Architected Futures™

Tools and strategies ... for boiling the ocean

Definitions

Data, Information, Knowledge, Wisdom

From: http://www.mindreality.com/wisdom-understanding-and-knowledge (religious in character, but these seem like useful base concepts and definitions)

Wisdom, Understanding, Knowledge, Prudence, Intelligence and Spirituality.

Wisdom is Insight. The ability to see into the underlying nature of a thing and to discern it’s true essence. To see something for what it really is. To look past appearance into substance. To read between the lines. To know the truth of a matter.

Intelligence is how well one thinks, which is dependent on how much one knows. The more wisdom, understanding and knowledge you have, the more intelligent you are. True Intelligence is not just academic, factual, worldly or social intelligence alone. True intelligence is encompassing of all intelligences.

Understanding is knowing the meaning or reason of a thing. It is about knowing why something is, and how it is so.

Prudence is foresight. The ability to see into the future of thing and to know the possible outcomes and its effect. It enables you to make decisions ahead of time that will lead things in the direction you want them to go.

Knowledge is the substance that wisdom, intelligence, understanding and prudence operate with. It isn’t just the amount you know. How a specific knowledge works with everything else you know determines whether it strengthens or weakens your wisdom, understanding and prudence.

All wisdom, understanding, knowledge and prudence increases your ability to know what is going on.

The more you can stay aware of how your mind creates your reality and environment, the more CONSCIOUS and AWARE you can become. Which will in turn lets you take back control over yourself and every area of your life.

Spirituality is about beingness. It is about the essence of who you are within. When you operate from a position of beingness, you are not playing by anyone’s rules or reasons but your own. You are real and authentic.

From http://www.systems-thinking.org/dikw/dikw.htm and attributed to Ackoff:

According to Russell Ackoff, a systems theorist and professor of organizational change, the content of the human mind can be classified into five categories:

Data: symbols

Information: data that are processed to be useful; provides answers to "who", "what", "where", and "when" questions

Knowledge: application of data and information; answers "how" questions

Understanding: appreciation of "why"

Wisdom: evaluated understanding.
Ackoff indicates that the first four categories relate to the past; they deal with what has been or what is known. Only the fifth category, wisdom, deals with the future because it incorporates vision and design. With wisdom, people can create the future rather than just grasp the present and past. But achieving wisdom isn't easy; people must move successively through the other categories.

A further elaboration of Ackoff's definitions follows:

Data... data is raw. It simply exists and has no significance beyond its existence (in and of itself). It can exist in any form, usable or not. It does not have meaning of itself. In computer parlance, a spreadsheet generally starts out by holding data.

Information... information is data that has been given meaning by way of relational connection. This "meaning" can be useful, but does not have to be. In computer parlance, a relational database makes information from the data stored within it.

Knowledge... knowledge is the appropriate collection of information, such that it's intent is to be useful. Knowledge is a deterministic process. When someone "memorizes" information (as less-aspiring test-bound students often do), then they have amassed knowledge. This knowledge has useful meaning to them, but it does not provide for, in and of itself, an integration such as would infer further knowledge. For example, elementary school children memorize, or amass knowledge of, the "times table". They can tell you that "2 x 2 = 4" because they have amassed that knowledge (it being included in the times table). But when asked what is "1267 x 300", they can not respond correctly because that entry is not in their times table. To correctly answer such a question requires a true cognitive and analytical ability that is only encompassed in the next level... understanding. In computer parlance, most of the applications we use (modeling, simulation, etc.) exercise some type of stored knowledge.

Understanding... understanding is an interpolative and probabilistic process. It is cognitive and analytical. It is the process by which I can take knowledge and synthesize new knowledge from the previously held knowledge. The difference between understanding and knowledge is the difference between "learning" and "memorizing". People who have understanding can undertake useful actions because they can synthesize new knowledge, or in some cases, at least new information, from what is previously known (and understood). That is, understanding can build upon currently held information, knowledge and understanding itself. In computer parlance, AI systems possess understanding in the sense that they are able to synthesize new knowledge from previously stored information and knowledge.

Wisdom... wisdom is an extrapolative and non-deterministic, non-probabilistic process. It calls upon all the previous levels of consciousness, and specifically upon special types of human programming (moral, ethical codes, etc.). It beckons to give us understanding about which there has previously been no understanding, and in doing so, goes far beyond understanding itself. It is the essence of philosophical probing. Unlike the previous four levels, it asks questions to which there is no (easily-achievable) answer, and in some cases, to which there can be no humanly-known answer period. Wisdom is therefore, the process by which we also discern, or judge, between right and wrong, good and bad. I personally believe that computers do not have, and will never have the ability to posses wisdom. Wisdom is a uniquely human state, or as I see it, wisdom requires one to have a soul, for it resides as much in the heart as in the mind. And a soul is something machines will never possess (or perhaps I should reword that to say, a soul is something that, in general, will never possess a machine).

Gene goes on to state:

Personally I contend that the sequence is a bit less involved than described by Ackoff. The following diagram represents the transitions from data, to information, to knowledge, and finally to wisdom, and it is understanding that support the transition from each stage to the next. Understanding is not a separate level of its own.

Data represents a fact or statement of event without relation to other things.

Ex: It is raining.

Information embodies the understanding of a relationship of some sort, possibly cause and effect.

Ex: The temperature dropped 15 degrees and then it started raining.

Knowledge represents a pattern that connects and generally provides a high level of predictability as to what is described or what will happen next.

Ex: If the humidity is very high and the temperature drops substantially the atmospheres is often unlikely to be able to hold the moisture so it rains.

Wisdom embodies more of an understanding of fundamental principles embodied within the knowledge that are essentially the basis for the knowledge being what it is. Wisdom is essentially systemic.

Ex: It rains because it rains. And this encompasses an understanding of all the interactions that happen between raining, evaporation, air currents, temperature gradients, changes, and raining.

http://www.incose.org/hra/docs/2011/INCOSE%20Presentation%20April%2019th.pdf has an interesting perspective. It states:

Research is defined by six interrogatives which are disseminated into two groups: those that relate to information and those that relate to knowledge.

This is on a page with a graphic which shows six circles, broken into two group, on labeled information, the other labeled knowledge. The four circles in the information group are detailed as:

  • Who (people)
  • What (entities)
  • Where (locations)
  • When (time)

The two circles in the knowledge group are detailed:

  • How (functions)
  • Why (purpose)

From Dr. Russell Ackoff on Systems Thinking - Pt 2:

"Why questions about objects called systems cannot be answered by the use of analysis. Now, answers to why questions are called explanations. And the product of explanations is understanding. And what we became aware of in the 1950's was that science produces no understanding. It produces knowledge. Because the product of analysis is how things work. Never why they work they way they do. We needed a new way of thinking to provide explanation, and therefore understanding. Explanations always lie outside the system, never inside. Analysis takes you into the system and how it works, it provides knowledge, but not understanding."

Context, Environment

According to Russell Ackoff in his paper Toward a System of Systems Concepts:

The environment of a system is a set of elements and their relevant properties, which elements are not part of the system but a change in any of which can produce a change in the state of the system. Thus a system's environment consists of all variables that can affect its state. External elements that affect irrelevant properties of a system are not part of its environment.

I don't know that Ackoff provides a definition for "context." From the definition provided below by Jack Ring, Ackoff's definition of environment seems to correspond with Ring's definition of context, but with a notable exception. Ackoff's definition identifies environment as external elements which can produce change in the state of the system. Ring's definition of context is external elements that interact with system. Merriam Webster defines 'interact' as 'to act one upon the other.' This would seem to include both unidirectional and bidirectional activity. Context is usually taken to include both bidirectional and unidirectional action. Ackoff's definition seems to exclude external entities upon which unidirectional internal-to-external action is involved and where outcomes are created but no feedback mechanism exists1.

Context is everything not System but that interacts with system. Environment is everything not Context and System.
ISO standards confuse these distinctions or worse, use them interchangeably.

- Jack Ring

http://www.incose.org/hra/docs/2011/INCOSE%20Presentation%20April%2019th.pdf (also noted above) discusses "executable architecture" and "executable context"

Purpose, Purposeful, Purposive

An ongoing discussion point regarding systems, the systems approach, and systems thinking is the idea of a system having purpose. Which often boils down to an inquiry into "where did this purpose come from?"

The purpose of system behavior can be described from at least two important perspectives according to Peter Checkland:

  • Purposeful behavior, describes as behaviour that is willed – there is thus some sense of voluntary action.
  • Purposive behavior, behaviour to which an observer can attribute purpose.

Using this distinction, only systems which have a capacity to willfully direct their own behavior can be purposeful. All other system can be purposive, but the purpose may be different for different stakeholders and from different viewpoints. Indeed, purposeful behavior can also be described as purposive with the two purposes being different and at times even opposed.

Russell Ackoff also provided a distinction between purposeful and purposive in his paper Toward a System of Systems Concepts. In that paper he provides the following definitions:

A goal-seeking system is one that can respond differently to one or more different external or internal events in one or more different external or internal states and that can respond differently to a particular event in an unchanging environment until it produces a particular state (outcome). Production of this state is its goal.

A multi-goal-seeking system is one that is goal-seeking in each of two or more different (initial) external or internal states, and which seeks different goals in at least two different states, the goal being determined by the initial state.

A purposive system is a multi-goal-seeking system, the different goals of which have a common property. Production of that common property is the system's purpose. These types of systems can pursue different goals, but they do not select the goal to be pursued. The goal is determined by the initiating event. But such a system does choose the means by which to pursue its goals.

A purposeful system is one which can produce the same outcome in different ways in the same (internal or external) state and can produce different outcomes in the same and different states. Thus a purposeful system is one which can change its goals under constant conditions; it selects ends as well as means and thus displays will. Human beings are the most familiar examples of such systems.

Ackoff's definitions seem to go beyond Checkland's, but they do not necessarily refute. Rather they add extra distinctions. In both cases purposeful is specified as willful. Purposive is specified as having a purpose that is not willfully, internally determined. Ackoff's definition seems to specify purposeful as a distinction from purposive. Checkland's definition seems to be more a matter of definition of determination of purpose by means of source, where purposeful can only be attributed to systems capable of willful action; but these then are a subset of purposive systems where external observers may attribute purposes to a willful system that are in addition to, and potentially in conflict with, their claimed purposeful goals.

Another viewpoint related to purpose is a line of thought advanced by some systems thinkers which attempt to focus on the definition or examination of systems based on accomplishment or activity. One example of this is found in the following quote from Jack Ring made in a Systems Thinking World discussion group:

System thinking does not obsess on what a system IS. It sees what a system DOES and parses out all the IS of the system that does not contribute to any given DOES.

This seems a useful approach in the decomposition of systems when trying to define what low level compositional elements matter as parts of the system, and what elements can be considered as noise. 

Reference

Properties and Attributes

This arose from a part of the dialog in the "Systems have architectures?" discussion on STW. In that discussion thread Jack Ring made the comment:

Regarding architecture, I doubt that any system "has" (posesses) architecture. I suggest that a system manifests content and structure. However, architecture, like beauty, is conditional on the eye of the beholder. Two people looking at one system may see two different structures, infer different purposes and reach different conclusions about how well the structure serves the purpose they presume and will even disagree on how well the structure serves the purpose posed by the other person. And just because they agree does not make it truth. Both groupthink and clanthink are always lurking..

IMO every system has content and structure whereas architecture, like complexity, is in the eye of the beholder.

This brought back to mind the struggle with determining how to define and create an appropriate set of 'attribute' relationships for specification of AIR/EATS models. The early basis for determination of what might be necessary and/or useful came from software development and OO modeling. Key concepts include the differentiation between "is" and "has" in class design.

The thought brough forward with Jack's statement reflects a need for a type of relationship where the nature of the attribute is determined more by the observer than the system's own intrinsic attribututes. Thus, 'architecture' might be best represented as an 'associative class' depending on the observer. This could be a new and interesting change within the EATS model.

Relative to some aspects, such as Jack's statement about structure, there may be multiple views. At a physical, granular level, there may be facts and physical truth to be determined relative to composition. However, at a higher level, say at a logical or conceptual modeling of the same system, different observers may see different sub-systems coming into play, configured in different ways and purposed to different objectives.

One of the potential benefits of EATS would be in the ability to simultaneously model these and compare, contrast and audit them - including the ability to share perspectives across the universe of observers.

Reference
  • Google "what are manifest characteristics"
  • Google "manifest definition"
  • Google "Property Definition"
  • Google "attribute definition"
  • 1. The existence of a feedback loop, even indirect, would create a situation where the external entity could produce a change in the state of the system. Intentional outcomes of system activity tend to be monitored with feedback loops to evaluate performance. Unmonitored outcomes tend to fall into the category of 'unintended consequences.' Ring's definition of 'context' seems to include unintended consequences. Ackoff's environment doesn't seem to include them.

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