child: "What's an animal?"

Father: "It's a creature that lot's of times have four legs"

Child: "What's a creature, Daddy?"...

But alas, we grow older, wiser, and too ‘intellectual’ to ask questions. In a society that is founded upon

memorization and intellect, a question really gains no clear advantage. In fact, it brings with it more risk

of failure than advantage. What will my peers think if I ask that question? Will I look like I don’t know

what I’m talking about? In a memorization driven, intellectual society, individuals must be seen as

knowing because it is perceived as weakness to ask questions.


In our present culture, questions are used more like a weapon in guerrilla intellectual warfare. Individuals

ask questions to test peers or to try to gain political advantage over them. Using questions in this way

further deepens the stigma and negativity that surrounds this powerful tool.

The question is the most powerful mental entity on the face of this earth. Questions are vastly under-

utilized in academia.

Everything we know, even the most apparently stable concepts; the simple concepts, the detailed

concepts the scientific, or technological concepts are all subject to the awesome power of the question.

Even staunch physical laws must surrender to strong questions.

Questions are difficult. Questions demand change. Questions demand explanation. Questions demand

new meaning/knowledge.

The Definition of the Question

Forming a question is essential to any human advance, whether it be on an individual level or a social

level. As you will soon see, questions are intrinsic to problems and we will now analyze existing

definitions and formulate an advanced definition. Please note that some definitions have been shortened

to only contain relevant definitions.

Definitions

Prob·lem, n.

A question to be considered, solved, or answered: math problems; the problem of how to arrange

transportation.

A situation, matter, or person that presents perplexity or difficulty: was having problems breathing;

considered the main problem to be his boss. [1]

Prob·lem, n.

[F. probl[`e]me, L. problema, fr. Gr. ? anything thrown forward, a question proposed for solution, fr. ? to

throw or lay before; ? before, forward + ? to throw. Cf. Parable. ]


1. A question proposed for solution; a matter stated for examination or proof; hence, a matter difficult of

solution or settlement; a doubtful case; a question involving doubt. --Bacon.

2. (Math.) Anything which is required to be done; as, in geometry, to bisect a line, to draw a

perpendicular; or, in algebra, to find an unknown quantity.

Note: Problem differs from theorem in this, that a problem is something to be done, as to bisect a triangle,

to describe a circle, etc.; a theorem is something to be proved, as that all the angles of a triangle are

equal to two right angles. [2]

Question, n

1. an instance of questioning; "there was a question about my training"; "we made inquiries of all those

who were present" [syn: inquiry, enquiry, query] [ant: answer]

2. the subject matter at issue; "the question of disease merits serious discussion"; "under the head of

minor Roman poets" [syn: head]

3. a sentence of inquiry that asks for a reply; "he asked a direct question"; "he had trouble phrasing his

interrogations" [syn: interrogation, interrogative, interrogative sentence]

4. uncertainty about the truth or factuality of existence of something; "the dubiousness of his claim";

"there is no question about the validity of the enterprise" [syn: doubt, dubiousness, doubtfulness]

5. a formal proposal for action made to a deliberative assembly for discussion and vote; "he made a

motion to adjourn"; "she called for the question" [syn: motion] [3]

Question, v.

1. call into question; challenge the accuracy, probity, or propriety of; "We must question your judgment in

this matter" [syn: oppugn]

2. pose a series of questions to; "The suspect was questioned by the police"; "We questioned the

survivor about the details of the explosion" [syn: interrogate]

3. pose a question [syn: query]

4. place in doubt or express doubtful speculation; "I wonder whether this was the right thing to do"; "she

wondered whether it would snow tonight" [syn: wonder] [3]


Summary

Problem

A problem is synonymous with a question and is difficult/requires effort. The word problem comes from

the Greek word that means, “to throw or lay before.” In mathematics, a problem is something to be done

while a theorem is something to be proved.

Question (Noun)

A question is an inquiry or query that demands a reply.

A question is an uncertainty about the truth or factuality of existence of something.

Question (Verb)

To question is to challenge the accuracy, probity, or propriety of something, placing it in doubt.

It seems odd that with all of the exacting terms in the dictionary, this term would be so nebulous. The

current understanding is that a question is synonymous with a problem and questions are questions.

When the definition for a question starts with “a question” one must wonder if it has truly been defined

within society.

We can establish from these definitions that a problem is simply a question or more typically a collection

of questions formed to seek a definition/solution/answer. Problems are something to “throw or lay

before.” They are synonymous with questions and stand as a bridge to future knowledge.

When many think of a problem, they think of a mathematical language problem like: (3+3)-4.

Using this as an example, we must perform a logical operation on this problem to bring it to solution. We

have established, in our mathematical language, a value for 3, 4 and 2. Understanding these values we

must use a logical operator of addition to quantify the sum of 3 and 3. This gives us six. We then

proceed to apply the logical operator of subtraction. We subtract the 4 from 6 and arrive at a solution, 2.

The solution to the problem equals 2. The final value after the logical operations are performed is 2.

(3+3)-4=2

Assuming the values of the symbols are established, a problem can be reduced to questions:


• What logical operation do I perform first (indicated by parenthesis)?

• What is 3 + 3?

• What is 6 – 4?

Some mathematical problems are so simple that they only contain a single question, for example, 1+1?

(=2).

This concept is often masked by the fact that we learn concepts and can innately answer problems based

on knowledge recollection. Yet, to the individual who first encounters this “problem” it is a series of

component questions seeking to answer a collective “problem.”

Problems are questions and questions are problems, even within natural language, as in the following

example.

Example

A wife asks her husband, “Honey, there is a problem with this faucet, would you fix it?”

The husband embarks on the problem, which is really a collection of questions:

• He turns the faucet on. Is there water running?

There is no water running out (data collection).

• Is the faucet opening obstructed?

Husband looks into the faucet nozzle to see if it is obstructed (data collection).

• Why is there no water?

Husband observes that the faucet is not running (data collection).

• Is there water in the pipes?

Husband opens the cabinet door and feels the pipes to see if they are hot or cold. They are not

hot or cold (data collection).


• Is the water pump working?

Husband checks the pump in the basement to verify that it is in working order. The pump is

operating properly (data collection).

• Is there water coming into the house?

While the pump is running the pressure gauge shows no pressure (data collection).

Husband notices that the water main coming into the house does not have condensation on it,

which is typical when it is moving water (data collection).

• Why is there no water in the water main?

Husband calls the local Water Company and asks this question (data collection).

• Solution/Answer/New Knowledge:

The response is “Sir, there is a construction project in your neighborhood and they severed a

water main with a backhoe. Water will be available in about six hours.”

The husband begins investigation of the problem and using both inductive and deductive logic as well as

personal effort, arrives at a solution.

Whether it be a centrifugal pump on a nuclear generator, a mathematical equation, or the kitchen sink; all

problems can be reduced to questions. Just take a moment and think of any problem in your mind and

reduce it to a set of questions on a blank sheet of paper. All problems can be broken down into individual

questions.

So then, what is a question?

Problems and questions arise when there is an absence of logical knowledge structure.

Advanced Definition

Question/problem

The question/problem is the mental realization of the absence of knowledge/knowledge structure or the

perception of the existence of an unknown, which does not yet have structure/meaning/definition/solution.

Knowledge Machine/Bruce LaDuke 2

If the mind does not discern or realize this lack of structure, the question does not emerge.

Two Questions Types

There are two sides to every question. -- Protagoras, From DIOGENES LAERTIUS, Lives of

Eminent Philosophers, Protagoras, bk. IX, sec. 51

For centuries, the concept of the question has received little scholarly attention, and has been shrouded

in both wonder and mediocrity, yet questions continue to be a pervasive aspect of our existence.

1. Who invented the cotton gin?

2. What type of steel can withstand the highest tension?

3. When is the best time to plant corn in Indiana?

4. Where is the highest population density on earth?

5. Why does the earth rotate on its polar axis?

6. How can light be accelerated?

Study these six questions above, which one is distinct from the others and why? The question, “How can

light be accelerated?” is different than the rest of the set.

This question is distinct from the other questions because it asks a question for which there is no known

answer. The other five questions ask about knowledge that exists, but this question is directed toward

knowledge that does not yet exist.

Related to this, society perceives the concept of the problem as operating in two distinct ways,

preconceived and investigative.

Preconceived problems are learning problems. They have been set up in a pre-conceived manner in

order to produce a learning result.

For example, when an instructor gives students a mathematical problem with a known answer, someone

has already found a solution. But to the student, the logical process is hidden and must be found. The

student questions the problem along the same line of reasoning as the original problem solver.

As another example, if a scientist constructs a maze and then a rat is placed in that maze to find its way

out, then the knowledge concerning the solution to this rat’s "problem" existed prior to the tested subject's


exposure to that problem. While the knowledge existed, the subject tested did not have access to the

knowledge of the solution.

Investigative problems, on the other hand, are problems that arise at the “cutting edge” of human

knowledge. The answers/solutions to this type of problem create new knowledge.

Advanced Definitions

Learning Questions

Questions about knowledge that already exists.

Knowledge Creation Questions

Questions about knowledge that does not yet exist.

Problem

A collection of questions.

Learning Problems

Problems created from knowledge that exists.

Knowledge Creation Problems

Problems that seek out knowledge that does not yet exist.

The question is the bridge between that which is known and that which is not yet known. But there are

two types of unknown:

1) That which is known by society but not known by the learner.

2) That which is not known by society or the learner.

As the bridge to both the learning and knowledge creation worlds, the question is a powerful intellectual

entity. Let’s look in more detail at each question type.

Learning questions


Learning questions are at the cutting edge of the individual learning process. For the two year-old this

might be as simple as “what is hot?” while for the physics student it may be a question like “what is

quantum mechanics?” In either case, the question thrusts the learner into knowledge that exists.

Learning questions serve as little explorers of existing knowledge. Study is closely tied to questioning.

When a person studies existing knowledge, they are transferring existing knowledge structures from

society. In order to do this the learner must create logic from any questions that arise. As long as the

learner has questions, the knowledge has not been completely incorporated.

Learning questions direct individuals and social groups to existing knowledge. For example, a young

child asks his or her parents why the moon shines at night. The knowledge freely exists in the social

knowledgebase and many individuals have already learned this knowledge. The child, by asking this

question, has initiated the learning process. The question itself was not learning, but did bring the child to

an awareness of existing knowledge.

It is important here to quickly look at the search engine, but in the context of these two question types.

The closest machine equivalent to asking a question is a query. A query is a computer term for a request

to recall specific information that matches noted requirements or stipulations. In the typical query, the

system is asked to recall data or knowledge from its database or knowledgebase. The system returns a

group of positive results for the stipulations of the query or provides a “no results” answer.

An Internet query, or search engine search, will return knowledge that exists. Search engines cannot yet

return knowledge that does not exist.

Anti-Knowledge Key

A systems query as we know it today, is directed at existing knowledge and asks learning

questions. A knowledge creation query does not exist.

Knowledge Creation Questions

Knowledge creation questions are pioneers of new knowledge. In the knowledge creation question is

wrapped up all of the intellectual power of the universe. Knowledge creation questions arise when a

person realizes a lack of structure that relates to existing knowledge.


Knowledge creation questions are not the entire process of creating new knowledge, but these questions

indicate areas of potential intellectual advance and as such are situated beyond the cutting edge.

Consider the fact that absolutely nothing would be known without first passing through this question type.

Indeed, understanding the mechanics of this question opens the door to accelerated and even

mechanized knowledge advance.

It is natural to focus on the known, since our senses naturally take us there. The unknown requires

creativity and innovation to even reach, and neither of these is sensory. As more and more knowledge is

amassed, it becomes even more difficult to focus on the unknown. The complexity of the known can

easily go beyond the individual’s capacity to retain the same in his or her intellect. As such, exploration of

the known can take a lifetime in and of itself.

All questions beyond the cutting edge are knowledge creation questions, but only those on the cutting

edge can be immediately converted to new knowledge.

Figure 6:1

Knowledge is created from a structural context. The end of this structural context is the cutting edge.

No one ever solved a “cutting edge” problem (creating new knowledge) without first understanding the

context of the problem. The context is all related knowledge required for advance. Sometimes called the

learning curve, an individual must be oriented to knowledge context before he or she is able to

understand how to make significant problem solving contributions.


Knowledge creation questions that extend far beyond the cutting edge are known as theory. Theoretical

questions are typically foreign to individuals oriented to an existing knowledge context.

Theoretical questions can, however, serve as a type of a knowledge goal or roadmap that points to new

knowledge.

The knowledge creation question points to new knowledge like a road sign. Understanding how to find

them and what to do with them once they are found, is paramount to the rapid advance of society. The

concept of anti-knowledge, which will be introduced in detail later, points to the existence of these

questions and facilitates their translation into new knowledge.

Key Question

The concept of two question types seems so simple, why hasn’t it been realized?

Identifying the question types is elusive because people hold different subjects and levels of

knowledge in the individual intellect.

To illustrate, consider an individual looking at the set of six questions we introduced earlier in this

chapter:

1. Who invented the cotton gin?

2. What type of steel can withstand the highest tension?

3. When is the best time to plant corn in Indiana?

4. Where is the highest population density on earth?

5. Why does the earth rotate on its polar axis?

6. How can light be accelerated?

Imagine that this individual comes to the conclusion that the answers to only four of these

questions exist. In reality the answers to five exist, but this individual is only personally aware of

answers to four of the questions and assumes that the other items are unknown by anyone.

On the converse, it is possible for an individual to have personal knowledge that society is not

aware of. Some individual may indeed have discovered how to accelerate light and I, in writing

this book, am unaware of that this knowledge exists because for whatever reason, it has not yet

been delivered to society.


Complexity is added to question identification by four primary factors:

1. Knowledge may be in a social knowledgebase that individuals have not learned or are

not aware of.

2. Individuals and/or social groups can choose not to deliver this knowledge or fail to deliver

this knowledge.

3. The knowledge that exists is not accessible.

4. The knowledge that exists, but is discounted because it cannot be understood.

If you now consider that every question and every knowledge concept is subject to these same

factors with a different mix for each individual and social group, it becomes much easier to

understand why this concept of two question types has been shrouded in mystery for centuries.

To further illustrate this concept, imagine that all the knowledge in world society was contained on

4x6 index cards and stored in a massive building. The contents of that building would represent

the knowledge of that society. Individuals could interact with the knowledge filing system of the

building to strengthen their individual intellect, but as a rule the cards must stay in the building.

At times an individual may come up with a concept that is not housed in the building. In this case

the individual has a choice. He or she may hold the knowledge in the personal intellect, or deliver

the same to the filing system to be recorded and stored in the building. Once it is stored there it

becomes a “social known” though still many people are not aware of its existence, particularly if

its existence is not broadly communicated by, for example, a new knowledge memo from the

building.

Regarding questions, if an individual asks a question of the keepers of the knowledge contained

in the building, he or she would need to locate, or be instructed on how to locate, the piece of

paper that holds the answer. As long as the card is in the building, even if it cannot be located, it

is a learning question.

If the same individual asks a question of a topic that was not contained on a card in the building,

such a question would be directed toward knowledge that does not yet exist, at least publicly (in

the building). This is a knowledge creation question.

Consider in this same scenario, how difficult it would be to discover that the knowledge is not yet

known. There are literally billions of cards in this building and searching for a possibly “new”


concept is a daunting task. Of course, computers make instantaneous searching possible, but

would not guarantee the logic and integration of the knowledge filing system.

Great confusion has evolved from a simple misunderstanding of these two question types. Learning and

knowledge creation are often confused and knowledge creation is often ignored as a result.

While questions are powerful, they are not independent. Questions have a partner in the definition.

These two cooperate to create new knowledge in a cyclical and definable process. To understand this

process, we first need to understand the definition.

The Definition of Definition

The dictionary is not strictly a modern concept, but its modern foundation date back to 1721:

"Dictionaries were produced in China, Greece, Islam, and other complex early cultures. The first modern

examples of lexicography are thought to be Nathan Bailey's Universal Etymological English Dictionary

(1721) and his larger Dictionarium Britannicum (1730)” [4]

Later and most notable, Webster's American Dictionary of the English Language, was a compilation of

70,000 definitions for words in the English language. Little did Noah Webster realize at that time that his

work would develop into an irrefutable bulwark of world society. The popularity of the work was immense

as it recorded the meaning of most of the significant terms in use by society at that time.

But as technology arose and knowledge began to multiply, the multi-volume encyclopedia developed

(1891) as a new resource. The encyclopedia grew larger in size as knowledge diversified into multiple

disciplines. Many of these developed their own independent body of knowledge and relevant dictionary of

terms (e.g. the medical dictionary).

With the development of the computer, knowledge expanded exponentially till it reached the height of vast

multidisciplinary and interactive knowledge bases we have today, with hundreds of fields of study

advancing existing terms and generating new terms daily.

How does one go about defining the definition? Let’s look at current definitions.


Definitions

Def·i·ni·tion, n.

1. a. A statement conveying fundamental character.

b. A statement of the meaning of a word, phrase, or term, as in a dictionary entry.

2. The act or process of stating a precise meaning or significance; formulation of a meaning.

3. a. The act of making clear and distinct: a definition of one's intentions.

4. a. The clarity of detail in an optically produced image, such as a photograph, effected by a combination

of resolution and contrast. [1]

Definition

1. The act of defining; determination of the limits; as, a telescope accurate in definition.

2. Act of ascertaining and explaining the signification; a description of a thing by its properties; an

explanation of the meaning of a word or term; as, the definition of “circle;” the definition of “wit;” an exact

definition; a loose definition.

Definition being nothing but making another understand by words what the term defined stands for. --

Locke.

3. Description; sort. [R.] ``A new creature of another definition.'' --Jer. Taylor.

4. (Logic) An exact enunciation of the constituents which make up the logical essence.

5. (Opt.) Distinctness or clearness, as of an image formed by an optical instrument; precision in detail.

Syn: Definition, Explanation, Description.

Usage: A definition is designed to settle a thing in its compass and extent; an explanation is intended to

remove some obscurity or misunderstanding, and is therefore more extended and minute; a description

enters into striking particulars with a view to interest or impress by graphic effect. It is not therefore true,

though often said, that description is only an extended definition. “Logicians distinguish definitions into

essential and accidental. An essential definition states what are regarded as the constituent parts of the

essence of that which is to be defined; and an accidental definition lays down what are regarded as

circumstances belonging to it, viz., properties or accidents, such as causes, effects, etc.”--Whately. [2]

Definition

n 1: a concise explanation of the meaning of a word or phrase or symbol 2: clarity of outline; “exercise

had give his muscles superior definition” [3]


Solution

2. The act of solving, or the state of being solved; the disentanglement of any intricate problem or difficult

question; explanation; clearing up; -- used especially in mathematics, either of the process of solving an

equation or problem, or the result of the process. [2]

Solution, n.

2: a statement that solves a problem or explains how to solve the problem; "they were trying to find a

peaceful solution"; "the answers were in the back of the book"; "he computed the result to four decimal

places" [syn: answer, result, resolution]

3: a method for solving a problem; "the easy solution is to look it up in the handbook"

4: the set of values that give a true statement when substituted into an equation [syn: root] [3]

Summary

Definition

A statement of fundamental character or meaning

The act of stating precise meaning or significance

The act of making clear and distinct

A concise explanation or description of the fundamental character, significance or meaning of a word,

phrase or symbol, by its properties, to make it clear and distinct

An exact enunciation of the constituents, which make up the logical essence

To determine limits or boundaries, scope

Solution

The disentanglement of any intricate problem or difficult question (answers a question) that results in an

answer, explanation, result, clearing up or resolution

A statement that solves a problem or explains how to solve a problem

In mathematics, the process of solving an equation, problem or the result of the process. The set of

values that give a true statement when substituted into an equation


Summary

• A definition provides meaning

• By implication, a definition brings clarity and therefore it also answers questions

• A solution solves a problem

• A solution answers a question

• A definition enunciates the logical essence

As we summarize these existing definitions, it is easy to see the circular logic contained in them. Behind

all of this confusion is hidden a single, unified process that drives all social advance.

Both solution and definition answer questions, clear up a process or a problem and advance knowledge.

Both the solution and the question provide meaning, though often from the standpoint of different types of

languages (mathematical vs. natural language). In reality, a solution is a definition and a definition is a

solution.

Recall now our earlier anti-knowledge:

Anti-Knowledge Key

Everything intelligible has meaning. Once a group of unintelligible, unstructured mental elements

(data) is structured, the result is knowledge/meaning/structure/definition/association/pattern

recognition, etc.

Knowledge = meaning. All knowledge has meaning and anything with meaning is knowledge.

In defining or solving something for the first time we create structure/meaning and definition/solution is the

result.


Advanced Definition

Meaning/Structure/Definition/Solution/ Comprehension – The result

Meaning is synonymous with structure and with definition and is the foundation of knowledge, meaning is

a logical conclusion, a pattern recognition, a connection, or an association that can result in an expression

of knowledge.

All knowledge has meaning/structure/definition and the sum of meaning/structure/definition comprises

knowledge.

Anti-Knowledge Key

Through the process of logic/reason/inference we create Meaning/Structure/Definition/Solution/

Comprehension (the result)

While there are nuances in these terms that are slightly distinct, all of these terms represent a knowledge

result.

Advanced Definition

Definition

The concise verbal or visual description/synopsis of any level of knowledge that e.g. makes clear an

object, a process, a topic, a domain, a discipline, a body of knowledge, or any other scope of the sum of

knowledge. A definition is:

• Synonymous with meaning, structure, comprehension, solution (while there are nuances in these

terms as it relates to the language they support, they all represent a knowledge result)

• Constantly advancing

• Involved in a dynamic interaction with the question


The Progression of Meaning

Now let’s expand on our advanced definition and incorporate the progressive nature of knowledge as I

begin to reveal the cycle of definition/question/definition.

New terms, like cyborg, cybernetics, hypertext, Internet, are born every year, and with increasing

frequency. The process appears slow and cumbersome from the vantage point of daily, or even yearly

activity, but terms are being invented and definitions created every year.

In fact, this progression of terms and term meaning is the progression of science and technology. The

term’s science and technology represent the cumulative result of micro level advances in meaning

(advances in terms and definitions).

Anti-Knowledge Key

The speed at which terms are created is the speed of social advance.

When we create/originate a new scientific or technological concept and accompanying term, we

create knowledge.

We are either advancing our understanding of terms, definitions and meaning, or we are dwelling

in the history of past creation and discovery.

Key Question

Are definitions stagnant or do they evolve?

Cybernetics is a relatively new term. Is it fully defined? If you ask a cyberneticist, they would say

no, because it is a fairly young discipline. But will it ever be fully defined? The term was

originated several years ago and has continued to evolve since then. The term will continue to

evolve until it migrates into another level of understanding and a new term is originated to take its

place.


Consider the term ‘computer.’ Have we fully defined the term? Are computers continuing to grow

and evolve? At some point in our future will the concept of the computer transform into a new or

related term? At this point we don’t know for certain how the concepts and knowledge will

develop, but we can be absolutely certain that it absolutely will change. The meaning will

progress, the object will progress and at some point the term will be revised or replaced.

Even a simple term like rabbit; has it changed over time? The answer is yes. Its habitat has

changed with the infusion of suburbs. Its diet might then have changed. Our understanding of

the genetic makeup might have changed. We now have more knowledge of various species. We

probably now can clone rabbits, so is a cloned rabbit still a rabbit? The rabbit may also be slowly

evolving to a different type of creature because of impacts on its environment. Our understanding

of the rabbit is reacting and changing itself to respond to these various changes. Even if the

rabbit were never going to change, our mental understanding of that rabbit will change. The main

point of this particular example is that literally noting is standing still. Not in our physical or our

intellectual world. Therefore definitions absolutely will evolve.

Everything is in a state of change, absolutely everything. At some point in the future, we will change our

perception around our entire reality, as we know it. The old will still exist, but only as the history of

creation and discovery.

Most individuals see the concept of the question as fluid, but would have more difficulty seeing the

concept of the definition as fluid. The definition is a bulwark of mental stability, how can it be fluid? Of

course the etymology of words reveals a slowly evolving meaning and representation within each and

every term.

Anti-Knowledge Key

Definitions are in a constant state of flux. Movement is very slow and sometimes is

counterproductive to knowledge, but these continually change into a different (higher or lower)

order with the passing of time.

But movement of a term is not always progressive. Sometimes terms move across disciplinary

boundaries, cultural boundaries, or other boundaries and change in meaning, but do not advance the

term in so doing. Other times a term may actually go into error or falsehood and not progress.


More often than not, movement equals a loss of meaning or the addition of confusion. Disciplinary

boundaries make it very difficult to realize progression. Terms may be originated in one disciplinary silo

and then migrate over to another discipline with either new words to describe the concept or a change in

meaning (see Figure 6:2). The entire scenario can be very, very complex.

Figure 6:2

The Question/Definition Cycle

There is a mental cycle that is so pervasive that it impacts the entire fabric of human existence. But in

spite of its pervasive nature, the evidence of this cycle often takes decades to manifest. Because it

moves painfully slow, the cycle has remained hidden for ages. This cycle is the cycle of the definition and

question.


Anti-Knowledge Key

Knowledge and intellect in our world culture is valued far above the question, but in reality, knowledge

and the question are partners in the cycle of knowledge creation.

We ask questions around defined concepts; we answer these questions and thereby advance to

advanced definitions/solutions.

It is interesting to note here that across disciplines the many activities are precluded by a definition stage.

For example:

• Systems developers call it requirements gathering.

• Instructional designers call it a performance assessment.

• Reengineering experts call it as-is process mapping.

• Scientists call it problem formulation

• Project Managers call it work-breakdown structure.

• Teachers call it lesson-planning.

• Architects call it drafting.

• Lawyers call it case-building.

• Clinicians call it patient screening.

• Mechanics calls it troubleshooting.

The first step of every mental endeavor in business, research, invention, or anything intellectual is to

define or confirm an existing definition. The second step is to find and ask questions. And the final step

is to advance the definition with solutions to questions, thereby arriving at an advanced level of

knowledge.

The basic realization that one must arrive at is that everything solved or defined, can be further advanced.

In effect, there is nothing that we know (no matter how new or unique) that cannot eventually be

transformed into something more advanced.

The door to new knowledge is in asking questions about existing definitions/solutions.


Example

The Telephone

Look at the concept of the telephone. A pretty solid and simple concept, one would think.

The telephone is a means to communicate with a person in a different location without physically traveling

to his or her location. Asking a few questions begins to unveil the dynamic, expansive nature of the

telephone concept:

• Are you communicating through a wire, fiber optics or via a wireless frequency?

• Is the communication vehicle, sound waves, microwaves or light?

• How many people can communicate together via one system? Can you conference call?

• Do you dial a number, punch a code, speak to a terminal or simply think about an individual and

thereby instigate a transmission?

• Will you type the words or speak them or speak them and have the computer type them?

• Is there any type of security involved in your communication?

• Is your communication private?

• Can you store your communication?

• Can you report on your communications?

• Does the person you want to contact have to receive your transmission immediately or can it be

stored until a convenient time (voice mail, e-mail, etc.)?

• Can you forward a message from another individual?

• Can you broadcast a message to thousands?

• Can you use the item for home and business?

• Are you mobile while you communicate?

• What mechanism notifies the receiver of your communication, a light, a noise, an icon or some

other signal?

• Can you see the person you are talking with?

• Can you change frequency?

• Can you project a holographic image to the person you want to communicate with?

• Can you communicate without the use of sound waves or words?


• Can you communicate with a form of language that does not rely on sound waves or technology,

i.e. telepathy?

Of course these are but a few of the vast number of questions that have driven and will drive the

expansion of the concept of a telephone. Internet e-mail, chat rooms, web conferencing, streamcasts,

video conferencing, cell phones, and a host of other options are now developing as a new wave of

communication tools is currently being created.

In short, a telephone today is not the same rotary dial telephone of 30 years ago. The definition has

expanded and grown more complex. A telephone is still a concept, but is quickly becoming knowledge

history.

Lingual representation shifts when the manner in which a particular meaning is represented by language

changes. This might be through a literal shift in language that describes the term and it might be through

a gradual shift of the description in the same language. For example, the British use of "s" versus the

American English use of "z" in the term organization (or organization). Over time, the American brand of

English developed to use a different descriptor (essentially the same language) for the same meaning.

Not only is language evolving, but even more importantly, meaning is also simultaneously evolving. It is

in this often gradual evolution of meaning that the question/definition cycle is constantly circulating.

Word meanings change as the concept these describe change. Terms found in the original Webster's

dictionary are at times quite different than the accepted meaning today. In addition, new terms have

continually developed to help represent the additional complexity in concepts.

Concepts evolve and language also evolves to keep pace with this evolution of meaning.

Key Question

How then does meaning evolve?

Meaning evolves through the cycle of the definition and question. As an existing concept is

scrutinized, questions become evident. As questions around a particular concept are answered,

meaning evolves and eventually this evolution of meaning impacts the definition of the term

describing the concept.


Definitions and questions continually cycle in this manner. Because most people are fully unaware of this

cycle the rate and consistency of the advance of meaning is irregular. This seemingly random nature of

thought has acted for centuries as a cloak for the cycle of the question and definition and kept it hidden

from scholarship.

Intellectual advance is not random, but is instead unwittingly purposed and methodical. This cycle is a

cause and effect process. As questions are uncovered and answered, potential knowledge is formed.

The question both identifies the lack of logic and prompts for advance of logic. All questions test the logic

of a particular definition and drive its expansion.

Anti-Knowledge Key

The question is the vehicle whereby we harness and stabilize data collected which is structured

through logic into knowledge.

Between the stability of defined knowledge and the vastness of sensory data lies an arbitrator with great

power. The question is more than a creativity method, but rather it is the mediator between the social

knowledge base and the random universe.

Definitions are not stagnant, but ever changing and evolving into higher and more complex forms. And at

the leading edge of this advance we find an array of questions aimed at the existing standard.

Questions identify immediate and pressing opportunities for knowledge advance, opportunities for

“concept origination,” but these questions are intrinsically tied to the existing definition.

While it is important to define a problem and ask questions for knowledge advance, it is equally important

to realize that the questions are irrevocably linked to a foundation of existing standards, for without the

definition there would be no question to ask.

In 1999 I published an article in the American Creativity Association (ACA) newsletter on the concept of

the spoon. At that time, at least to my knowledge at that time, there was no such thing as the portable

pudding or yogurt packages and the many other types of self-dispensing containers that are available

today. Below is an excerpt from this article, see the text in bold.


“Think of the word "spoon," an eating utensil...quite stable, or so you might think. Over the years

spoons have taken on many forms; soup spoons, salad spoons, ladles, teaspoon, tablespoon,

baby spoon, measuring spoon, strainer spoon, wooden spoon, silver spoon, plated spoons, etc.

Consider of all the knowledge that exists to support (or define) even such a simple concept;

knowledge of metals, plastics, wood and knowledge for combining materials; knowledge of

manufacturing equipment (e.g. mold, stamp, etc.) and processes (e.g., quality control, resource

planning, packaging, etc.). Knowledge of marketing (e.g. how many to whom and of what

material). But with all this knowledge of the spoon and its manufacture, we cannot assume that

society has arrived at an immovable standard. For where there is a standard, there is potential to

advance that standard; which leads us then to the awesome power of the question.

What is a spoon? A simple definition might be a utensil used to transfer food in somewhat liquid

or unstable form to the body. Are there other ways we might transfer this type of material to the

body? Can we change the design of the spoon to make this process more effective or

comfortable? How else could we make the spoon? Is the handle effective? What are the

drawbacks of the current handle? What are the drawbacks of the spoon itself? Can we

substitute a large diameter straw for soup? Can we change the food itself to be capable of

ingestion without utensils (e.g. in tablet form)? From what other materials could we make the

spoon to make it more cost effective, comfortable or efficient? What material would allow the best

ease of clean up (e.g. Teflon coated)? What material would best resist heat? Indestructible

spoons? Could we make spoons of inexpensive, disposable and biodegradable material?

Microwavable spoons? Freezable spoons? Spoons that hold content in the handle?

Containers designed to transfer material to the body without a separate spoon (spoon

essentially built in)?”

After reading this excerpt, you can plainly see that the term, which at the time was very much a bulwark of

immovable definition, when challenged by the power of someone’s question, emerged with new meaning.

Today, spoons for some products are not necessary, perhaps in the future, there will be indestructible

spoons? We might want to subject the spoon to further scrutiny via questions and open up a world of

possibilities. Dissolving spoons? Self-washing spoons? Dual purpose spoons? Environmentally friendly

spoons? The possibilities are vast.

Consider of all the knowledge/definition/structure that exists to fully define even such a simple concept:

• Knowledge of materials (metals, plastics or wood/tolerances, pliability, heat resistance, etc.)

• Knowledge of toxicity

• Knowledge for combining materials


• Knowledge of manufacturing equipment (e.g., CNC mill, mold, stamp)

• Knowledge of manufacturing processes (quality control, resource planning, packaging, etc.)

• Knowledge of marketing (e.g. how many to whom and of what material)

But with all this knowledge and more, we cannot assume that society has arrived at an immovable

standard with regard to the spoon. For where there is a standard, there is potential to advance that

standard.

There are an equal number of questions to match this knowledge. This is the cycle of question and

definition that is central to all knowledge advances. This same cycle is repeated in countless scenarios

every day. We must pass through the question to advance definitions; over and over and over again.

References

[1] The American Heritage® Dictionary of the English Language, Fourth Edition, Copyright © 2000 by

Houghton Mifflin Company. Published by Houghton Mifflin Company. All rights reserved.

[2] Webster's Revised Unabridged Dictionary, © 1996, 1998 MICRA, Inc.

[3] WordNet ® 1.6, © 1997 Princeton University

[4] The Concise Columbia Encyclopedia, "Dictionary", pp. 232, Copyright 1983 by Columbia University

Press, ISBN: 0-380-63396-5

[5] “Definition, Question and the Creativity Engine,” American Creativity Association Focus Newsletter,

Volume 10, No 1, Jan-Feb, 1999


Chapter 7 – Creativity Fallacies

Let’s first review the goal of this book, so you don’t become weary in this somewhat difficult investigation

of terms. It may seem like we are jumping around quite a bit, but very soon all of these disparate

investigations will come together in a single, simple solution.

As you’ve likely notice by now, this book is methodically breaking down the uniqueness of words that

were long seen as distinct. Many words are actually synonyms, even though we define them differently.

Revealing these synonyms often greatly simplifies what were seemingly complex and disparate concepts.

This book has targeted many of these terms with the ultimate goal of arriving at the painfully simple

process of knowledge creation—and to provide you with tools that will radically advance science and

technology in any discipline.

It is impossible to be an expert in, or to reference all of the experts in semiotics, semantics, artificial

intelligence, cybernetics, taxonomy, ontology, cognitive science, computer science, library and information

science, epistemology, etc. In so doing, this work would quickly become a puddle of references and

semantic barriers.

Instead, my primary goal has been to break down this intellectual complexity, discover synergies and

fallacies, and develop a concise vision of the knowledge adventure we have embarked upon.

The term creativity is truly a key in this effort. It has literally hundreds of definitions across disciplines. It

also has many hidden synonyms. Thousands and thousands of books have been written to try to define

it. But in this book I will contend that the vast majority of these efforts are confused because they have

not understood the term across all disciplines and in a truly holistic sense.

Creativity Fallacies

A fallacy is a misconception or error that results from incorrect reasoning or poorly structured logic. Many

commonly accepted precepts around creativity are actually fallacies and are hindering our progress in

understanding the term. This section deals with several of these fallacies in detail and provides a brief

explanation for why each is false. These will be further validated in subsequent chapters and will serve

as a foundation for the new look at human creativity, which I intend to present.


Fallacy 1 -- Human creativity is divine

Divine creation is the creation of something from nothing. Divine creation does not imply reconstruction

from, or reorganization of, existing materials.

A work of art, for example is a creation through the reorganization of materials that exist.

So how does human creativity work, is it the creation of something from nothing or the rearranging of

existing elements.

For centuries creativity held with it both a privilege and a stigma. Those blessed with this creativity were

exercising a “gift” in the divine. Creativity was absolutely not a learned behavior or a skill, but was rather

a lofty state reserved for gifted individuals. With this gift often came a stigma that if you were a “creative

type” you could not possibly be intellectual. The two were typically mutually exclusive.

And even when individuals possessed this gift, they were still not necessarily accepted for their creative

ideas. In fact, many were rejected, even burned at the stake, for their new concepts.

Today, in many organizations, new ideas are rewarded and encouraged. However, even today, new ideas

have a mystical air and not always well received. Corporate culture can even foster the perception that

ideas are threatening or counter-productive.

The mysticism that has surrounded human creativity for centuries has actually shielded society from

advancing this term. Why try to understand mysticism? It is obviously beyond human understanding and

pure knowledge of it is unattainable.

While there is a temptation to let human creativity remain mystical, unfortunately we humans are really

“manipulators” of existing reality.

As individuals with creative ideas changed the face of society in a seemingly random fashion, society saw

the revolutionary process and marveled at the progression. Yet they did not explore enough to realize

that these grand new ideas were actually intrinsically tied to existing knowledge.

With regard to intellectual endeavors, humans "create" something from something. They utilize,

manipulate, combine and connect mental and physical elements that exist to “create” new physical and

intellectual products.


As trees exist in nature and are manipulated into various forms to produce building material to create the

product of a house, such are the multitude of physical and intellectual products. We are manipulating our

reality to produce a new product that exists also within that same reality.

It is easy to see that we manipulate physical elements into products because we can see the raw

material, the construction or manufacturing process and the resulting product. But intellectual raw

materials, process and product are not visible, making the intellectual state much more difficult to observe

and creating this shroud of mysticism around creativity.

Fallacy 2 -- Creativity is seen from the perspective of the person, process or product

The prevailing efforts of scholarship in recent history have concentrated on creativity from one of the three

perspectives; the creative person, the creative process or the creative product.

Creativity is a process utilized by persons to create either a physical or an intellectual product. Creativity

is not a person or a product.

One might look at a painting and describe it as a creative work, but it was the creative process, executed

by a human person, that made that work possible. Related to this, one could study creative persons

throughout the next decade, as many have in past decades, and never realize the mechanics of this

process and how it works.

This fallacy probably leans a little toward the realm of a simple distraction, but it is certain that if one does

not see human creativity as a distinct process that specific process can never be ascertained.

Fallacy 3 -- Various disciplines utilize creativity and innovation in different ways

Creativity is universal. The artist, musician and inventor use the same creative process as the engineer,

computer programmer, chemist or technician. Creativity and innovation describe a single process that is

inherent to every discipline and every business enterprise. Indeed it is a process that is inherent to every

human advance.

Think for a moment of what would have been accomplished in human society, if individuals had not been

creative and innovative. Accomplishments like sending a man to the moon or the invention of the

automobile surely come to mind. However, absolutely no human accomplishments or successes could

have been achieved without this universal engine of change.


We would not, for example, have the fork, the ink pen, crop irrigation, quantum physics, ocean liners, a

concept of time, philosophy, telescopes, etc. Without creativity and innovation there would be literally no

advancement within a society. Creativity is the universal engine of intellectual advance and advance in

science and technology. Without creativity we could not discover new drugs or invent new machines; we

could not place satellites in space or create a new cuisine; we could not write a computer program or

paint a picture; we could not walk on the moon or write a song. Someone in human history was the

initiator of each of these concepts. Everything intellectual, scientific and technical had a beginning and its

beginning was creative.

Artists, musicians and inventors are easily labeled as creative or innovative because, again, one can

plainly observe, via the senses, the physical outcome and uniqueness of their particular product. But

keen investigation reveals that creativity and innovation are literally everywhere, intricately laced into the

entire human experience and every human endeavor; science, art, social issues, environmental issues,

education, etc.

Creativity has been described as "the highest peak of mental functions" and "the peak of human

achievement" yet this high honor is typically coupled with a very nebulous definition for the term. This is

indicative of our perception of creativity as a society. We know it is incredibly pivotal and we know it has

very broad impact, but our educational and business enterprise systems are organized into disciplinary

silos that mask its commonalties and cripple its power.

In a public letter dated February 5, 1993 from the National Science Foundation to the prospective

grantees, the following quote was made:

A leading recommendation of the 1992 report of the Commission on the Future of the National

Science Foundation was that NSF should take a more active role in fostering multi- and

interdisciplinary work and partnerships among sectors of the research community since "Nature

knows nothing about disciplinary boundaries." Scholars in astronomy, chemistry, materials

research, mathematical sciences, and physics have identified opportunities for discovery in such

areas as advanced materials, environmental sciences and global change, high performance

computing, complexity and non-linear phenomena, biotechnology, and science for civil

infrastructure and manufacturing. Research in many of these subjects and in others on the

frontiers of knowledge often requires integration of ideas across fields of science, close coupling

of research and education, partnerships between universities, industry, and government, and

bridging fundamental research to practical application. [1]


The statement, “Nature knows nothing about disciplinary boundaries” struck me as a profound. This

person realized that to look at a single aspect of nature was not providing an entirely accurate or even

natural portrait of the problem. The old adage, "we cannot see the forest for the trees," applies here.

To see human creativity, a universal process, one must investigate many disciplines with an open mind to

begin to understand clear ties to a single process applicable to every facet of human knowledge. A

process utilized by individuals in every discipline in exactly the same manner.

In reality, creativity is one, but descriptions of the term vary by field of endeavor. Some are more descript

than others, but all use different terms and vantage points to describe this single process. When all of

these experiences and terms are combined, it is a bit like the blind men touching different parts of an

elephant and reporting back all kinds of things they encountered, none realizing it was an elephant.

If one can step back and look at creativity from a multidisciplinary perspective, the common process that

exists becomes evident. A process that is really quite simple.

Fallacy 4 -- Creativity, creative problem solving, innovation and creative methods are separate

entities

Once again, creativity is one. There is actually only one process for creativity, innovation and creative

problem solving. There are slight differences between creativity and innovation with regard to the logic

employed, but the process behind both is still easily comprehended as a singular process.

Problem solving and creative problem solving use this same process and the literally hundreds of creative

methods in existence tap into this simple, single process. In order to move forward in understanding this

term one must realize that each of these methods holds a component of a much higher level and all-

inclusive methodology. Once a person cuts through the confusion of terms, the sum total of these various

methods reveals the full equation of creativity and innovation.

Fallacy 5-- An idea is an accident

Another common misconception about creativity is that an idea is an accident, a ‘lucky guess’ or

something stumbled into. When people are not really sure where ideas come from, they seem to appear

out of nowhere or just ‘happen’ in a seemingly random and unpredictable fashion.

Of course, people just keep coming up with new ideas. It would be truly amazing to have so many

accidents in such a short timeframe.


To give some credit to the idea accident theory, most are accidental in the sense that the person arrives at

a new conclusion but is not quite sure about the process he or she used to get there. In actuality, behind

all of these “accidents” is a process that was accidentally followed.

Related to this, management and implementation of the ideas is often rewarded and not the origination of

the idea itself. After all, why would we reward someone for something that is accidental?

Even though an idea initiates an effort that results in multi-million dollar savings or earnings, the concept

originator is quite often forgotten or minimized in a mass of bureaucratic detail. The journey resulting

from the concept origination is rewarded and the originator of the concept either switches to implementer

or fades into obscurity.

In addition, accidents are not managed. Why measure or judge the worth of ‘accidents.’ Benchmarking,

background, qualifications, experience and even bluff determine direction far more frequently than the

objective judgment and measure of ideas. Disciplines and enterprises manage projects, process and

overall change. Yet these fail to manage ideas upon which all change and progress is founded. Bad

ideas comprise a bad change, no matter how well the change is managed or implemented. But no matter

how flawless the implementation, implementation has not, and never will, create new concepts. The

process of creativity creates new concepts.

Ideas are a priceless, but severely neglected commodity.

Fallacy 6 -- Knowledge is stagnant

Knowledge is moving. Think of something you know and ask yourself if you know everything around the

concept. Of course, there is always room to grow in knowledge and we have never ‘arrived.’ Knowledge

around all disciplines and enterprises is increasing in volume and complexity and this progression of

knowledge is fueled and/or limited by the rate of creativity and innovation.

Someday we will migrate from everything we know as having a stable meaning into a new realm of

understanding and our current understanding will become historical. But this change is not just

movement from new to old, but is rather a gradual transformation from prior knowledge to new

knowledge.


In any common dictionary, the very foundation of human knowledge, one might see the same words are

there in the same place with the same meaning year after year. But a look over centuries in the

etymological dictionary (A dictionary giving the historical origins of each word) would reveal the migration

of word meanings over time. Language is the vehicle for transmission of knowledge and changes in

language reflect changes in knowledge itself. Knowledge is progressing and is fully dependent upon

preceding knowledge.

Fallacy 7 -- Mankind cannot control the rate of knowledge advance

Most people acknowledge that knowledge within our world is growing exponentially, but few would admit

that this rate of advance could be controlled with mechanical precision. Of course, if one is not privy to

the process that creates this growth and advance, one surely cannot methodically employ this process in

such a way as to control the rate and amount of knowledge advance.

Human creativity is the primary rate-limiting factor of science, technology and knowledge itself. As such,

human creativity is the primary barrier that stands in the way of truly massive intellectual advance.

Corporate enterprises literally spend billions of dollars each year on research and development to fuel this

progression, because they are constrained by their lack of awareness of the creative process. It is quite

expensive to engineer “accidents” as one must rely on the laws of probability to realize advance. Sooner

or later, you’ll discover something…if you try enough times.

But imagine a company (and a world) that fully understood this rate limiter of human creativity and

thereby removed all barriers for the progression of science and/or technology. The impact would be

staggering and a true space age would quickly develop.

References:

[1] National Science Foundation, Letter: NSF 93-13 MPS Multi- and Interdisciplinary Research, Mathematics and

Physical Sciences Division, 2/15/93, File nsf9313, signed by William C. Harris, Assistant Director, URL:

http://www.nsf.gov/cgi-bin/getpub?nsf9313


Chapter 8 - The Sum of Creative Method

Parallel with the exponential increase in psychological research around creativity that began around 1960,

a virtual flood of "creative methods" emerged in the 80’s and 90’s. Many of these methods are now

centric to quality and process improvement, reengineering and other business endeavors. This flood of

working method must work by some logical means, since they all seem to make some contribution and

thereby stay in existence.

This chapter will show you the basic mechanics behind all creative method. While there is some overlap

and not all methods are represented here as examples, current creative methods can be divided into the

following categories:

1. Association, connection, structure, stratification and problem definition

2. Question-related, problem solving

3. Directional or morphological

4. Subconscious

5. Visual representation

6. Holistic

Association, Connection, Structure, Stratification and Problem Definition

Association and analogy are the connection of disparate thoughts. Structuring and stratification connect

related thought. Problem definition is also a type of structuring. By thoroughly defining a problem the

problem is given mental structure. Disparate points are structured into a problem definition. Here are

some examples of methods that connect thoughts:

Forced analogy -- Forcing an analogous relationship between seemingly unrelated items. For

example, ink pens and computer programming.

Imitation -- Imitate other already successful solutions.

Lotus blossom technique -- Start with a central theme and work outward, using ever-widening

circles or petals. [1]


Mind maps -- Start in the center of the page with the main concept, and works outward in all

directions, producing a growing and organized structure composed of key words and key images.

[2]

Morphological forced connections -- A variant of attribute listing, where attributes are listed and

alternatives are listed below each, then various combinations of each are assembled. [3]

Stratification -- The process of separating data into groups or categories according to the values

of one or more variables.

Synectics -- Synectic thinking is the process of discovering the links that unite seemingly

disconnected elements. [4]

Attribute listing -- Breaking a problem and alternatives down into smaller and smaller bits to

ensure that the entire problem has been examined. [5]

Question-Related, Problem Solving

Asking/answering questions is a miniature form of problem solving. These two are often disassociated,

when in fact; both bring mental chaos into structure. Here are some examples:

Ask questions -- Ask the six universal questions who, what, when, where, why and how. Ask

why five times concurrently around the same problem. [6]

Applied imagination -- outlines about 75 idea-generating questions like: Adapt, modify,

substitute, magnify/maximize, minimize/eliminate, rearrange, reversal, combine? [7]

Assumption smashing -- List the assumptions of the problem, and then explore what happens

as you drop each of these assumptions individually or in combination. For example, "What if we

don't close at 5:00pm?

Information question -- The determination of the precise question that needs to be answered by

quality efforts


Function analysis -- Defines current product or process such that one asks "How could I do it

differently?" The answers to this question lead to the necessary new strategies, new product,

and new ways.

Oracles -- Create an oracle (in some cultures, an object of divine inquiry) by asking a question,

generating a random piece of information and interpreting the resulting random piece of

information as the answer to your question. [8]

Neuro-linguistic programming (NLP) -- Experts are carefully studied and modeled as a way to

make conscious and unpack the mental strategies they used to get expert results. Once the

strategies are decoded, they are the available for others to enhance their own expertise. [11]

Directional or Morphological

At the heart of every creative method is change; new directions, new avenues and new thought patterns.

Creative method often forces individuals "out of the box" of current thinking and into a new perspective or

paradigm. Brainstorming is the classic and most popular creative method and is strictly associated with

forcing change. While some methods focus on the change itself (morphological), others are directional

and focus more on the direction of the change.

Brainstorming -- Random generation of ideas, individually or in a group. Participants think up

imaginative solutions and suspend judgment until a list is generated.

Lateral thinking -- Thinking "around" a problem by moving down a path and suddenly taking a

jump to the side. That jump, like the punch line of a joke, places one on a parallel but unseen

and very different path, which in retrospect, is extremely logical. [9]

Pareto analysis -- A ranked comparison of factors that contribute to a quality issue that

separates the "vital few" from the "useful many"

Problem reversal -- Look at things backwards, inside out, and upside down; state your problem

in reverse, change from positive to negative, define what something is not and benchmark what

others are not doing. [6]

Scenario planning -- Using what if statements to anticipate the most likely direction of change


Process mapping -- The mapping of business process using swim lane flow diagrams;

comparing the as is and should be states of the business process.

Random input -- The associations of a new word applied to the "out of context" situation

generates new connections in our mind, often producing an instant "Eureka" effect, insight or

intuition. [9]

SCAMPER -- Mnemonic for Substitute, combine, adapt, modify, put to other uses, eliminate, and

reverse. [1]

Sensation -- "Think Out of the Box" is geared toward simultaneously thinking in the five senses

of sight, sound, taste, touch and smell. Sensation gives us a wider range for thinking, and must

be cued or triggered by some mental device to engage the creative process. [10]

LARC -- Left and right creativity -- The LARC method is step by step process that brings the right

(creative) side of your brain into play with the left (logical) side. The right brain can be stimulated

using drawing and visual images. [12]

Subconscious

As I touched on in Part I, I believe that the subconscious mind is a visual problem solver and that dreams

can be a tool in this process. Suffice it to say for now that several creative methods utilize the

subconscious mind, typically by tapping into it in a relaxed state.

Visual Methods and Visual Representation

Knowledge is a three-dimensional entity, as is vision. It is no coincidence that there is strength in

representing a problem visually. Here are a few examples of visual methods:

Affinity diagrams -- Developed by the Japanese for creating the new language necessary to

think in and about new ways.

Bar graphs, line graphs, pie charts, scatter diagrams, histograms -- A graphic representation

of the variation in a set of data.

Flow diagram -- A graphic representation of process flow


Box plot -- A five-number summary of a set of data. A box encloses the space between the first

and third quartiles and a line dividing the box indicates the median. The highest and lowest

values are shown as the ends of lines extending from the box.

Cause-effect diagram -- A graphical representation of the suggested causal relationships to a

quality problem with the effects of each cause

Drawing and visual thinking -- While much of thinking is based on left brain activity, but the

visual right brain can be used to visualize and solve problems if a visual language is created. [13]

Storyboarding -- Ideas are placed into storyboards and placed side to side such that

relationships and connections become evident.

Holistic Approaches

These “second generation” creative methods are typically some combination of the other categories of

methods listed above; thereby attempting a more holistic approach. It is often a bit of a stretch to refer to

some of these as creative methods, since they tend to bring in other business or cognitive elements. It is

very important to differentiate between what is creative method and what is not. Here are some

examples of holistic methods.

DO IT -- Mnemonic for define, open, identify, and transform. [14]

Six Thinking Hats -- Six hats which are used to metaphorically signify the type of thinking used

by the wearer:

1 White hat thinking; facts, figures, information needs and gaps.

2 Red hat thinking; intuition, feelings and emotions.

3 Black hat thinking; judgment and caution

4 Yellow hat thinking; logical positive.

5 Green hat thinking; creativity, alternatives, proposals, what is interesting, provocations

and changes.

6 Blue hat thinking; overview or process control. [9]

TRIZ -- The Russian-language acronym for the theory of inventive problem solving; a systematic

process applying an exhaustive family of inventive solutions to unresolved tasks [15]


Simplex -- Three stage process: Finding problems; developing creative solutions; and

implementing your solutions. These stages have eight total steps:

Step 1 - Problem Finding

Step 2 - Fact Finding

Step 3 - Problem Defining

Step 5 - Evaluating and selecting (converting selected ideas into practical solutions)

Step 7 - Gaining acceptance

Step 8 - Taking action [16]

Anti-Knowledge Key:

Creative methods, which aim to find solutions and solve problems, generally fall into six categories:

• Association, connection, structure, stratification and problem definition

• Question-related, problem solving

• Directional or morphological

• Subconscious

• Visual representation

• Holistic

This is a very important aspect of understanding creativity, genius and knowledge creation. The fact is

that many, many methods replicate the same, singular process in a variety of combinations, none of

which are fully accurate or universal.


References:

[1] Michalko, Michael, Thinkertoys, Published by: 10-Speed Press, 1991

[2] Buzan, Tony, The Mind Map Book, (authored with Barry Buzan), Dutton, 1994

[3] Koberg and Bagnall, The New Universal Traveller, William Kaufmann, Inc..

[4] Gordon, W. J., Synectics, Harper & Row, 1961

[5] Morgan, Michael, Creating Workforce Innovation, Business and Professional Publishing,

1993

[6] Thompson, Charles, What a Great Idea! Key Steps Creative People Take, Harper

Perennial, 1992

Thompson, Charles, Yes, But.... The Top 40 Killer Phrases and how to fight them, Harper

Business, 1993

[7] Osborn, Alex, Applied Imagination, Scribner's, 1953

[8] von Oech, Roger, A Kick in the Seat of the Pants, Perenial Library, 1986

von Oech, Roger, A Whack on the Side of the Head, Warner Books, 1990

[9] De Bono, Edward, The Use of Lateral Thinking (also published as New Think), 1967

De Bono, Edward, Lateral Thinking - Creativity Step by Step, Perennial Library, 1970

De Bono, Edward, Six Thinking Hats, Little, Brown and Company, 1985

[10] Vance, Mike and Deacon, Diane, Think Out of the Box, Career Press, 1995

[11] Dilts, Robert and Epstein, Todd, Tools for Dreamers, Meta Publications, Cupertino CA

Dilts, Robert and Bonissone, Gino, Skills for the Future, Meta Publications, 1983

[12] Williams, Ph. D., Robert H. and Stockmyer John, Unleashing the Right Side of the Brain -

The Larc Creativity Program, Stephen Green Press (distributed by Viking Penguin), 1987

[13] Edwards, Betty, Drawing on the Artist Within, Simon & Shuster, Inc.

Edwards, Betty, Drawing on the Right Side of the Brain, Harper Collins, 1993

[14] Olsen, Robert, The Art of Creative Thinking, Perennial Library, 1980

[15] Altshuller, Ginrich, Creativity As An Exact Science, Gordon and Breach Science Publishers,

New York, 1988

[16] Basadur, Dr. Min, Simplex: A Flight to Creativity, 1994


Chapter 9 – Intelligence, Genius, Creativity, and Knowledge Creation

The topics of human intelligence, genius, creativity and knowledge creation are as high level as any topic

in historical or modern scholarship. In fact, the arms of each of these terms embrace the entire

intellectual universe and harnessing these concepts means placing one’s foot lightly into virtually every

intellectual discipline.

Key Question -- What is Genius?

An expert is one who knows more and more about less and less. - - Nicholas Murray Butler,

Commencement address, Columbia University

Human history is largely a record of human genius…genius in politics, in art, in music, in war, in science,

in technology, etc. Genius has played a pivotal role in our society throughout the ages, but is little

understood.

Definition

Genius, n.

1: someone who has exceptional intellectual ability and originality [syn: mastermind, brain] 2: unusual

mental ability [syn: brilliance] 3: someone who is very highly skilled [syn: ace, adept, sensation, maven,

virtuoso, hotshot, star, whiz, whizz, wizard, wiz] 4: exceptional creative ability [syn: wizardry] 5: a natural

talent; "he has a flair for mathematics"; "he has a genius for interior decorating" [syn: flair] [1]

Genius here is represented as possessing natural or exceptional intellectual and/or creative ability/skill. A

genius by this definition is either intelligent or creative or both.

If you are in school or remember your school days, the term genius was typically applied to the intellectual

student. He or she was able to commit large amounts of knowledge to memory. In our contemporary

educational system, memorization is focused upon, rewarded and utilized. Amassing knowledge awards

the best grades, the highest recognition and ultimately top jobs. In contemporary society, intelligent

people win the prize.


Key Question -- What exactly is intelligence?

What is this quality found in humans that we reward so highly?

Definition

Intelligence, n.

1. The act or state of knowing; the exercise of the understanding.

2. The capacity to know or understand; readiness of comprehension; the intellect, as a gift or an

endowment. And dimmed with darkness their intelligence. --Spenser.

3. Information communicated; news; notice; advice. Intelligence is given where you are hid. --

Shak.

4. Acquaintance; intercourse; familiarity. [Obs.] He lived rather in a fair intelligence than any

friendship with the favorites. --Clarendon.

5. Knowledge imparted or acquired, whether by study, research, or experience; general

information. I write as he that none intelligence Of meters hath, ne flowers of sentence. --Court of

Love.

6. An intelligent being or spirit; -- generally applied to pure spirits; as, a created intelligence. --

Milton. The great Intelligences fair That range above our mortal state, In circle round the blessed

gate, Received and gave him welcome there. --Tennyson.

Intelligence office, an office where information may be obtained, particularly respecting servants

to be hired.

Syn: Understanding; intellect; instruction; advice; notice; notification; news; information; report. [2]

Summary

• Intelligence is a capacity to know.

• Intelligence is knowledge imparted or acquired, whether by study, research, or

experience

Most people assume that an intelligent person is also creative or a genius. In fact, intelligence is entirely

separate from genius. These are two entirely different concepts. Let’s look at an advanced definition.


Advanced Definition

Intelligence

The human capacity to retain and recall existing knowledge gained from study, research or

experience.

While high intelligence is typically found in those who create knowledge (since knowledge context

is required to create knowledge), it is definitely not a given that an intelligent person will always

create knowledge.

Genius

High volume knowledge creation

Reaching into the unknown and grasping as much newly created knowledge as one can retain

and returning it to society for inclusion in the sum of human intellect.

• Genius is often erroneously attributed to:

• A high level of intelligence

• A high intelligence quotient

• A high profile/high recognition

• A high level of experience

• A large amount of knowledge amassed

• Excellent intellectual marketing

• A noteworthy curriculum vitae

The Intelligence Quotient or ‘IQ’ measures intelligence, but there is no real measure for the

capability of genius. In our world genius is a misunderstood and typically accidental process.

Consider men like Aristotle, Plato, Leonardo da Vinci, Sir Isaac Newton, Thomas Alva Edison and Albert

Einstein. While these men are all considered to possess genius, in the same breath you can also name

concepts that these men originated or knowledge that they created; concepts that far extended the

knowledge of the day and at times extended the knowledge of the ages. Genius is out in front of the


intellectual masses creating knowledge in high volume. Men with high intelligence have been born and

died without significance, but the genius creates intellectual history.

As we will soon learn, knowledge creation is dependent upon past knowledge, or knowledge context, to

bring one to the ‘cutting edge’ of knowledge. Intelligence is a necessary foundation to obtain the

knowledge context from which to create knowledge, but it is definitely not a given that a highly intelligent

person will always create knowledge. In fact, the vast majority of people learn everything there is to know

in their discipline and never enter the realm of knowledge creation.

The genius, however, utilizes intelligence, memory and recollection to tap into the knowledge context of a

certain domain and then utilizes the knowledge creation cycle to create knowledge for society. Attaining

genius is attaining high volume knowledge creation, be it at an individual level or at an enterprise level.

Creativity

We’ve learned a great deal about the term creativity and creative method thus far in this work. Creativity,

as genius, has long held a high, or even divine, position among human endeavors; too mystical to

explore, too magical to understand, too lofty to reach, too complex to explain.

There have been thousands of attempts to define creativity, but most have centered in on the creative

person, process and product. Research has increased exponentially since 1950 [3]. This exponential

interest has also generated exponential confusion of terms and concepts. As in all our chapters, we need

to start with current definitions.

Definitions

Creative

1 Having the power or ability to create.

2 Characterized by originality of thought and execution.

3 Productive [4]

Creative, adj.


Having the ability or power to create: Human beings are creative animals. Productive; creating.

Characterized by originality and expressiveness; imaginative: creative writing.

n. One who displays productive originality: the creatives in the advertising department. [5]

Creative

1 marked by the ability or power to create: given to creating <the creative impulse>

2 having the qualityof something created rather than imitated: IMAGINATIVE <the creative arts>

3 managed so as to get around legal or conventional limits <creative financing> also: deceptively

arranged so as to conceal or defraud <creative accounting> [6]

Creativity

1 the quality of being creative

2 the ability to create [6]

Creative, n.

the ability to create [syn: creativeness] [ant: uncreativeness] [7]

In summary, creativity is defined as simply having the power to create.

Amabile, 1996 [8] defined the early historical approaches to creativity as centering in on the creative

person, process and product. The person in the sense that creativity originates within the individual; the

creative process in that creativity is a process that the person taps into; the creative product in that

creativity can be realized by observing the outcome(s) or product of the term. This view has dominated

research across disciplines.

While persons and products are involved, creativity is strictly a process. Describing creativity in this

fashion is a bit like looking at an automobile assembly line and calling it an automobile or a line worker.

Yes, line workers are involved and yes, automobiles are produced, but the assembly line is neither one of

these.


Anti-Knowledge Key

Creativity is a process by which the person “creates” a new mental or physical product or service.

The Creative Science View

The creative science view emerged with more detail on the term. This view is perhaps best encapsulated

in the following excerpt found on the Australian web site of Charles Cave (emphasis removed) [9] [10].

This site contains key highlights of the creative science view as follows:

• This view reiterates creative person, process and product.

• Creativity includes the ability to take existing objects and combine them in different ways for new

purposes.

• There are three ways to achieve a creative solution: serendipity, similarity (association) and

meditation

• As with thinking itself, “creative” thought is subject to divergent and convergent thinking.

• Creativity is distinct from its application

• People have different levels of creative competency, which implies that creativity is a learned

behavior.

Creativity in other disciplines

Other disciplines also stepped in to add more understanding to the term. The following set of statements

includes some of the high points that can be drawn out of the bulk of creativity research outside of

creative science.

• Creativity is a search for the novel solution and product.

• Creative processes are performed both consciously and subconsciously.

• Creativity is a new organization of familiar components.

• Creative changes may exceed comprehensibility and are thereby in danger of rejection.

• Creativity/innovation is the engine of enterprise evolution and competitiveness.

Again, this view could be encapsulated by the same creative person, process and product, but with an air

of curiosity around the multi-disciplinary nature of the term.


Most of the scientific disciplines concerned with humans, from neuropsychology to economics, have been

called upon to contribute to an understanding of creativity. From whatever perspective creativity is

regarded, there are heated contests among alternative theories, and between the disciplines (e.g.,

between psychology and sociology) the coordination often leaves much to be desired. In addition, the

subject matter itself seems to inspire an individualistic approach that hampers communication and the

steady accumulation of warranted fact and understanding. To bring order into such a poorly unified field,

a useful first step is to select a kind of prototypical creativity. Scientific creativity fits this role best. The

greater part of the literature concerns scientific creativity. When scientific creativity was compared to

other kinds (e.g., in architecture or writing) typically more commonalities than differences were found

(Taylor and Barron 1963). A second unifying principle is that the study of creativity has three focuses also

known as the "three Ps": the creative person, the creative product, and the creative process. [11]

Summary

Creative People:

• Have different levels of creative competency, which implies that creativity is a learned behavior.

Creative Process:

• Is having the power to create

• Takes existing objects and combines them in different ways for new purposes

• Utilizes serendipity, similarity (association) and meditation

• Is subject to divergent and convergent thinking

• Is the engine of enterprise evolution and competitiveness

• Is performed both subconsciously and consciously.

Creativity products:

• A new mental or physical product/service; a solution

• A new organization of familiar components

• May exceed comprehensibility and are thereby in danger of rejection.

Creative Confusion


A multidisciplinary term demands a multidisciplinary explanation. When one views a term that is literally

related to everything from only a single perspective, a full understanding lies dormant.

Creativity is the thread of commonality to all disciplines and enterprises. No matter what the science, the

enterprise or the effort, creativity is there. One might conclude that creativity is somehow tied to

knowledge itself. As we progress you will see that creativity is in fact intrinsic to the knowledge creation

process.

Can you think of another term with the scope and magnitude of creativity? Perhaps the only term with

such reach is the term knowledge itself.

Anti-Knowledge Key

Creativity is related to everything because knowledge is related to everything and creativity is

intrinsic to the knowledge creation process. Knowledge and creativity are partners in the

knowledge creation process.

The process of creativity, outlined above is actually the same process as that of genius and is

also synonymous with knowledge creation!

After this statement, one might become sidetracked at this point with creativity from the standpoint of the

artist, but art is a form of visual knowledge. It progresses from historical artistic or musical products to

modern products. Modern products are extensions of the knowledge of past products. They have no

independent existence outside of history.

Keep in mind that all products are rooted in knowledge. No one could build an automobile without the

underlying knowledge that sustains the product itself. The artistic or industrial product is simply a

reflection of knowledge processes at work. Look at the process steps of creativity from historical

definitions again with comments in parentheses:

• Is having the power to create (knowledge)

• Takes existing objects (knowledge) and combines them in different ways for new purposes (new

knowledge)

• Utilizes serendipity, similarity (association) and meditation (to find new knowledge)

• Is subject to divergent and convergent thinking (as is knowledge)


• Is the engine of enterprise evolution and competitiveness (new knowledge is this engine)

• Is performed both subconsciously and consciously (both the conscious and subconscious minds

contribute to the development of new knowledge).

Recall now the six categories of creative method:




4. Subconscious


6. Holistic

These categories are actually characteristics or sub-processes of the single process of creativity, genius

and knowledge creation!

Also, the creativity fallacies outlined in Chapter 6 are common to genius and knowledge creation:

• Human creativity is divine

• Creativity is seen from the perspective of the person, process or product

• Various disciplines utilize creativity and innovation in different ways

• Creativity, creative problem solving, innovation and creative methods are each separate entities

• An idea is an accident

• Knowledge is stagnant

• Mankind cannot control the rate of knowledge advance

Let’s tie all of these terms up into two advanced definitions


Intelligence

The human capacity to retain and recall existing knowledge gained from study, research or

experience.


While high intelligence is typically found in those who create knowledge (since knowledge context

is required to create knowledge), it is definitely not a given that an intelligent person will always

create knowledge.

Genius, creativity, creative problem solving, innovation, creative method and knowledge

creation

A single process of high volume knowledge creation, utilized by individuals with differing levels

of competency that utilizes existing knowledge and combines it in different ways to create a

new mental or physical product or service.

This process works by one of six methods:

• Association, connection, structure, stratification and problem definition

• Question-related, problem solving

• Directional or morphological

• Subconscious

• Visual representation

• Holistic

This process is:

• subject to divergent and convergent thinking

• is the engine of enterprise evolution and competitiveness

• Is not divine or accidental

• Is the same across all discipline

This process is often erroneously attributed to:

• A high level of intelligence

• A high intelligence quotient

• A high profile/high recognition

• A high level of experience

• A large amount of knowledge amassed

• Excellent intellectual marketing

• A noteworthy curriculum vitae


This process taps into a moving knowledgebase and is the rate limiter of knowledge advance.

The end product is a new mental or physical product/service; a solution that may exceed

comprehensibility and are thereby in danger of rejection.

I realize that reaching this conclusion might seem like a leap in logic, but there are so many

angles to this problem that I need to clarify one facet at a time. The next chapter ties all of

these facets together.

References



[3] The Encyclopedia of Educational Research, Fourth Edition, A project of the American

Educational Research Association, Creativity.

[4] Funk & Wagnalls Standard Desk Dictionary, 1983 Edition, Copyright 1980 by Lippincott &

Crowell, Publishers

[5] The American Heritage® Dictionary of the English Language, Fourth Edition, Copyright ©

2000 by Houghton Mifflin Company. Published by Houghton Mifflin Company. All rights

reserved.

[6] Merriam-Webster’s Collegate® Dictionary, Tenth Edition, Copyright 1999 by Merriam-

Webster, Inc.


[8] Creativity in Context, Update to The Social Psychology of Creativity, Teresa M. Amabile,

Harvard University with updates by Teresa M. Amabile, Mary Ann Collins, Regina Conti,

Elise Philips, Martha Picariello, John Ruscio, Dean Whitney, Copyright 1996 by Westview

Press, Inc., A Division of Harper/Collins Publishers, Inc., pp. 20-22.

[9] "Creativity Definitions", Charles Cave Creativity Web Site,

URL: http://www.ozemail.com.au/~caveman/Creative/Basics/definitions.htm

[10] Notes by Charles Cave on: The Creative Brain, Ned Hermann, Brain Books; Revised

edition (September 1989). Charles Cave URL:

http://www.ozemail.com.au/~caveman/Creative/Basics/herrmann.htm

[11] The International Encyclopedia of Education, Second Edition, Volume 2, Editors-in-Chief

Torsten Husen, T. Neville Postlethwaite, pp. 1175, copyright 1994 Elsevier Science Ltd.

http://www.ozemail.com.au/~caveman/Creative/Basics/herrmann.htm

http://www.ozemail.com.au/~caveman/Creative/Basics/definitions.htm


Chapter 10 – The Knowledge Creation Engine

The fox knows many things, but the hedgehog knows one great thing. -- Archilochus

This chapter ties together many different arguments presented in this book. The result is a clean and

simple understanding of the engine of knowledge advance in its purest and simplest form. To this point,

most of the complexity has revolved around conflicting terminology and definitions of terms. In reality, the

process of genius/creativity/knowledge creation itself is very simple and extremely pervasive.

The Single Process of Knowledge Creation

Recall our earlier conclusion that every creative method (e.g., brainstorming) in existence falls into one of

six categories:




4. Subconscious


6. Holistic (some combination of the above)

Setting the complexities of the subconscious aside, the first three categories basically summarize the

simple, holistic (# 6) knowledge creation process and can be rephrased as follows.

1. Structure or define

2. Reach out for new structure (question)

3. Change structural direction

This same three step process include subconscious (# 4) and visual problem solving (# 5) processes,

though we will not address these in this paper.

Figure 10:1 is a visual model of how these three steps and the knowledge creation process interact with

our knowledge sphere.


Figure 10:1

Consider the sphere in the center of Figure 10:1 as a constantly expanding logically structured and

logically categorized, global intellect or sum of human knowledge. Keep in mind that knowledge is

structure of mental elements, without structure there is no knowledge.

The sum of knowledge, or all that humanity knows, is a constantly expanding structural entity composed

of pure logic. It often takes many evolutions for us to locate pure logic and we tend to attempt to progress

without logic or with error, or in silos of duplicate logic (concept duplication). Yet, knowledge itself is

logical. Knowledge in its purist form is an interconnected perfectly structured and three-dimensional

entity.

The cutting edge is the leading edge of this structure and is represented by the planar surface of the

sphere. Problems and questions identify opportunities for a yet to exist future structure. These are

represented by the infinite empty space that surrounds the sphere. The simple, knowledge creation

process operates on this cutting edge. This process is cyclical and continually builds knowledge.

Advanced Definition

Cutting Edge

The cutting edge is the line or planar surface (in terms of three dimensions of knowledge)

between that which is known (current state) and the unknown (future state).

The arrows represent knowledge advance at a variable rate. Some disciplines and topics advance faster

than others.


Changing direction/morphology laterally (DeBono’s lateral thinking), or technically in any direction, can

help opportunities or ideas emerge because new direction helps advance related connections within this

logical base of knowledge. By changing directions another related problem can be advanced or solved

and that newly advanced knowledge can contribute to clarity of the initial, related problem.

The knowledge creation cycle that continually builds this sphere via human effort is a cyclical process,

constantly renewing itself. The full knowledge creation process is represented below.

Originated Concept

The Simple Cycle of Knowledge Creation

This cycle is always the same across all disciplines, enterprises and human efforts

1. Definition/Solution/Structure/Meaning (Knowledge Context)

2. Question/Query/Problem

3. Logical Operation (connects/structures/defines)

4. Advanced Definition/Solution/Structure/Meaning

5. Return to Step 2

Future knowledge is fully dependent upon past knowledge and this cycle of knowledge creation is the

driver of all knowledge advances.

This cycle is also the question/definition cycle discussed in chapter 6. Definitions advance by the process

above.

Anti-Knowledge

Anti-Knowledge is the perceived area between the known and the unknown. This area is recognized

because questions, problems, errors, and the absence of knowledge structure exist there. Anti-

knowledge serves as a roadmap to future knowledge. See Figure 10:2 below.


Figure 10:2

I recently submitted the following definition for anti-knowledge to several dictionaries as newly created

knowledge.

Originated Concept

Anti-knowledge

<multidisciplinary> (From creative science) /anti’-nä-lij/ The collective set of questions that form

an antithetical structure to a set or the sum of knowledge.

The sum of questions has a yin and yang relationship with the sum of human knowledge. The

dividing line between the sum of human knowledge and the sum of questions is the cutting edge

or leading edge. The knowledge creation process operates on this cutting edge and converts

questions to knowledge by structuring them.

Terms like genius, creativity, innovation, creative problem solving, and knowledge creation have

been historically studied and represented as loosely related topics. When appropriately

understood, all of these terms can be encapsulated into a single cycle of knowledge and anti-

knowledge as follows:


1. Definition/Solution/Structure/Meaning (Choose Knowledge Context)

2. Question/Problem


4. Result: Advanced Definition/Solution/Structure/Meaning

5. Return to Step 2

Mechanization of this question/definition cycle is true artificial intelligence.

{Anti-Knowledge Enterprises, LLC (http://www.anti-knowledge.com)}

(2004-07-26)

Questions are the unknown and if one is going to manage and reach out into the unknown, that person

ultimately needs to understand and manage questions.

Anti-Knowledge Key

The unknown is a ghost structure of the known and is composed of questions.

The unknown is really an infinite question structure and that question structure is composed of an infinite

number of tiny little questions or component questions as represented in Figure 10:3.

Figure 10:3

http://64.4.26.250:80/cgi-bin/linkrd?_lang=EN&lah=850f2a05a69270eae1fb69997b254e36&lat=1091408594&hm___action=http%3A%2F%2Fwww.anti-knowledge.com


Anti-Knowledge Key

1. Questions about existing knowledge follow the same structure as the knowledge itself

(categorized/classified/taxonomized).

2. Questions about future knowledge form a ghost structure of anti-knowledge that is

categorized/classified/taxonomized in an antithetical domain that eventually becomes knowledge.

In other words, knowledge creation questions follow the same structure as knowledge to come.

Anti-Knowledge is the yin of the Chinese yin and yang (figure 10:4) struggle as it relates to the intellectual

universe. The yin represents the force of the moon or darkness while the yang represents the force of

light. In intellectual terms, the yin is the knowledge we have yet to subdue and bring into the light of the

knowledge we possess.

Figure 10:4

Said in other terms, anti-knowledge is the extension of knowledge structures, recognizable by question

patterns and extended logic patterns that form an antithetical, structured shape of knowledge to come.

Figure 10:5 shows how anti-knowledge ties in with our triadic knowledge model.

The cutting edge is expanding on all fronts of the structure of the sum of human intellect. By adding

hierarchical category heads, knowledge can be expanded in breadth as well as in its progression. Also

notice that learning is associated with amassing that which is known, while genius/creativity/knowledge

creation is concerned with harnessing the unknown.


Figure 10:5

And figure 10:6 shows how everything ties together in our knowledge model. The dotted lines represent

the perceivable unknown or areas of question. A picture in this case is worth a thousand words.

Figure 10:6


Visualizing the Cutting Edge

During my last year of college, I developed a creative method I called Directional Categorization to help

me come up with creative advertising solutions. This creative method is a concise way of advancing the

precedent.

Originated Concept

Directional categorization

A creative method that plainly shows the cutting edge of a knowledge context. The user draws a

matrix as follows:

Who What When Where Why How

Today

Future

Any category heads could be used in this matrix; who, what, when, where, why and how are all-

inclusive of knowledge and work very well.

In the Today fields, the user records the current understanding of the problem.

In the Future rows the user brainstorms either existing questions or potential new solutions within

each column.


Consider also how the line in the directional categorization matrix, between Today (the known) and the

Future (the unknown) as the cutting edge.

The cutting edge is the home of the cyclic intellectual engine of question/definition. In fact, the cutting

edge is the line where questions begin. The advanced definition is expanded below.

Advanced Definition

Cutting Edge

The cutting edge is the line or planar surface (in terms of three dimensions of knowledge)

between that which is known (current state) and the unknown (future state).

The cutting edge is the line where cumulative questions that surround an existing knowledge

domain begin.

Figure 10:7 shows how one could use directional categorization to create a diaper advertisement.


Figure 10:7

Directional Categorization Example

Today

Who What When Where Why HowBaby Diapers After

Birth

Home Sanitary Purchase at

groceryMother Fresh

Smell

Low cost

AbsorbentComfortDisposable

Future

Who What When Where Why HowFather Stylish 5 stages Public Ease DeliveredGrandma Design Adjustable

to age

Hospital Conven-

ience

Mail order

Grandpa Compact Now,

hurry

Happy

baby

Phone

orderDoctor Velcro All happy From

hospitalNurse Safety SafeNanny Non-toxic StylishPet Form-

fitting

Sale!

Neighbor ElasticVisitor Fitted

PastelsPortableNot plasticImproved

The current advertisement (Today) would look something like this:

Mother at home gives baby comfortable, absorbent, fresh smelling and disposable Brand A

diapers.

While the new advertisement (Future) might look like this:


Daddy at home gets certain stage diapers in the mail and gets baby ready for a social with pink

pastel, comfortable, absorbent, fresh smelling, and environmentally safe disposable diapers. The

Velcro straps make it easy for Daddy. Grandpa loves it too.

Soon after college, I realized that the methodology applied to far more than advertising solutions and

began to expose this method to new disciplines. I found that the method had striking potential to solve all

types of problems from engineering problems to training problems to computer problems and process

problems. This simple method worked across all disciplines and enterprises and could be used to solve

any problem.

I didn’t realize it at the time, but I had stumbled upon a rudimentary form of the single method of

knowledge creation:

• Structure or define -- The categories to force structure in both the present and the future.

• Reach out for new structure (question) -- The future portion of the matrix forced me to consider

new structural direction. I also realized that I could add questions, instead of current scenarios,

into the same matrix and these questions, when organized, created solutions.

• Change structural direction -- These same categories, when combined with brainstorming forced

changes in structural direction.

Knowledge Docking

Knowledge is always advanced via the single cycle of genius/creativity/knowledge creation. Questions

are signals of impending logical connections. Questions and data can be managed in such a way as to

facilitate these logical connections.

Questions call out new knowledge. Behind every question and set of questions is a developing new

concept. Structuring questions and data in such a way as to facilitate logical structuring creates

knowledge.

Knowledge docking is the process of “docking” new knowledge into knowledge structures at the cutting

edge.


Originated Concept

Knowledge Docking

The docking of questions/data into existing knowledge on the cutting edge using the knowledge

creation process.

The mental entity that is docked is an idea. The term idea has been fairly hotly debated in philosophical

circles for hundreds of years. In the light of anti-knowledge, the definition is simple.

Advanced Definition

Idea

The realization, often sudden, of new knowledge structure. Ideas are formed by structuring

questions. When an idea is proven structured and logical, it becomes knowledge.

A bad idea, is proven to have an illogical knowledge structure.

Logic is the process of making mental connections that drive knowledge advance. Knowledge Docking is

the act of incorporating new connections/logic into existing structure. It is possible, for example, to dock

an entire discipline into another discipline.

Knowledge Creation Methodology

The greatest invention of the nineteenth century was the invention of the method of invention.

-- Alfred North Whitehead, Science and the Modern World [1925], ch. 6

Much of our time, attention and effort in modern society is focused on knowledge that already exists. The

simple knowledge creation process helps individuals and groups focus on knowledge that does not yet

exist.

1. Definition/Solution/Structure/Meaning (Choose Knowledge Context)


2. Question/Problem



5. Return to Step 2

Exhaustive Categorization

The power of leveraging anti-knowledge lies in a concept called exhaustive categorization.

Originated Concept

Exhaustive Categorization

Knowledge exists in categorical structure. Forcing categorical structure on illogical concepts

and/or on questions inevitably results in knowledge creation.

In order to effectively solve any problem, the knowledge context, or existing knowledge and the

questions/problems related to that context must be exhaustively listed and then structured.

How does it work? Since all knowledge is interrelated it is important to focus on a particular topic. Simply

choose a concept you want to advance or a problem that you want to solve and formulate an overarching

statement that fundamentally describes the problem.

Exhaustively Define

Knowledge advance requires knowledge context or definition. Focusing entirely on questions will not

result in created knowledge. Questions must be found relative to the context of the current definition or

common sense.

The common dictionary or encyclopedia is a great resource for this step. Current definitions are

knowledge context and the common sense. Throughout this book, this process of leveraging existing

definitions as knowledge context is at work. It makes for difficult reading at times, but the results of this

type of knowledge working are powerful.

It is human nature to accept definitions at face value as logical and correct. They are the stability of our

language and few would contend against the common dictionary. However, forcing all of the current


knowledge available into structure often reveals missing connections or connections never before

realized. Unless the social knowledgebase is extremely logical, exhaustively structuring/defining a

problem will almost always sheds light on an existing concept.

In order to exhaustively categorize, it is best to leave natural language, and choose a matrix or some

other type of hierarchical mapping tool or concept map. The linear nature of natural language fights

against a full, structural definition. Breaking the problem down into single words or small phrases helps

free you from the constraints of natural language.

The tool you choose should be capable of reducing the problem into single word elements and should be

able to facilitate categorization of these elements. I personally like to use a Directional Categorization

matrix or some derivative of this tool, because I can then see, present, cutting edge, and future, all on one

page. The weakness of using a two-dimensional matrix is that you will find yourself associating many of

these together because knowledge is three-dimensional. Planar matrices do not fully describe this three-

dimensional structure.

Use familiar creative methods like brainstorming to help you exhaust all mental connections on the topic.

The active agent in the brainstorming method is “exhaustion of mental elements. Brainstorming is

effective because it helps to list/exhaust available options.

You should force structure on everything you know about the problem or existing definition using

categorization/taxonomy. In addition, one must break the problem down into the smallest mental

elements within categories to begin to realize its structure.

Anti-knowledge Key

An incomplete category indicates a lack of definition.

Exhaustively Question

After you have exhaustively defined the problem, it is time to leverage the power of the question. In the

same way that you exhaustively and categorically defined the problem, list all questions that you can

perceive related to the knowledge context.

Categorize these questions, just as you categorized the definition.


As you begin to exhaustively define and structure the problem, a visual representation of the “cutting

edge” will begin to emerge. The cutting edge is the line between definition and questions. Remember

that a question is an indicator of a lack of logical structure.

Originated Concept

Question Mapping

Traditional concept mapping strictly delineates what you know now and at times serendipitously

stumbles into the unknown; but purposefully mapping the unknown in the form of structured

questions or question mapping forces structure on the unknown.

It is in forcing structure on the unknown that knowledge is created.

Anti-Knowledge Key

Knowledge creation is the process of structuring questions or data that are ‘thrown ahead’ of a

knowledge context.

Every known on the cutting edge has at least one question associated with it.

When knowledge is created at the cutting edge, new questions emerge at the advanced cutting

edge and the cycle of knowledge creation continues.

An Exhaustive Categorization Example

We will now review a very simple example of exhaustive categorization (an actual example could include

volumes of information around even a simple problem). I'm not an expert by any means on water

pollution (the topic selected) and there is no way to truly exhaust it in the context of this work. I randomly

selected this topic, but you will soon see that even a novice without the input of experts in the field can

come to some startling results by utilizing this process.


Keep in mind that, as questions emerge, they need to be differentiated from known mental elements so

that you will be able to visualize the cutting edge. This can be as simple as a matrix; or, for example,

placing known information or definition in a square and questions or the unknown in a circle.

Imagine that you have been asked by a world council to come up with a solution to the global problem of

water pollution. You start this investigation with a blank piece of paper.

The goal of your knowledge creation effort to collect data and knowledge and to ask questions, structuring

everything you collect. You start with your own understanding. Questions indicate the absence of

knowledge so, while you want to record questions, they represent an opportunity for more structured

knowledge and the goal is to eventually eliminate them. The goal is to continually replace questions with

created knowledge.

You start this process and categorize on one sheet of paper. You attempt to exhaust every thought that

you place on the page by creating a categorical structure around it. For example, for “types of

obstruction”, you list every kind that you can think of, but are always open that there are more options at

any level.

Using this method, you quickly identify areas where you lack understanding. At the close of this individual

session you decide to call in a collaborative effort. The knowledge and experience of a group of experts

can greatly expand the definition and questions.

In this example, questions are in bold boxes. Knowledge elements are categorized in non-bold boxes.

Arrows represent existing connections. Keep in mind that this is just one potential presentation.

In this example, you start with a problem statement: What is water pollution? From there, the problem is

defined/questioned and expanded to the limit of available knowledge.


(Continued)


Note: Solutions typically emerge from question areas. The solution might be learning for you

personally, or an actual knowledge advance.

(Continued)


This is a very simple example and only one method of representation intended to show how the concept

can be applied to any human endeavor in various levels of complexity

In the context of this paper, an understanding of Anti-knowledge harnesses the power of the knowledge

creation question.

Imagine a world in which methodical structuring of questions is systematized.


Chapter 11 – The Knowledge Creation Enterprise

The Quest for Newness

Structuring knowledge creation questions creates knowledge. The primary goal of a knowledge creation

enterprise is to structure questions to convert ideas into knowledge. Let’s look again at our advanced

definition for an idea.

Advanced Definition

Idea

The realization, often sudden, of new knowledge structure. Ideas are formed by structuring

questions. When an idea is proven structured and logical, it becomes knowledge.

A bad idea, is proven to have an illogical knowledge structure.

A good idea then, is new knowledge. As such, ideas are incredibly valuable to the enterprise. New

knowledge feeds research and development. New knowledge is competitive. New knowledge sells.

Granted there is often durable utility and value contained in maintenance of the old, but it is in the sale of

the new and different that the big profits are realized. In some industries, first to market can mean a

difference of billions of dollars. More and more, the very existence of an enterprise depends upon new

knowledge.

The exponential escalation of research and development expenses worldwide is a simple reflection of this

growing need enterprise for streamlined knowledge creation.

Enterprises are willing to spend a hefty percentage of the budget on R&D such that the hope of a new

product could be fulfilled.

A rule of thumb in advertising is to list the three key benefits that consumers will realize from the client’s

product or service. These benefits only hold value if they are distinct from competition. If a product


benefit is the same as a competitor’s product benefit, then the two are in head to head competition. If a

product benefit is new, beautiful, different or unique than a competitive advantage is created.

Consider for a moment: Old products vs. new products, old news vs. new news, old ideas vs. new ideas,

old knowledge vs. new knowledge, old paradigms vs. new paradigms, old technology and new

technology, old hardware and new hardware, old ways vs. new ways, old style vs. new style, old design

vs. new design, old process vs. new process, old competition vs. new competition, old markets vs. new

markets, old school management vs. new school management, old culture and new culture, old science

and new science, etc.

The list can go on and on, but the point I want to make is that this demand for newness does not stop at

the product itself. It is most prevalent in the product portfolio, which is the lifeblood of an enterprise, but

the same demand for the new and different exists in every facet of business. Change is driven by the

quest for the new and different. Change management is aimed at managing the process of change, but

change management is not aimed at the origination of the change. In spite of a lack of focus on change

origination, the level of change is increasing exponentially, as are efforts to manage this change. The

underlying driver for this rise in change is the quest for newness. This drive is the life force in the blood of

business.

All elements of business, like products, culture, process, markets, management style, etc., have a birth, a

lifecycle and a death. It is easy to see the lifecycle in a technology or a product, but it is much more

difficult to see this lifecycle in less concrete business elements like process, design, personnel, strategy,

events, and yes, even knowledge itself.

The Knowledge Creation Enterprise

Before we understand what a knowledge creation enterprise is, it is prudent to first explore the concept of

the organization/enterprise itself. To this end, I’ve included an advanced definition below.


Advanced Definition

Organization/Enterprise

A working group of people who collaborate and connect in a methodical, structured, and typically

hierarchical, cooperative knowledge effort toward an established purpose or set goal.

Every organization has a complex mix of product, process, knowledge, and etc. lifecycles.

In all instances, organizations/enterprises move forward with a purpose and that purpose is typically to

make a profit.

Knowledge creation is a key component to realization of this desired outcome, but an organization needs

to position itself to support knowledge creation. This does not happen automatically, but rather must be

part of the enterprise strategy.

Key Question

What is the difference between a normal enterprise and a knowledge creation enterprise?

A knowledge creation enterprise is distinct from a normal enterprise in that the

organization/enterprise has a clear strategic focus on the unknown and on creating knowledge.

In most organizations/enterprises knowledge creation is serendipitous, but in a knowledge creation

enterprise, knowledge creation is purposed and there are roles, processes and technology in place to

support this purpose.


Knowledge Creation Enterprise

A working group of people who collaborate and connect in a cooperative effort with a strategic

goal of generating value, benefit and/or intellectual capital through purposed knowledge creation

that supports novel product and service life cycles.


Now that we know what a knowledge creation enterprise is, let’s look at how we might enable such a

specific knowledge creation strategy. If we are going to realize knowledge creation, the first place to start

is in finding the sources for newly created knowledge.

Key Question

Where can an enterprise realize newly created knowledge?

Newly created knowledge is either found in the minds of a few individuals internal to the enterprise

(potential hires, existing employees and/or external consultants) or it is compiled from external sources

that express the new knowledge (created by individuals or groups).

Figure 1:1 shows how newly created knowledge typically flows into an enterprise in our current paradigm:

Figure 11:1

Academic professors pull knowledge from science and technology and give lesser amounts back into

these same entities through respective disciplines. The knowledge of the professor is passed on to the

student who is in turn hired by the enterprise.

As in natural language, the nature of the process is linear and there is disparity between enterprise and

academic knowledge creation. Enterprises and academia grow knowledge at different rates and at


different times and only one facet of this paradigm pulls everything together. That facet is publishing, or

the public expression of knowledge, which forms a web around this linear knowledge flow.

Without published knowledge, this linear paradigm would collapse. Publishing allows knowledge

transport through all levels of this process and allows business and academia to advance at a

comparable rate of knowledge advance.

While knowledge creation can be introduced anywhere in this process, it typically arrives at the door of

the corporation either through one of four means:

1. A new hire brings it in

2. It is created internally (individual or collective)

3. It is garnered from published works

4. It is garnered from the university, consulting agency or other external collaboration

We are so used to hiring intelligent individuals that it seems against our nature to think about knowledge

creation as a core competency for a new hire or for an existing employee. In the current age, intelligence

is king, but recruiters will some day be seeking an entirely different skill set and our educational system

needs to be able to supply the same.

Figure 11:2 shows the life cycle of a traditional enterprise and how knowledge creation skills can

inadvertently go to competition and subsequently new knowledge never arrives in the future state.

• The letters represent individuals or classes of individuals

• The numbers represent tacit knowledge

• The numbers in black boxes represent newly created knowledge


Figure 11:2

Notice that this enterprise (Corporation A) hired intelligence (top of the class applicants) and knowledge

creation went to competitors. Corporation A also has no internal infrastructure to support knowledge

creation. To this corporation, the unknown is still the unknown and their only hope of accessing it is

through published knowledge that every other competitor also has access to.

Figure 11:3 shows how new knowledge can be both hired and developed from within through a conscious

effort. In this scenario, Anti-Knowledge management enables even more unknowns to become known by

facilitating cutting edge knowledge creation. As you can see, the final result is new knowledge and a

vastly more competitive position than Corporation A.


Figure 11:3

The knowledge creation enterprise (Corporation B), on the other hand, made a conscious decision to hire

created knowledge. Both created knowledge and knowledge creation capabilities are highly valued

elements and are sought out within the new recruit.

The Model Knowledge Creation Enterprise

The optimized model of the knowledge creation enterprise is one where the enterprise is directly

connected to the advance of science and technology.

This does not imply elimination of the education process, but does imply that an improvement/solution

may be necessary to make the flow of new knowledge to the enterprise as seamless as possible.

Figure 11:4 shows this interrelationship on a very simple level; the cycle of knowledge from science and

technology into the enterprise.


Figure 11:4

In the model enterprise, the organization is a participant in the advance of science and technology and

not a spectator or a recipient.

The knowledge creation enterprise breaks new ground, leads the charge for new knowledge in science

and technology. The end is new or novel products and services, but the means is management with

overarching culture management.

Management

What exactly is management?

Definitions

Management is similar to the term question in that it is really quite difficult to find a good definition.

“Managing something” is not a good definition for management. Here is a fairly good one:

Management, n.

1. The act or art of managing; the manner of treating, directing, carrying on, or using, for a

purpose; conduct; administration; guidance; control; as, the management of a family or of a farm;

the management of state affairs. ``The management of the voice.'' --E. Porter.

2. Business dealing; negotiation; arrangement.


He had great managements with ecclesiastics. --Addison.

3. Judicious use of means to accomplish an end; conduct directed by art or address; skillful

treatment; cunning practice; -- often in a bad sense.

Mark with what management their tribes divide Some stick to you, and some to t'other side. --

Dryden.

4. The collective body of those who manage or direct any enterprise or interest; the board of

managers.

Syn: Conduct; administration; government; direction; guidance; care; charge; contrivance;

intrigue. [1]

Summary

• The judicious means or manner of treating, directing, carrying on, guiding, administrating, using,

controlling for a purpose or accomplish an end.

• The collective body of individuals who manage or direct any enterprise or interest.

Advanced Definition

Management

The individuals and process that judiciously direct, guide, administer and/or control a working

group of people who collaborate and connect in a cooperative effort with a strategic goal of

generating value, benefit and/or intellectual capital through purposed knowledge creation that

supports novel product and service life cycles.

The purpose of management is to guides and control enterprise efforts in order to achieve its

purpose, which is typically novel products and services.

In today’s business environment we see lots of ‘little’ management sciences as separate entities. We

typically think of human resource or administrative management as true “management” with all the other

management sciences being technical specialties or ‘sub-management.’

In the knowledge creation enterprise, these little management sciences are incorporated into a holistic

view of the enterprise that collectively and with connectivity drive toward the same purpose and do not

neglect the management discipline of knowledge creation.


The typical knowledge creation enterprise would manage its business through an ordered and

interconnected combination of management sciences intended to produce capabilities. Management

sciences like:

• Strategy/Capabilities Management

• Publishing/Records Management

• Knowledge/Learning Management

• Human Resources Management

• Metrics/Balanced Scorecard Management

• Technology/Process/Change Management

• Data Management

• Research & Development/Product Design Management

• Portfolio/Program/Project Management

• Engineering/Production Management

• Marketing Management

The overarching management of this framework is enterprise culture management.

Enterprise Culture

Enterprise culture or corporate culture has been defined so many disparate ways that it is extremely

difficult to arrive at a common sense definition. The fact that the common sense is scattered is a strong

indication that we have not arrived at a clear definition of what culture is.

This lack of clarity is partially due to the fact that culture includes the soft side of enterprise efforts like

feeling, values or morals. These softer qualities of the enterprise are often less objective and much more

difficult to express.

I know there are scores of other definitions, but let’s look at a fairly simple definition that I believe

accurately reflects the common sense:


Definition

Culture, n.

1: a particular civilization at a particular stage

2: the tastes in art and manners that are favored by a social group

3: all the knowledge and values shared by a society [syn: acculturation]

4: (biology) the growing of microorganisms in a nutrient medium (such as gelatin or agar); "the

culture of cells in a Petri dish"

5: the raising of plants or animals: "the culture of oysters" [2]

Culture is related here to a civilization, a society, an organism and a microorganism. I don’t believe that

the word selection was accidental here. I believe that all of these represent levels of complexity in the

control of a living organism.

Culture can be seen as a controlled or guided growth/maturity in a living organism(s), be it a civilization, a

social collective or enterprise, a living being or a microorganism. If the living organism is not controlled or

guided in some way, culture would be an inappropriate term.

By this definition, a herd of wild buffalo is not considered a culture, but a controlled herd of cattle would be

considered a culture. The cattle may be grain-fed or grazing cattle, fenced in or allowed to roam,

medicated or organically treated. These variations in control provide for a distinct culture within the herd.

Compared with other cultures there are varying levels of effectiveness.

Advanced Definition

Enterprise Culture

The history and the current state of controlled or guided growth/maturity of the collective

enterprise. This as it relates to the knowledge, skills, priorities, morals, beliefs, values, myths,

practices, feelings, behaviors and attitudes of the enterprise and its collective membership.


As when individuals speak they can be culturally identified, so when the enterprise speaks,

culture is identified. Culture is the mix, weak and strong, of the enterprise lifecycles morally,

organizationally, emotionally and mentally.

Culture and Business Lifecycles

Any enterprise is a complex combination of business lifecycles. Some aspects of the business are newly

born, while others are in maturation. Consider culture then, as the management of these lifecycles. A

controlled environment is established for the birth, growth and maturation of business “organisms” like

process, design, change, knowledge, capabilities, projects, documents, etc.

I like to call culture ten-year management, as its focus is more observational with very, very slow

introduction of change at a high level. As upper management observes the various life cycles in

operation, decisions are made to change either the mix of lifecycles or the controlled environment in

which these exist at the highest level. These decisions are five and ten year decisions.

In biology, attempting to fully control the organism’s growth often leads to its demise, as the life of the

organism does not adjust well to direct intervention/control. Instead, other living or chemical elements are

introduced to influence the growth direction of the organism at its own level. This is how cultural impacts

are made in a business. Rarely is a direct, massive culture change undertaken. The common approach

is to introduce cultural influences over time and to keep the environment fairly stable.

A set of management sciences has developed and will continue to develop. Change management,

quality control and assurance, process control, records management, project management, human

resource management, capabilities management, strategy management, knowledge management, etc.

have emerged to monitor, facilitate and feed the growth of the business organisms. Each of these

management disciplines is a concerted effort to manage the inherent lifecycles of a single element of

business. Each business element has its own collection of entities, all with distinct lifecycles that react to,

interact with and influence one another.

For example, at the highest level, one business might have an empowered culture while another may be

struggling with a culture of overt control. At the midrange level, one business may be mature in quality

control, but immature with regard to project management. At the lifecycle level, one business may be

great at project startup and weak with regard to project control or project closure. The important point is

that, at all levels, the business organization is a complex mix of lifecycles, each with its own set of

inherent strengths and weaknesses.


So then culture is the sum total experience/maturity level of the enterprise. Culture is what the enterprise

values, what they consider to be good business practices, how they manage knowledge and other efforts

of the business, how they feel and what they believe, etc.

Cultural statements then, are not limited to emotional topics. For example, an enterprise could have a

culture that places no value on measures, but this doesn’t mean that valid measures are not a best

practice for that enterprise. Not valuing measures is really an indication of a low maturity level for the

enterprise.

Being generally late for meetings as an enterprise could be considered bad in the sense that it wastes

time, but could contribute positively to lowering the stress level of the corporation. The decision to allow

or not allow this practice is a cultural decision in that it represents a maturity level for the enterprise in

understanding the optimal order and guidance for its membership.

In other words, whether or not the employees attend meetings on time is not culture, but why the

enterprise management perceives this to be ‘uncontrolled’ or ‘allowable’ and employee reaction and

tolerance for the resulting state is culture.

Culture is hierarchical if there is another controlled environment that ‘contains’ the organism. For

example, when an enterprise (which is itself a controlled and guided social organism) is placed into the

context of a nation (a controlled and guided organism) the control and guidance of the containing entity

impact those of the contained.

The whole concept ties in nicely with the concept of cybernetics, which will explore in more detail in the

next chapter.

Knowledge Creation Embedded in Enterprise Culture

Simply bringing new knowledge to the enterprise and managing the efforts around this knowledge is not

sufficient. The culture must also support the knowledge creation paradigm and the new knowledge when

it arrives.

Many individuals talented in knowledge creation have simply conformed to ‘intelligence’ expectations of

the enterprise because corporate culture simply does not value their knowledge creation efforts.

Cohesive and interconnected/interactive management of the capabilities of the enterprise is part of a

controlled/guided mature state of business. From this perspective, valid measurement is equally as


important to the enterprise as enabling technology. Strategic focus is equally as important to the

enterprise as change management. And yes, managing the unknown is equally as important to the

enterprise as managing the known.

All of these management elements are interconnected and interdependent. For example, if an enterprise

pushes innovation and fails to measure accurately the result is a deluge of misguided projects that do not

positively impact the bottom line. If on the other hand an enterprise manages innovation and measures,

but fails to manage projects, execution becomes the failure. All elements of management must work

together to accomplish the purposes of the enterprise.

A model holistic view of enterprise management is represented within Figure 11:5.

Figure 11:5


Figure 11:5 is a more detailed view of Figure 11:4. Each of these sub-management sciences is actually a

link in the chain of the larger organizational effort to produce capabilities that drive the creation and

maintenance of novel products and services.

There are surely different mixes of this diagram with regard to different types of enterprises. This is not

an attempt to cookie cut all enterprises, but rather to provide a general framework for integrated

management that includes knowledge creation.

Science and Technology Centric vs. Customer Centric

The first glaring change that you will surely notice in viewing Figure 11:5 is the displacement of the

customer as the ultimate object of the enterprise service. The customer is an offshoot here of servicing

science and technology; this because it is ultimately advances in science and technology that the

customer will want and need. If we change then, our primary focus to how we interact with science and

technology, the customer will be served in the process.

Most enterprises assume that they have met science and technology requirements by serving the

customer, but in so doing these are attempting to make the customer the innovator in the process. Let

me explain.

These enterprises, in placing the customer perception first, assume that the customer knows what he or

she wants. But if the customer knew what he or she wanted, he or she would have invented it. In fact,

the highest demand among products and services come from products that no one has ever heard of or

at the time of its introduction, no one even imagined it could exist.

Granted, valuable information will come from customers with regard to new product development, but

these only serve to hone the direction we choose to create/innovate. Ultimately, the enterprise holds the

responsibility of knowledge creation and to create knowledge must be intrinsically tied to the specific

knowledge context within science and technology.

The active verbs along the left side of the downward arrow describe the effort being managed. These are

probably oversimplified, but again show that management is an interconnected and interdependent entity

with specific sub-goals. General competencies like communication, influence, organizational skills, etc.

also run through all of these management veins.


These divisions of management and employee efforts all work together to provide novel products and

services by their hopefully holistic efforts and by not neglecting to infuse knowledge creation management

into the enterprise.

The Knowledge Creation Process

From an enterprise perspective, knowledge creation is a measured and demonstrated radical advance in

products, services or supporting capabilities that demonstrate radical improvement in cost vs. benefit or

return on investment (ROI).

Most enterprises would love to have more products than they can support and neat/clean solutions for all

of their problems.

Such enterprises could pick the products of value and sell the remaining intellectual property. With instant

solutions to every problem, the internal business process would be a well-oiled machine. Of course,

arriving at this condition requires skill in knowledge creation.

When any enterprise cannot replicate the knowledge creation process, then creativity and innovation

become more serendipity than intelligent business process.

It’s not that difficult, when employees are intelligent, for the laws of probability to assure us that every now

and then a talented employee will find a breakthrough product or solution. All throughout history, at times

sparsely and at times abundantly, knowledge has been serendipitously created.

But think of the power if we could replicate this success and establish a knowledge creation process that

will perpetuate knowledge advance within the enterprise.

Did you ever wonder why at times there were centuries between meaningful social knowledge advances

and at other times there were literally only minutes? Knowledge can be created every minute, but we

must understand the process and be equipped to handle the result.

Hierarchical Management

This knowledge creation process is much more complex than it seems because the

organization/enterprise is typically hierarchical and as a result has layers of control/guidance. This means

most categories of management must also be hierarchical.


For example, knowledge management, strategy/ capabilities management, publishing/records

management project management and metrics management all need to be hierarchically controlled and

guided throughout the enterprise. None can be effective in a simply “top down” approach.

As a more specific example, a single balanced scorecard for the entire enterprise is not metrics

management. Metrics management is the entire infrastructure of hierarchical measures that compliment

and cooperate with other management efforts and roll up into a balanced scorecard.

Knowledge creation aspects are really no different. We need to manage the unknown hierarchically

throughout the organization. Historical approaches are typically at a single point of entry within the

enterprise; enter the “idea box.”

Historical Idea Collection and Processing

Most of the historical efforts in the arena of knowledge creation to this point can be summarized in the

phrase ‘Idea Collection and Processing.’ Idea collection and processing is a blanket phrase I will use

here to describe any and all past attempts to harvest the unknown.

I’m sure that anyone reading this work has had some type of exposure to the idea box. Perhaps it was a

nice paper box with a slot on top and a light bulb pasted or drawn on the side. Or perhaps a slick

interface to a computer database, but whatever it looks like it is typically a centralized place where

employees can go to provide suggestions that they feel are meaningful for the enterprise.

Of course, the idea box seemed to run the course of a fad as they are rarely utilized anymore.

Corporations like 3M are famous for the way that they manage idea collection and processing company

wide, but most corporations have settled into a placid indecision around this management capability.

Idea collection and processing is definitely a sensitive business operation. Employees tend to gravitate to

the idea box to use it as a place for expression of feelings, a place to complain, a place to achieve a

political goal, a place to make a sale, even a place for a gum wrapper. It’s just a pretty box with a light

bulb on it sitting on the administrative assistant’s desk. There is little real connection with the enterprise

reality, so why not utilize it in any way you want and meet your perceived aims?

Paper and electronic idea boxes typically fill up with complaints, arguments, problems, politics and every

now and then, a fabulous idea! Because management is seeking this excellent idea, they typically

overlook all the transgressions and express an unwavering support for the idea box concept.


But alas, the quality of the inputs from the idea box just weren’t what we wanted to achieve. We got a few

good ideas, but there were problems that developed. We aren’t terribly sure how to deal with or even

express these problems, so we threw the idea box in the trash.

Common Failures Specific to Knowledge Creation Management

There are several common problems/failures that arise in any enterprise relative to knowledge creation

management. These are:

1. Weak sponsorship, communication and/or change management

2. No link or an unclear link to science and technology

3. Insufficient knowledge context

4. Complaint overload/complaint confusion

5. Inability to cope with constructive criticism

6. The voice unheard syndrome

7. No clear processes for idea submission, collection and processing

8. No real or perceived benefit for knowledge creation

9. Concept confusion

10. A breakdown in supporting management areas (e.g., Metrics)

11. External/legal barriers

1. Weak Sponsorship, Communication and/or Change Management

A process that is not fully understood is will likely never be sponsored. It is quite difficult to establish a

business case at the front end of enterprise processes that almost exclusively impacts the outcome at the

back end of the enterprise.

But as with any other implementation, sponsorship and other basic change management principles are

quite necessary for successful implementation. Knowledge creation is an implementation, but it happens

to require a change in the entire spine of the enterprise. It is a fundamental shift that impacts all other

management areas.

Weak sponsorship or poor communication around the change to the spine of the enterprise can prove to

be disastrous. The line of failures in the knowledge creation history of the company can create a “Not

another idea box!” syndrome.


Don’t expect a knowledge creation program to implement itself. It must be managed like any other

implementation.

2. No Link or an Unclear Link to Science and Technology

As we discussed earlier, the knowledge creation enterprise, while still focusing on customers, must rise

above the customer needs to realize the next advance in science and/or technology that the customer is

likely fully unaware of.

The customer cannot want something if they have no knowledge of its existence. And if an enterprise

focuses entirely on customer wants and needs it may never realize products and services beyond the

cutting edge of science and technology.

Linkage to the cutting edge of science and technology must be clear and crisp. Hiring into a corporation

and severing the relationship between that individual and academia is not a clear and crisp linkage.

The knowledge creation enterprise bridges the gap between academia and enterprise. In fact, it tightens

down this relationship to begin to fully understand its efforts within the broader context of its own

contributions to science and technology.

3. Insufficient Knowledge Context

To understand this typical problem we need to better understand knowledge context.


Definition

Context , n.

1: discourse that surrounds a language unit and helps to determine its interpretation [syn:

linguistic context, context of use]

2: the set of facts or circumstances that surround a situation or event; "the historical context"

[syn: circumstance] [2]

Context

That which surrounds, and gives meaning to, something else.

<grammar> In a grammar it refers to the symbols before and after the symbol under

consideration. If the syntax of a symbol is independent of its context, the grammar is said to be

context-free. [3]

Summary

Knowledge context surrounds a language unit, situation, event, symbol, etc. Knowledge context gives

meaning/interpretation to something else.

Consider now the following advanced definition:


Context

The entire knowledge structure that precedes the advance of a question/problem. In the case of

newly created knowledge, the context is on the cutting edge. In the case of the learner’s

orientation to existing knowledge structures, the context precedes the knowledge structures to be

learned.

Knowledge context then, is the knowledge that surrounds a problem or situation that helps us

understand the current state and that which is the actual target of advance.


In learning we advance our way through knowledge context until we arrive at the cutting edge.

Context then, precedes any advance in either learning or knowledge creation. Learning context

eventually ends up at the cutting edge, after that you’ve learned everything the world knows.

Knowledge context is part of the knowledge creation cycle:


2. Question/Problem



5. Return to Step 2

An individual absolutely must possess knowledge context of a problem to solve/answer it and must have

an understanding of the current definition in order to advance that definition.

Consider a project manager attempting to solve a physics problem. While the project manager might be

learned as a physicist, more than likely he or she is not. There is a strong possibility that he or she does

not possess the knowledge context that would allow him or her to contribute to physics knowledge or

solve physics problems.

As a learner of physics, the typical project manager would need to start at the bottom of the knowledge

structure for physics and incorporate this structure, fact by fact and concept by concept, until he or she

reaches the cutting edge of this knowledge domain.

As this concept relates to idea collection and processing, consider that a manufacturing line employee

suggests to upper management that everyone in the company should receive an across the board pay

increase. While that might be great suggestion from the context of manufacturing line employees, the

individual submitting the idea does possess the knowledge context necessary to evaluate and formulate

such a solution. If management were then to take the same idea to accounting and discuss it with

someone who has the knowledge context, he or she very well might get a radically different view of the

proposed change.

The implication here is that you can’t just place the idea box on the administrative assistant’s desk. The

idea box needs to be in specific context. Knowledge context is absolutely necessary for knowledge


advance. It’s fine to cross-pollinate and send observations to a specific area of knowledge context, but

the responsibility for assessment of the change and eventual implementation lies with those familiar with

the knowledge context.

This gets a bit tricky in that one cannot assume an employee’s knowledge competency in a knowledge

paradigm by sheer job location or job function. For example, I might be very knowledgeable in

document/records management, but my actual job and location really don’t appear to be associated with

this arena.

Employee profiling, then, is an essential support structure for any idea box. In employee profiling an

association is made between the employee and what he or she knows, is capable of and/or is competent

in (vs. education, background or current status).

4. Complaint Overload/Complaint Confusion

Any time an enterprise requests ideas it inevitably receives complaints. Typically far more complaints are

received than ideas! The world has its share of negative or disgruntled enterprise members who just

want to vent or complain anywhere the opportunity arises.

This is a symptom of a lack of hearing the employee voice. Employees are dying to express their

opinions and have no opportunity. An idea box is set out, and in goes a barrage of complaints.

In a world where the company does not listen to you, an idea box is the next best means for having your

voice heard. The rule of thumb is, where employees have no voice, they will seek an avenue to be heard.

Idea harvesting must be accomplished in tandem with a complaint/employee voice process and/or

system.

When there is a clear place to vent and complain and an equally clear place to submit ideas, the

enterprise member is forced to differentiate between ideas and complaints and place the appropriate

content in the appropriate system.

5. Inability to Cope with Constructive Criticism

In most disciplines and enterprises, employees have absolutely no concept of how and why to give and

receive constructive criticism. Related to this, such behavior is typically penalized and not rewarded.


In a graphic design college level program, an individual’s work is submitted to the rigorous criticism of the

rest of the class and the individual is then forced to defend his or her work. In this context, the activity is

known as a critique.

At first, critique causes sweat beads to rise up on the student’s forehead, but after many critiques, the

student adjusts and learns to deal with the many negative feelings that tend to arise. Dealing with the

sensitivity of constructive feedback is a learned behavior.

Work is a very personal object for most individuals, particularly those with a high amount of knowledge in

the field or for those with an unusually strong motivation. And of course, everyone really deep down

believes that they are “right” and everyone else is catching up to their level of understanding.

Constructive feedback puts employees in a state of weakness that does not feel natural or rewarded.

Employees are rather rewarded for strength, composure, ability. Weakness is a threat. Weakness is a

problem.

As part of this learned behavior, many are unable to suspend judgment and thereby differentiate between

an idea and a personal attack. One might also tend to immediately go to the consequences of a proposal

and take offense if the consequence has a negative effect on them personally.

And then there is the problem of differentiating between objective and subjective criticism or between

opinion and fact. Opinions don’t demand change, but facts do. Yet in most environments, opinions are

perceived as demanding change.

For example, “I don’t like that color” has an entirely different message from “That color is not acceptable.”

And of course, there are always communication hurdles to overcome. For example, some people say,

“That color is not acceptable” when they really mean, “I don’t like that color.”

In short, giving and receiving criticism is a lost art that most individuals and enterprises have failed to

realize.

6. The Voice Unheard Syndrome

If two individuals sat in conversation and one talked while the other just sat there, never acknowledging

any of the conversation, the speaker would likely come to the conclusion that the listener does not care.

A response is not only courteous; it helps check understanding and engages the participants.


Keep in mind that everyone’s personal idea is the best idea (on their personal cutting edge) and so how

would anyone have the audacity to reject or fail to recognize that idea? “Since it was ignored, I must

conclude that management is an outsider and is against me.”

Failing to manage the sensitive and personal nature and the strong feelings of personal ownership of

ideas can be devastating to the knowledge creation program. And not only that, but it contributes to the

“voice unheard syndrome” throughout the enterprise; management doesn’t listen, management doesn’t

care, management is removed, etc.

7. No clear processes for idea submission, collection and processing

Of course, it is just an idea box. Instructions don’t come with it. Or should they?

Most definitely, all idea collection and processing systems need a simple and clear set of guidelines for

submission along with submission etiquette.

Example

Simple idea submission guidelines:

• Objectively state all ideas and provide measures/benefits if available

• Include any observation around an existing idea as a new idea…do not reference other

ideas

• Suspend judgment…no idea is a bad idea

• Be respectful toward individuals and groups

• Maintain a courteous, positive and helpful attitude

• Understand the idea submission topic and adhere to the topic requested

It’s a lot like chat room etiquette. You abide by the rules or you and your ideas are not accepted into the

system.

“Where do all these ideas go?” employees will surely ask. “ Its like a big black hole; you submit ideas and

they are gone.”


There is no clear progression of the idea. I can’t, for example, track the progress of an idea in the idea

collection and processing system. I don’t know how it was perceived and I can’t contribute to that

perception. No dialog is allowed, just one-way submission of ideas.

Ideas are like working prototypes. Interaction will strengthen the quality of the concept. When all

communication is one-way, this interaction is by default not allowed or enabled. And when the submitter

cannot track the evolution of the various ideas, perception arises that it has spun into the meaningless

black hole or even worse the a perception that now someone else will profit from that idea, so “don’t

expect me to submit any more ideas!”

8. No real or perceived benefit for knowledge creation

Perceived benefit is a twofold proposition. On the one edge is idea protection and on the other is idea

reward and recognition.

With regard to idea protection, if I submit five fabulous ideas that fuel the careers of five of my peers, I will

quickly curtail any meaningful contribution.

Mechanisms should be put in place protect an individual’s idea from becoming someone else’s great idea.

An internal patent office, if you will.

If I am a knowledge creator, in a few short seconds I can disclose my new knowledge and fuel ten to fifty

other people’s career or I can choose rather to keep my idea to myself. Which would you choose if you

knew the outcome? You say this isn’t so, but in this scenario the individual that did not originate the idea;

having now stolen it, tend not to include the concept originator. They know what they have done is

damaging to the individual, but there are no rules and they really don’t want to be reminded of the

infraction. So the group excludes Sally or Tom, Becky or Bill from the group as it moves forward.

With regard to reward and recognition, knowledge creation contributions not recognized and not rewarded

will not continue.

If it is a good idea you need to implement it, but don’t expect the flow of ideas if you don’t have some

process for reward of that use. This tends to run against the grain of our intellect-based society. We jump

to reward intellect and invariably fail to reward knowledge creation.


By and large there are few cultures that both reward and protect the employees who wish to disclose a

novel concept. In most cultures it is much more prevalent to protect and reward background and

knowledge and totally ignore knowledge creation capacity.

9. Concept Confusion

If the enterprise has an unclear knowledgebase or unclear term descriptions and definitions there is a

negative spill over into the knowledge creation realm. How can an enterprise create new knowledge

when it does not have an effective command of existing knowledge?

Related to this are strong enterprise records management capabilities, strong training and

communications capabilities and a strong enterprise dictionary.

It is absolutely imperative that knowledge advance is supported by a harmony of terms.

The enterprise “common sense” must be established to streamline knowledge advance.

10. A Breakdown in Supporting Management Areas (e.g., Metrics)

Knowledge creation management is fully dependent upon other enterprise management factions to

successfully accomplish its goals.

For example, measurement is knowledge creation’s best friend. Metrics validate a good implementation

and no idea is proven successful until it is successfully implemented. Only by measurement can we truly

differentiate between waste and value generation. But the metric is typically a long distance from the

originating idea.

For example, a product designer patents a product that sells like hot cakes five years after design.

Everyone remembers the salesman and forgets the concept originator.

Measurement should be hierarchical, from the local department to enterprise level. These should be

unified and ‘rolled up’ such that departmental impacts are reflected in enterprise level metrics. Likewise,

knowledge creation is a hierarchically organized partner with recognition and reward tied to actual

metrics.

The knowledge creation enterprise is necessarily holistic. This is why it is so tightly tied into enterprise

culture.


Any activity where the created knowledge is more than two years from the results of that knowledge is not

appropriately managed.

11. External/Legal Barriers

Intellectual property protection in enterprises can become like a nine-foot tall fence for cows (and cows

can’t jump or climb). It is quite possible to overprotect intellectual assets.

Sometimes it is not the enterprise, but government regulation that steps in and makes knowledge creation

difficult. Intellectual property laws or even internal intellectual property policies are formed that restrict

individual rewards and recognition for knowledge creation.

The push for patent protection and copyright protection can easily move out of the boundaries of

protecting enterprise assets and into the realm of hindering knowledge creation.

It is important to lobby for laws and create internal policies that encourage individual contributions to

knowledge creation while also protecting inherent value to the enterprise.

An enterprise, for example, might use policy restrict an employees access to knowledge stimulating

activities like conferences or collaborative groups in an attempt to protect enterprise intellectual assets.

Not that such an activity is inappropriate, but I don’t think that such implementations were ever made with

recognition of the backlash these have on knowledge creation contributions.

I’m definitely not proposing ignorance of intellectual property security, but I am saying that knowledge

creation should be a prominent part of any equation that restricts employee’s access to external

knowledge or restricts an employee’s reward and recognition for knowledge creation contributions.

The Knowledge Creation Cultural Framework Summary

Turning these historical failures into positives, we arrive at the following set of cultural elements that

support the knowledge creation enterprise.


Anti-Knowledge Key

The cultural elements that support the knowledge creation enterprise are:

• Clear sponsorship, communication and change management

• Strong linkage with the advance of science and technology

• Knowledge creation at the point of knowledge context with tempered cross-fertilization of ideas

• A clearly separate complaint system

• Employee knowledge creation/constructive criticism capabilities

• A feedback loop

• Clear processes for idea submission, collection and processing

• Clear benefits that motivate participation

• A clear and hierarchical enterprise dictionary (at all levels) and a clear enterprise knowledgebase

and knowledgebase governance

• A synergistic management effort under the enterprise culture umbrella

• Reasonable and balanced intellectual asset protection

References



[3] The Free On-line Dictionary of Computing, © 1993-2001 Denis Howe


Chapter 12 - Knowledge Machine

This chapter is basically about systematizing question processing—something which really has never

been consciously attempted before. While the models are slightly different, doing so creates

unfathomable possibilities in both learning and knowledge creation.

Anti-Knowledge Key

Just as a knowledge base is a storage area for knowledge, a question base is a storage area for

questions.

Before we bring out this approach, it is important to first look quickly back and understand how we got to

where we are today.

The Closing Information Age

Adam Smith wrote “The Wealth of Nations” and the division of labor was born. The flood of industry that

followed became fondly known as the Industrial Revolution, an age of invention. The pace of industry

quickened and the complex science of industry became known as technology. Technology increased in

complexity and computers arose to process and manage the wealth of information that was produced.

The attitude that developed was an optimistic, but limited view:

“Except for tasks requiring human creativity, the applications of the digital computer are virtually

limitless, such limitations as there are being principally related to difficulty in acquiring adequate

data for the computer or in reducing the data to numbers.” [1]

The computer could do ANY task, except those tasks requiring human creativity. These tasks have been

perceived as beyond the capabilities of both human understanding and the computer system.

The information age emerged as an era of generating and connecting knowledge. As knowledge grew,

the computer arose to meet the growing need to manage it. But the computer itself also required

knowledge to support its maintenance and growth. Technology emerged as a scientific support

mechanism.

People realized that local computerized information was not sufficient and as a result there were various

pushes toward connectivity. These started with Local Area Networks and culminated in the introduction of


the World Wide Web. Connectivity emerged as the business element of primary emphasis. Knowledge

management and e-Business were born. At the close of the last century there was a large push for

improved management of this knowledge and the focus of this push was largely connectivity and

collaboration as the ultimate solution.

Unfortunately, connecting everything to everything else, without understanding the knowledge interactions

that sustain it, results in a massive influx of knowledge chaos.

Society has also overlooked the birth of knowledge—knowledge creation. Birth only takes a fraction of

time as compared with the lifecycle, but knowing when and where a baby is born is critical to sustain it.

The information age is closing and a new era has already begun to emerge. But the hallmark of this era

will not simply be the amassing of more and more knowledge, but rather the cooperative management of

existing knowledge and cooperative generation of new knowledge. This era of cooperative advance does

not imply the death of capitalism by any means. It simply means that we will reach a level of conscious

competence in cooperative knowledge working.

The Learning Machine

To the small part of ignorance that we arrange and classify we give the name knowledge. -- A

Bierce - Peter, L.J. the Peter Prescription: How to Make Things Go Right, Bantam, NY, 1972

Recall now our two question types, learning questions and knowledge creation questions. Learning

questions are questions about knowledge that exists. The learner asks these questions as they attempt

to incorporate existing knowledge structures.

Efficient mechanisms for learning are developing an ever-increasing demand in our world. This trend is

fueling the formation of an emerging multi-billion dollar instructional design and technology industry.

This spending trend in part is due to the fact that while knowledge is increasing exponentially, the social

and individual capacity to cope with, incorporate and utilize this knowledge is not.

Keep in mind that we learn what society or individuals already knows, while we create knowledge that no

one knows. Learning and knowledge creation are two separate knowledge interactions.

For everything you’ve ever learned, the structural logic had been previously formed, but to you as a

learner it was unknown. If you recall figure 1:3 below and remember that learning is the transport of from


the global brain/intellect to the individual or machine brain/intellect. This transfer results in increased

intelligence or increased knowledge stored by that individual or machine. Some language vehicle must be

used for the transfer of knowledge.

Figure 1:3 (Copy)

Both instructor and a learner can be electronic or human. The instructor, human or machine, can transfer

knowledge to machine and by this transfer the machine is learning. By this definition, expansion of

computer networks is considered machine learning.

But learning is not always fully efficient. Five factors weigh in.

Anti-Knowledge Key

The combination of five factors creates learning efficiency and or inefficiency:

1. Order, clarity, and structural integrity of the knowledge source.

2. The capacity and capability of the language transfer.

3. Order, clarity, and structural integrity of the knowledge destination.

4. Social coordination. No one knows everything. This limitation drives the need for social

coordination.

5. Knowledge chaos. Knowledge chaos at any level is a barrier to learning and cripples the

learning process.


The human instructor is typically specialized and knowledgeable on a category of knowledge. Through

language, the instructor transfers knowledge to the learner. Intelligence is the amount of this subset of

knowledge that is retained or “known” by the learner—be it an individual or group.

Key Question

Do you know what you do not know? Do you know what your student does not know? Do you

know what your enterprise does not know? Does your nation know what it does not know?

Etc.

Today, the answer to every one of these questions is of course, no.

Consider for a moment the impact of a learner or instructor not knowing what he or she does not know.

The result is really an aimless or a wandering learning experience in which goals are difficult.

Knowing what is known is the foundation of solid and cooperative knowledge working.

As both instructor and learner possess a finite known, each also necessarily has a definable unknown.

This mix of known and unknown among a given social group can be quite complex. The instructor

typically has more intelligence or stored knowledge than the learner and imparts this knowledge to the

learner, but knowledge cuts across disciplines and an instructor in one arena may be a learner in another

at the same time. At times the student can be more knowledgeable on a topic than his or her instructor.

This scenario is complicated even more by the fact that a computer can also be a learner.

Consider now the fact that everything you know began as a question. Questions are the realization of the

absence of knowledge structure. Recall figure 10:3.


Figure 10:3 (Copy)

A Known Unknown

This concept is a bit difficult to grasp, but from the perspective of the learner, his or her personal unknown

is already known by society. The learner’s unknown is already known.

A question is the perceived or realized lack of knowledge structure. This perception does not always take

the form of a sentence with a question mark. Any lack of clarity or logic on a topic is a question.

By this definition, study is a type of questioning. We learn by applying ourselves to the knowledge

structure around a topic and attempting to incorporate these structures into our own. This involves

continual internal questioning of our own structures and assumptions.

Anti-Knowledge Key

For all existing knowledge a counterpart question exists from the perspective of the learner.

From the perspective of the learner, society can express any known as a question.

The individual learns, or incorporates knowledge structure, through study of existing knowledge. The

learner incorporates existing knowledge into his or her personal intellect and then uses that knowledge for

pragmatic purposes in science and technology.


This concept is represented in the figure 12:1. The dark grey area represents the unknown to the learner

and the line between existing knowledge and the unknown is the line of learner questions.

Figure 12:1

While it has not traditionally been the case, it is entirely possible for learners to know what they don’t

know and have this perspective prepared for them by society.

Returning to our question, ‘Does the instructor know what the learner does not know?’ There are many

negative implications to not knowing this. Here are a few of these:

1. The learner can spend much of his or her time re-learning instead of learning. This repetition

is both time consuming and costly.

2. The learner can be required to learn knowledge for which he or she has no knowledge

context. Since all knowledge is interrelated, a learner must advance knowledge according to

its structure, one concept at a time.

3. The choice of what to can make the learning experience aimless and make the learner

unable to apply his or her knowledge in pragmatic applications of science or technology.


Power Learning

Power learning at the core involves ordered social knowledge. A social knowledge base so clear and

logical, that it is a simple event for the learner at any time to assess what he or she does not know.

Learning systems of the future will support this awareness by organizing flexible learning questions for the

learner to use in interactions with social knowledge. Figure 12: 2 illustrates this system design.

Figure 12:2

The power learning system must be able to answer a question by mirroring the knowledge base with a

question base. When all questions are exhausted, the topic is known.

Such a system would essentially keep score for the learner on that which is known and that which is not

yet known. The system would be disciplined, but fully flexible (modularized) for review and movement to

a new subject. The learner could browse through topics and ask pre-defined questions and

systematically avoid knowledge that is already known.

In this system, the learner must be given full control of rate and repetition as only the learner knows

whether or not he or she understands a concept.


The implication is that the social knowledge base is so well organized that it could generate a life-long

knowledge profile for any learner. Such a profile would totally eliminate learning waste.

The Knowledge (Creation) Machine

And now there is merely silence, silence, silence, saying All we did not know. -- William Rose

Benét, Sagacity

Current Research Efforts

Before we learn about the knowledge machine, we first need to review a few of the current efforts to

understand this concept.

Artificial intelligence (AI) is a broad concept that includes physical and mental automation. In AI mental

automation is largely modeled after the thinking of individual experts (expert systems), problem solving

scenarios (case-based reasoning) or other means to replicate the existing individual human intellectual

process.

Definitions

Artificial Intelligence (AI), n.

the branch of computer science that deal with writing computer programs that can solve problems

creatively; "workers in AI hope to imitate or duplicate intelligence in computers and robots" [syn:

AI] [2]

artificial intelligence (AI)

The subfield of computer science concerned with the concepts and methods of symbolic

inference by computer and symbolic knowledge representation for use in making inferences. AI

can be seen as an attempt to model aspects of human thought on computers. It is also

sometimes defined as trying to solve by computer any problem that a human can solve faster.


Examples of AI problems are computer vision (building a system that can understand images as

well as a human) and natural language processing (building a system that can understand and

speak a human language as well as a human). These may appear to be modular, but all attempts

so far (1993) to solve them have floundered on the amount of context information and

"intelligence" they seem to require. [3]

Summary

The goal of AI is to use computers to solve problems or make logical inferences like humans do, or that

humans can solve faster.

AI can be seen as an attempt to model human thought on computers.

Attempts thus far have floundered because of the amount of intelligence that seems to be required for

even small problems.

Artificial Intelligence research today falls generally into one of these four categories:

• Pattern recognition

• Problem solving

• Language processing

• Game playing.

Pattern recognition is in a sense, the resolution of a problem (find the pattern). Problem solving in current

artificial intelligence research is largely modeled after the thought patterns of disciplinary experts, hence

the term "expert systems". Expert systems today solve problems which model the thought pattern of the

human expert who has solved the same problem. In the business realm, expert systems have risen in

popularity and today many tasks in many corporations once performed by "experts" are now performed by

a computer system. Language processing is aimed at teaching the computer to communicate with

humans and is modeled after speech patterns exhibited by humans. Some robots are a good example of

this technology. And game playing is found in chess playing computers and the like, where the machine

competes with the human mind.

The discipline has made many strides but is largely dependent upon modeling individual thought

processes and holds no overarching theory of cognition. Cognition is defined as the act or faculty of

knowing or perceiving all information processing activities of the brain."

As stated earlier, the highest aim of artificial intelligence is human creativity and creative problem solving,

an aim not yet realized because tied up in creativity IS this overarching theory of cognition that must be


understood in order to truly model human thinking patterns. Also bound up in creativity is an

understanding of how human cognition interacts with the sum of human knowledge, studied within the

realm of the disciplines epistemology (how we know) and ontology (what we know).

"A major goal among computer scientists is the development of machines that communicate with their

environments through traditionally human sensory techniques and proceed intelligently without human

intervention. Such a goal often requires that the machine "understand" the input received and be able to

draw conclusions through some form of a reasoning process.” [4]

Anti-Knowledge Key

AI is aimed at teaching computers to think like men vs. teaching men to think like computers.

Most efforts in AI are based upon examples of the actions of the human experts or based on past

problems solved. Here is a list of several areas of AI research that I’ve described in my own words:

• Case Based Reasoning – Computer reasoning based on similar solutions to problems previously

solved.

• Genetic Algorithms - A search utility through that attempts to create pattern or order within data

using a “survival of the fittest” concept.

• Pattern Recognition – Seeks to either find a previously established pattern or to find new patterns

in data or knowledge.

• Expert Systems – Systems modeled after problem solving methods of experts.

• Neural Network - A network of simple processors that each has a small amount of memory

holding numeric data. Neural networks are highly connective and can accomplish rudimentary

learning. Neural networks extract from the data within the system and attempt to create new data

from that base through logical inference.

• Fuzzy Logic - Computer logic that represents a range of truth vs., for example, binary logic where

1 or 0 is true.

• Artificial Life - Attempts to simulate living organisms in computers.

Each of these examples is normally limited and applied to a problem scenario or a fairly narrow, specific

and somewhat predictable situation like a manufacturing floor operation or a specific scientific problem.

Problem statements are typically fairly narrow, for example, playing Chess.


There is a recurring argument among AI researchers called “neats vs. scruffies,” which is prevalent

enough to have the following entry in the computer dictionary.

Definitions

neats vs. scruffies

The label used to refer to one of the continuing holy wars in artificial intelligence research. This

conflict tangles together two separate issues. One is the relationship between human reasoning

and AI; "neats" tend to try to build systems that "reason" in some way identifiably similar to the

way humans report themselves as doing, while "scruffies" profess not to care whether an

algorithm resembles human reasoning in the least as long as it works. More importantly, neats

tend to believe that logic is king, while scruffies favour looser, more ad-hoc methods driven by

empirical knowledge. To a neat, scruffy methods appear promiscuous, successful only by

accident and not productive of insights about how intelligence actually works; to a scruffy, neat

methods appear to be hung up on formalism and irrelevant to the hard-to-capture "common

sense" of living intelligences. [3]

Summary

“Neats” try to model how humans report that they reason and believe logic is king, while “scruffies” build

any algorithm that will work, regardless of how it relates to human reasoning.

Of course, if you try to model systems that reason after humans and humans don’t know how they really

reason or cannot express how they reason, an insurmountable problem emerges.

“Scruffies” are in a different position, but still up against a wall in that they are really attempting to model

human reasoning from scratch or recreate it. This cannot be done without passing first through the terms

creativity and knowledge creation, as we have done in this book.

AI has included some efforts that are in or near knowledge creation fronts, but a holistic solution to human

creativity has eluded the discipline and this is the reason that it has not prospered.


Anti-Knowledge Key

The name artificial intelligence holds in it the confusion around this knowledge interaction.

Artificial intelligence, or artificial knowledge storage and recollection, already exists in any

computer system. Artificial knowledge creation or artificial reasoning or artificial knowledge

structuring does not yet exist.

Other related fields include cybernetics, systemics, and informatics defined as follows:

Definitions

cybernetics, n.

(biology) the field of science concerned with processes of communication and control (especially

the comparison of these processes in biological and artificial systems) [2]

Systemics - relating to systemic

adj : affecting an entire system; "a systemic poison"

informatics, n.

the sciences concerned with gathering, manipulating, storing, retrieving and classifying recorded

information [syn: information science, information processing, IP] [2]

Again, in these disciplines, the focus is on that which is known. Cybernetics is the most advanced and

has made ongoing attempts to understand the cognitive aspects of mechanized change, but a

comprehensive theory will continue to elude this discipline as well until it fully elucidates the knowledge

creation process.

The Question Machine

Through learning we extract knowledge from the global brain/intellect, but through knowledge creation we

can add knowledge to the global intellect. Recall figure 1:5 below.


Figure 1:5 (Copy)

Also recall our earlier anti-knowledge key:

Anti-Knowledge Key

A systems query as we know it today, is directed at existing knowledge and asks learning

questions. A knowledge creation query does not exist.

The ultimate search engine will create the ultimate tool for recollection of knowledge. A knowledge

machine facilitates the creation of or creates new knowledge. At the core of this machine is the

knowledge creation loop.

The Big Loop

In the world of computer programming the cycle of knowledge creation is called a loop or a line of code

that will run perpetually or until it is instructed to stop, for example, by meeting some condition. Recall the

cycle of knowledge creation.


Originated Concept

The Cycle of Knowledge Creation:


2. Question/Problem


4. Advanced Definition/Solution/Structure/Meaning

5. Return to Step 2

They key to artificial reasoning or artificial knowledge creation is to harness the computer’s power to

mechanize this knowledge creation loop.

Anti-Knowledge Key

A knowledge creation machine requires three primary elements to function.

1. A logical knowledge context.

2. The ability to specifically pinpoint questions or areas of weak logic within this context.

3. The ability to perform the knowledge creation loop, by structure questions identified new

knowledge is created.

The fundamental obstacle to mechanized knowledge advance is not an expert level of knowledge, a

particularly powerful new programming language or a new and more powerful computer chip. The

missing link is computer recognition of the question. If the computer can be taught to recognize

questions, the computer can also be taught to create knowledge.


Anti-Knowledge Key

He who asks the questions (and then answers them) creates the knowledge, man or machine.

Mechanized knowledge advance is not concerned with human or computer consciousness, human sensory

capabilities, computerized speech capability, or human emotion. None of these things is required for a knowledge

creation machine. A knowledge creation machine is a question processing machine—nothing more and nothing less.

References

[1] The New Columbia Encyclopedia, pp. 619 "Computer" (New York and London, Columbia

University Press, 1975)


[3] The Free On-line Dictionary of Computing, © 1993-2001 Denis Howe

[4] Third Edition, Computer Science, An Overview, J. Glenn Brookshear, Marquette University,

Copyright 1991 by The Benjamin/Cummings dfghjkkl;' '''Publishing Company, Inc., pp. 361,

"Artificial Intelligence".