🦋Science: On Context (Quotes)

 "Some of the common ways of producing a false statistical argument are to quote figures without their context, omitting the cautions as to their incompleteness, or to apply them to a group of phenomena quite different to that to which they in reality relate; to take these estimates referring to only part of a group as complete; to enumerate the events favorable to an argument, omitting the other side; and to argue hastily from effect to cause, this last error being the one most often fathered on to statistics. For all these elementary mistakes in logic, statistics is held responsible." (Sir Arthur L Bowley, "Elements of Statistics", 1901)

"When evaluating the reliability and generality of data, it is often important to know the aims of the experimenter. When evaluating the importance of experimental results, however, science has a trick of disregarding the experimenter's rationale and finding a more appropriate context for the data than the one he proposed." (Murray Sidman, "Tactics of Scientific Research", 1960)

"Data in isolation are meaningless, a collection of numbers. Only in context of a theory do they assume significance […]" (George Greenstein, "Frozen Star" , 1983)

"Graphics must not quote data out of context." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"The problem solver needs to stand back and examine problem contexts in the light of different 'Ws' (Weltanschauungen). Perhaps he can then decide which 'W' seems to capture the essence of the particular problem context he is faced with. This whole process needs formalizing if it is to be carried out successfully. The problem solver needs to be aware of different paradigms in the social sciences, and he must be prepared to view the problem context through each of these paradigms." (Michael C Jackson, "Towards a System of Systems Methodologies", 1984)

"It is commonly said that a pattern, however it is written, has four essential parts: a statement of the context where the pattern is useful, the problem that the pattern addresses, the forces that play in forming a solution, and the solution that resolves those forces. [...] it supports the definition of a pattern as 'a solution to a problem in a context', a definition that [unfortunately] fixes the bounds of the pattern to a single problem-solution pair." (Martin Fowler, "Analysis Patterns: Reusable Object Models", 1997)

"We do not learn much from looking at a model - we learn more from building the model and manipulating it. Just as one needs to use or observe the use of a hammer in order to really understand its function, similarly, models have to be used before they will give up their secrets. In this sense, they have the quality of a technology - the power of the model only becomes apparent in the context of its use." (Margaret Morrison & Mary S Morgan, "Models as mediating instruments", 1999)

"Data are collected as a basis for action. Yet before anyone can use data as a basis for action the data have to be interpreted. The proper interpretation of data will require that the data be presented in context, and that the analysis technique used will filter out the noise."  (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"[…] you simply cannot make sense of any number without a contextual basis. Yet the traditional attempts to provide this contextual basis are often flawed in their execution. [...] Data have no meaning apart from their context. Data presented without a context are effectively rendered meaningless." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"All scientific theories, even those in the physical sciences, are developed in a particular cultural context. Although the context may help to explain the persistence of a theory in the face of apparently falsifying evidence, the fact that a theory arises from a particular context is not sufficient to condemn it. Theories and paradigms must be accepted, modified or rejected on the basis of evidence." (Richard P Bentall,  "Madness Explained: Psychosis and Human Nature", 2003)

"Mathematical modeling is as much ‘art’ as ‘science’: it requires the practitioner to (i) identify a so-called ‘real world’ problem (whatever the context may be); (ii) formulate it in mathematical terms (the ‘word problem’ so beloved of undergraduates); (iii) solve the problem thus formulated (if possible; perhaps approximate solutions will suffice, especially if the complete problem is intractable); and (iv) interpret the solution in the context of the original problem." (John A Adam, "Mathematics in Nature", 2003)

"Context is not as simple as being in a different space [...] context includes elements like our emotions, recent experiences, beliefs, and the surrounding environment - each element possesses attributes, that when considered in a certain light, informs what is possible in the discussion." (George Siemens, "Knowing Knowledge", 2006)

"Statistics can certainly pronounce a fact, but they cannot explain it without an underlying context, or theory. Numbers have an unfortunate tendency to supersede other types of knowing. […] Numbers give the illusion of presenting more truth and precision than they are capable of providing." (Ronald J Baker, "Measure what Matters to Customers: Using Key Predictive Indicators", 2006)

"Mostly we rely on stories to put our ideas into context and give them meaning. It should be no surprise, then, that the human capacity for storytelling plays an important role in the intrinsically human-centered approach to problem solving, design thinking." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"Context (information that lends to better understanding the who, what, when, where, and why of your data) can make the data clearer for readers and point them in the right direction. At the least, it can remind you what a graph is about when you come back to it a few months later. […] Context helps readers relate to and understand the data in a visualization better. It provides a sense of scale and strengthens the connection between abstract geometry and colors to the real world." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"Readability in visualization helps people interpret data and make conclusions about what the data has to say. Embed charts in reports or surround them with text, and you can explain results in detail. However, take a visualization out of a report or disconnect it from text that provides context (as is common when people share graphics online), and the data might lose its meaning; or worse, others might misinterpret what you tried to show." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"The data is a simplification - an abstraction - of the real world. So when you visualize data, you visualize an abstraction of the world, or at least some tiny facet of it. Visualization is an abstraction of data, so in the end, you end up with an abstraction of an abstraction, which creates an interesting challenge. […] Just like what it represents, data can be complex with variability and uncertainty, but consider it all in the right context, and it starts to make sense." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"Without context, data is useless, and any visualization you create with it will also be useless. Using data without knowing anything about it, other than the values themselves, is like hearing an abridged quote secondhand and then citing it as a main discussion point in an essay. It might be okay, but you risk finding out later that the speaker meant the opposite of what you thought."  (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"Statistics are meaningless unless they exist in some context. One reason why the indicators have become more central and potent over time is that the longer they have been kept, the easier it is to find useful patterns and points of reference." (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

"The term data, unlike the related terms facts and evidence, does not connote truth. Data is descriptive, but data can be erroneous. We tend to distinguish data from information. Data is a primitive or atomic state (as in ‘raw data’). It becomes information only when it is presented in context, in a way that informs. This progression from data to information is not the only direction in which the relationship flows, however; information can also be broken down into pieces, stripped of context, and stored as data. This is the case with most of the data that’s stored in computer systems. Data that’s collected and stored directly by machines, such as sensors, becomes information only when it’s reconnected to its context."  (Stephen Few, "Signal: Understanding What Matters in a World of Noise", 2015)

"Infographics combine art and science to produce something that is not unlike a dashboard. The main difference from a dashboard is the subjective data and the narrative or story, which enhances the data-driven visual and engages the audience quickly through highlighting the required context." (Travis Murphy, "Infographics Powered by SAS®: Data Visualization Techniques for Business Reporting", 2018)

❄️Systems Thinking: On Epidemics/Pandemics (Quotes)

"[…] an epidemic does not always percolate through an entire population. There is a percolation threshold below which the epidemic has died out before most of the people have." (Manfred Schroeder, "Fractals, Chaos, Power Laws Minutes from an Infinite Paradise", 1990)

"Epidemics are sensitive to the conditions and circumstances of the times and places in which they occur." (Malcolm T Gladwell, "The Tipping Point: How Little Things Can Make a Big Difference", 2000)

"Epidemics tip because of the extraordinary efforts of a few select carriers. But they also sometimes tip when something happens to transform the epidemic agent itself. This is a well-known principle in virology. The strains of flu that circulate at the beginning of each winter's flu epidemic are quite different from the strains of flu that circulate at the end." (Malcolm T Gladwell, "The Tipping Point: How Little Things Can Make a Big Difference", 2000)

"Epidemics are another example of geometric progression: when a virus spreads through a population, it doubles and doubles again, until it has (figuratively) grown from a single sheet of paper all the way to the sun in fifty steps. As human beings we have a hard time with this kind of progression, because the end result - the effect - seems far out of proportion to the cause. To appreciate the power of epidemics, we have to abandon this expectation about proportionality. We need to prepare ourselves for the possibility that sometimes big changes follow from small events, and that sometimes these changes can happen very quickly." (Malcolm T Gladwell, "The Tipping Point: How Little Things Can Make a Big Difference", 2000)

"For any given population of susceptibles, there is some critical combination of contact frequency, infectivity, and disease duration just great enough for the positive loop to dominate the negative loops. That threshold is known as the tipping point. Below the tipping point, the system is stable: if the disease is introduced into the community, there may be a few new cases, but on average, people will recover faster than new cases are generated. Negative feedback dominates and the population is resistant to an epidemic. Past the tipping point, the positive loop dominates .The system is unstable and once a disease arrives, it can spread like wildfire that is, by positive feedback-limited only by the depletion of the susceptible population." (John D Sterman, "Business Dynamics: Systems thinking and modeling for a complex world", 2000)

"It takes only the smallest of changes to shatter an epidemic's equilibrium. [...] There is more than one way to tip an epidemic, in other words. Epidemics are a function of the people who transmit infectious agents, the infectious agent itself, and the environment in which the infectious agent is operating. And when an epidemic tips, when it is jolted out of equilibrium, it tips because something has happened, some change has occurred in one (or two or three) of those areas. These three agents of change I call the Law of the Few, the Stickiness Factor, and the Power of Context."  (Malcolm T Gladwell, "The Tipping Point: How Little Things Can Make a Big Difference", 2000)

"Simply by finding and reaching those few special people who hold so much social power, we can shape the course of social epidemics. In the end, Tipping Points are a reaffirmation of the potential for change and the power of intelligent action." (Malcolm T Gladwell, "The Tipping Point: How Little Things Can Make a Big Difference", 2000)

"That is the paradox of the epidemic: that in order to create one contagious movement, you often have to create many small movements first." (Malcolm T Gladwell, "The Tipping Point: How Little Things Can Make a Big Difference", 2000)

"The existence of the tipping point means it is theoretically possible to completely eradicate a disease. Eradication does not require a perfect vaccine and universal immunization but only the weaker condition that the reproduction rate of the disease fall and remain below one so that new cases arise at a lower rate than old cases are resolved." (John D Sterman, "Business Dynamics: Systems thinking and modeling for a complex world", 2000)

"These three characteristics - one, contagiousness; two, the fact that little causes can have big effects; and three, that change happens not gradually but at one dramatic moment - are the same three principles that define how measles moves through a grade-school classroom or the flu attacks every winter. Of the three, the third trait - the idea that epidemics can rise or fall in one dramatic moment - is the most important, because it is the principle that makes sense of the first two and that permits the greatest insight into why modern change happens the way it does. The name given to that one dramatic moment in an epidemic when everything can change all at once is the Tipping Point." (Malcolm T Gladwell, "The Tipping Point: How Little Things Can Make a Big Difference", 2000)

"This is the first lesson of the Tipping Point. Starting epidemics requires concentrating resources on a few key areas. The Law of the Few says that Connectors, Mavens, and Salesmen are responsible for starting word-of-mouth epidemics, which means that if you are interested in starting a word-of-mouth epidemic, your resources ought to be solely concentrated on those three groups." (Malcolm T Gladwell, "The Tipping Point: How Little Things Can Make a Big Difference", 2000)

"We often hear warnings that some social problem is 'epidemic'. This expression suggests that the problem's growth is rapid, widespread, and out of control. If things are getting worse, and particularly if they're getting worse fast, we need to act." (Joel Best, "Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists", 2001)

"In a complex society, individuals, organizations, and states require a high degree of confidence - even if it is misplaced - in the short-term future and a reasonable degree of confidence about the longer term. In its absence they could not commit themselves to decisions, investments, and policies. Like nudging the frame of a pinball machine to influence the path of the ball, we cope with the dilemma of uncertainty by doing what we can to make our expectations of the future self-fulfilling. We seek to control the social and physical worlds not only to make them more predictable but to reduce the likelihood of disruptive and damaging shocks (e.g., floods, epidemics, stock market crashes, foreign attacks). Our fallback strategy is denial." (Richard N Lebow, "Forbidden Fruit: Counterfactuals and International Relations", 2010)

🔖Knowledge Representation: Mental Models (Part II: Breaking of Complexity)

Mental Models Series

To understand how something complex works one has two main tools  - the mechanistic, respectively the holistic approach. The mechanistic approach assumes that something can be understood by breaking it into parts (aka analysis) and then by combining the parts to form the whole (aka synthesis). However, this approach doesn’t always account for everything as there’s behavior and/or characteristics not explainable by the parts themselves. Considering that the whole is more than its parts, the holistic approach studies the interactions of the parts that lead to such unexpected effects (aka synergies), the challenge being to identify those characteristics, circumstances or conditions that lead to or related to these effects. Thus, when these two tools are combined within multiple iterations one can get closer to the essence.

When breaking things into parts we need first to look at the thing or object of study from a bird’s eyes view and identify the things that might look like parts. Even if the object of study looks amorphous, the experience doubled by intuition and perseverance can offer a starting point, and from there one can start iteratively to take things apart until one decides to stop. When and where one stops is a question of possible depth, as allowed by the object itself, by the techniques available or our grasping, respectively by the intended depth  -  the level chosen for approximation.

Between the whole and the lowest perceived components, one has the luxury of experimenting by breaking things apart (physically and/or mentally) and putting things together to form unitary parts  - parts that typically explain one or more functions or characteristics, respectively the whole. In addition, one can play with the object, consider it in a range of contexts, extrapolate its characteristics, identify behavior not explainable by the parts themselves. In the process one arrives to a set of facts (things known or proved to be true), respectively suppositions expressed as beliefs (things hold as true without proof), assumptions (things accepted as true without proof) or hypotheses (things who’s value of truth is not known, typically because of limited evidence).

One builds thus a (mental) model, an abstraction of the object of study. The parts and relations existing between the parts form the skeleton of the model, while the facts and suppositions attempt to give the model form. Unfortunately, models seldom accommodate all the facts, therefore what one ignores or considers into the model can make an important difference on whether the model is of any use. One is forced thus to advance theories on how the skeleton can accommodate the form, how form reflects the facts and suppositions.

Simple models can prove to be useful, especially when they allow approximating the real thing within the considered context. However, the better approximations one needs and/or the broader the context is, more complex the models can become, especially when the number of facts considered as important increases. This can mean that two models or theories can be useful or correct when considered in different contexts but lose their applicability when considered in another context.

Having a repository of models to choose from is usually a helpful thing, especially in understanding more about the object studied. The appropriate usage of a model depends also on understanding its range of applicability within a context or across contexts, the advantages, and disadvantages of using the model. Knowing when to use a model is as important as knowing when not to use it, while understanding the measure of the error associated with a model can make us aware of the risks associated with a model and decisions made based on it.

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🔖Knowledge Representation: Defining Mental Models (Part II)

Mental Models Series

Even if a good definition for a concept is available this doesn’t mean that people are able to recognize concept’s characteristics, usage and area of applicability. Therefore, the easiest way to understand what a concept like ‘mental model’ is about is by having one or more representative examples. Even if the examples aren’t always sufficient, they allow us in theory more chances to succeed. With this in mind, let’s consider the scenario of ‘opening a door’. 

If I need to open the door, then I can use the door knob or latch to do that. If I try that and the door doesn’t open, then I might also need a certain key (or one deals with something that isn’t a door, e.g. a false door). Only a specific key will work unless I have a master key which can open any door within the building, though not in other buildings. Supposing that I have other special skills, I could open a locked door with other objects – a big and hard object that will break the door, maybe a picklock, or any other device that will help me achieve that. Otherwise I can call somebody who has the skills or means to do it. I still need to differentiate between the different types of doors, keys or on whether I am or not entitled to enter the building.

We deal thus with a set of objects (doors, keys, knobs, latches, picklock), subjects (doorman, security man, locksmith), actions (opening, breaking, entering), heuristics (trying the knob first, first trying by myself then ask for help) and contexts (am I entitled to open the door?, can I ask for help?) which, when put together form a mental model. If we look at this mix, one can break it down to smaller units – how to open a door, a lock, how to bypass security, how to break a door, etc. All these can be considered as separate mental models which when put within a context or situation can form another mental model. Therefore, the delimitation between mental models is really thin as many mental models overlap or aggregate to handle more complex situations. 

To open a door, one even doesn’t need to know how the objects or subjects are called. Being able to use a language gives us this luxury. In contrast, a cat or a dog can open an unlocked door (when that is possible) and probably there must have some cognitive structure that allows them to recognize that they deal with something that can be opened, how it could be opened, and repeat the action when needed. Of course, they could also mimic behavior they have seen (us opening the door), though there must be a small sprung of intelligence in recognizing that by reaching to the door latch and grabbing it the door will open.

Being able to use language allows us to describe and communicate how to open a door, how to arrive from A to B, how to solve problems, etc. Labeling things and agreeing on the labels with other people helps in the process, to the degree that we can use the respective labels to communicate with people we don’t even know, or cope with unknown situations based on descriptions using the labels we know (e.g. opening a door with a touch key). It helps also that the labels carry with them some characteristics that define them and that characteristics apply to a set of objects sharing them. There are also characteristics that can slightly differ, while upon case these variances can be ignored or make a considerable difference.

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🔖Knowledge Representation: Beyond Mental Models (Part II)

Mental Models Series

One can regard the mind as a mirror which mirrors what a person perceives, however the representations don’t necessarily reflect the reality faithfully, but are simplified to the degree we are able to incorporate the reality within our mental structures and their content. We can assume that the mind represents to some degree the physical as well the inner reality, such representations being hold within the mental space – the combined aggregation of such representations. Of interest are especially mental space’s basis units and their further aggregations.

The most important unit is the one of concept. A concept is an idea having a given meaning or set of meanings, considering that concepts can have different meanings when framed in different contexts, where the context is the situation or frame in which the specific meaning can be extrapolated. A concept can be associated with multiple language-specific and context-dependent labels, where a label can be a symbol, a name or a phrase used to identify the concept. The labels associated with a concept belong to the same class of meaning as they point to the same meaning.

Meaning is by itself a unit, denoting the (personal) interpretations we associate with perceptions, concepts, symbols, events, beliefs, opinions, thoughts, understandings or aggregates of them. Meaning is formed rather by the relations existing between these elements and the place they occupy in the network formed by their relations. Think of a complex network of such components in which the associations between the components together with the components allow us grouping them together in units (of meaning), which by aggregation can form further units over and over.

Typically, a unit of meaning is associated with a concept within a given context. Meaning is created within this complex network, each change in the network having the potential to result in a change of meaning that can further propagate into the network and lead thus to further changes, though usually the changes and their impact are small, almost neglectable.

Another type of unit of meaning is represented by the mental models we form to explain, describe or model reality’s phenomena. When one attempts externalizing a mental model into a form of representation then it becomes an external model, multiple external models may thus result depending on the level of abstraction or understanding used. A mental model has the role of delimiting the scaffold that attempts explaining the concept. The essence of understanding a concept is having a mental model that reflects the structure of that concept and the meanings associated with it.

In the attempt to understand the reality, we are forced to advance theories on how the phenomena work. A theory is a reasonable explanation for facts, conditions and events carrying with it the various assumptions (ideas accepted as true) and beliefs we hold, directly or indirectly. A theory can be associated with one or more models, and thus further with mental models.

Several (mental) models can be grouped within a broader structure called a view, structure that reflects our beliefs on how one or more related concepts depict the reality. When the concepts represent the backbone of a domain then the view becomes a worldview. When together with the (mental) models are considered the theories as well other tools used in the process one talks about a paradigm. Paradigm shifts involve fundamental changes in the basis concepts, theories or models of a domain, and thus can a greater impact within our networks of meaning.

The concepts considered by all these (meta-)structures have a representation also within our mental space, even if the space can’t be clearly delimited.

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Disclaimer: Most of the definitions were adapted from the Cambridge Dictionary, however there can be important deviations from the respective source.

🔖Knowledge Representation: Defining Mental Models (Part I: A Definition)

Mental Models

A mental model (aka mental representation/image/picture) is a mental structure that attempts to model (depict, imagine) how real or imaginary things look like, work or fit together. Thus, a mental model can correspond to an object, idea, a collection of information, objects or ideas, or anything else, concrete or abstract, that the mind is thinking about [1] consciously or unconsciously. The mental structure behaves likes an ‘analogical representative’ of the thing envisioned [2] being an abstraction as certain characteristics are eventually left out, either because are irrelevant, poorly understood, not perceived or simply forgotten.

A mental model is a blend of mental content – beliefs, assumptions, concepts, meanings, thoughts, intuitions, ideas, icons, symbols, analogs, metaphors, memories and even psychological or spatial-temporal contexts, together with the relationships existing between them.  I tend to believe that a mental model doesn’t necessarily have a definite structure within our mental space given the dynamics nature of its components. We are talking here about multiple substrata of meaning that can move beneath as the currents of a sea without the sailor being aware of it. The beneath currents are nothing but pulls, pushes and swings of the mental processing taking place inside the mind.

Even if they appear to have unity, mental models are rather amorphous or multifold. The unity is apparent, derived from their iconic aspect – when we look from above, they seem to have unity, however when we look under microscope at them the unity dissipates into the multitude of relations. To delimit a mental model’s structure one can make a cut when the relationships become weaker and weaker however also this weakness is apparent. Moreover, the mental space seems to be a combination, recombination, aggregation and overlapping of mental models that resemble the structure of a hypergraph.

A mental model is also a mechanism of explaining how the mind works, how it can perceive and represent reality, think, imagine, etc. Therefore, the mental models we hold are mainly unconscious unless we make them conscious when we start to evaluate their structure, delimitate their boundaries in one form or another, respectively identify the effect they have on us.

When one attempts to externalize a mental model into a form of representation then it becomes an external model, however it might need several adjustments spread over several iterations before it can become self-contained. During this process our internal model might change as well as new understanding is gained, as gaps in understanding are filled with facts, assumptions or other mental content.

In reverse, one attempts understanding a model, the internal projection being one or more mental models depending on our understanding and its various interpretations we depict. This might happen unconsciously as we acquire knowledge or the mind does its inner workings, or consciously when an active component is involved – playing with the model, analyzing its advantages, disadvantages, areas of applicability, presumptions, etc.

Some suppose that there’s a one-to-one mapping between an external and a mental model, thing which is not necessarily true. A many-to-many relation is more plausible considering that there can be multiple interpretations of an external model, and a mental model can result in multiple external models with different abstraction levels.

Several resources consider the mental models only as representations of external things though they can be also representations of fictitious things or even processes that take place within the mind. In the end, mental models are mental analogs of physical models as they attempt to abstract some of the important features of the reality or some aspects of it. Moreover, many of the characteristics of external models apply to mental models as well.

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Resources:
[1] Anders Ericsson & Robert Pool (2016) "Peak: Secrets from the New Science of Expertise"
[2] Jean-Paul Sartre (1940) "The Psychology of Imagination"

🔖Knowledge Representation: DIKW (Part I: An Introduction)

Collocations like “Web of data”, “Web of Information” “Web of Knowledge” “Data Management”, “Information Management”, “Knowledge Management”, “Data Technologies”, “Information Technologies”, “Knowledge Technologies” and other similar such compounds involving Data, Information and Knowledge (DIK) are increasingly and interchangeably used in various contexts by vendors, customers, researchers and bloggers, sometimes being misused from confusion or exaggeration. On the other side it is true that the border between data, information and knowledge can be shallow, and from here rise up divergent opinions and theories, and as always people like to fight about their ideas and believes. To the three adds wisdom forming what is known as DIKW, the Knowledge Pyramid, however wisdom is less met in the technical landscape presumptively because it’s harder to express and quantify.

DIKW reveals the layered evolution of information from data to knowledge and further to wisdom; it’s just a wide accepted model, a pyramidal representation to which there were attempts of including proto-facts and facts [2], understanding [1] [6], transaction and intuition [7], vision and “One Mindness” [6], and probably other similar attempts might be there in the deep web. Sometimes such theories are pure philosophy apparently without applicability and quite far away from the work of scientists who are used to juggle with data, information and knowledge, relatively easy to quantify and grasp compared to wisdom, intuition, meaning, creativity, vision, consciousness, awareness or any other similar terms that made, since ancient times, philosophers’ delight. 

On the other side the scientific work is built upon the philosophic work, and seemingly as new advancements are made the “New Age” philosophies tend to be integrated in scientific work. From the concepts from the second group, only meaning has started to stir up researcher’s attention, and this mainly in AI domain, which attempts to bridge the gap between humans and machines. I suppose there are attempts also in the area of creativity, vision, intuition or wisdom, though it will be difficult to do concludent steps until the barriers of meaning are not broken.

Data is considered to be “simple isolated facts” [4], a combination of "symbols or isolated and non-interpreted facts" [5], symbols, raw that “simply exists and has no significance beyond its existence” [10], quantified or qualified symbol sets, signals or sensory stimuli perceived [3], etc. Be it facts, symbols, events, signals or sensory stimuli, it is highlighted their isolated and not-interpretable characteristics. When we think of data we refer mainly to the written symbols, independent on whether they have or not meaning for us – a digit or set of digits, a letter and the words they form, an image or any combination of them. In a apparently collection of signs, each sign could be regarded as a data chunk, though it happens that we decipher patterns of signs that we know, for example words, phone numbers, addresses, coordinates, time, etc. In database terminology data refers to a matrix structure (columns vs. records) in which each cells of the matrix contains a data chunk that nowadays could be any aggregation of signs and data types a database system supports. 

 As we can see interpretations of data could have different granularity dependent on the form of aggregation and purpose, thus a natural question: when could we discuss about information? For sure a collection of data is not information, then what ingredient(s) is/are essential in having a chunk of information? I believe that there are two magic words – relation and meaning, actually meaningful relation. Using various algorithms, and even from a simple review of the data, researchers could establish various relations between two data chunks, for example that they always occur together, that one causes the other, that XYZ is the address of John Smith, and so on. 

To make it even more complicated, for an IT professional, a structure, and I’m referring here mainly to databases, supposes already the existence of relations between data, thus statements like XYZ is the address of John Smith who was born on 1st of July and works for ABC are already available. Of course, only few relations are useful, just because two chunks of data occur together, in one or several locations, this doesn’t mean that such a relation is meaningful, while in other situations that might be quite important. 

When we discuss about a meaningful relation we actually imply a context, a multidimensional (mental) frame in which meaning is formed and derived from. Thus a relation could be meaningful in one context and irrelevant in another, and that’s why many say that data don’t equate with information. Discovering that the evolution from data to information is context dependent could maybe solve some of the philosophical disagreements, though this aspect makes it difficult to find a common accepted definition for information. Basically information is just chunks of data found in (meaningful) relation(s).

 In what concerns knowledge things are not less complex, the just formulated definition for information could be adapted from my point of view also to knowledge, advancing the idea that knowledge is information found in meaningful relation. Establishing meaningful relations between information implies a certain level of understanding, conscious or unconscious processing and systematization of information, their integration in the existing mental structures - contexts. Mental structures apply here not only to individuals but also to communities, in the past years the later gaining considerable importance. 

Communities equate with networks, and networked is also the structure of the knowledge, this involving a dynamic (evolutional) nature, with multiple values of truth, levels of organization and granularity. The systematization and integration of knowledge relies on existing or new formed patterns based on characteristics of knowledge. There were written many books about knowledge, trying to depict its characteristics, and there are so many facets that it’s difficult to find a definition that accommodates all the types of knowledge – tacit vs. explicit knowledge, personal vs. networked knowledge, declarative vs. procedural, practical vs. theoretical knowledge, personal vs. collective knowledge, contextual, situational or definitional knowledge, and so on. I find it quite important to understand the various types of characteristics because each of them comes with their own characteristics and uses.

Wisdom is skillful use of knowledge, knowledge put into use, transcended knowledge, understanding the patterns and principles of knowledge, knowing when, where, how, why and by what means to apply the knowledge. Wisdom involves also keeping the balance between the various aspects of situations, strengths and weaknesses altogether, minimizing or totally overcoming biases. In spiritual literature and tradition, wisdom plays a fundamental role, the many books on this topic attempting to reveal the qualities (dimensions) and implications of wisdom. I will limit myself only to give two quotes, namely one from A. Einstein who was remarking that "wisdom is not a product of schooling of the lifelong attempt to acquire it", and B. Russel was remarking that “although our age far surpasses all previous ages in knowledge, there has been no correlative increase in wisdom” [8].

The new added scales to the DIKW pyramid are questionable, it’s kind of a gray area, based maybe on deeper considerations not so easy to grasp, while the lack of definitions and argumentation makes the understanding process even more difficult.  The best example regarding the lack of definitions is the proto-fact, in such situations we could eventually call into help the possible etymological formation. The prefix proto-, from Greek protos, when used in compounds has the meaning of “first” or “before”, thus that proto-facts could be considered as basic/primary facts, maybe axioms, idea that matches also [5]’s view, who’s considering them as assumptions that are used to extract new facts from the world. Following this definition or assumption, proto-facts and facts are rather information, data describing them at most so I really don’t see why the facts and proto-facts are considered as the lower layer on which data layer is based, this because I see data as describing facts, mainly as quantitative or qualitative associations.

From a creationist point of view, everything is vibration, what we perceive is nothing but grossier vibrations taking various forms, some of them recepted and processed by our senses. The layer that comes into contact with the medium (environment) is called by [7] the transaction layer, where the “packets” of sensations are put onto or taken off of the medium, and before any data can exist, the packets have to be received, unpacked and assembled into data. Even if the denomination of transaction layer is not maybe the best, and the description is quite simplistic, this should be at high level the process in which the stimuli from the outside and inside world are transformed by our senses and brain in the so called “data”, forming our continuous reality. [7] believes that somewhere at this level the intuition occurs, though it could be also at higher level as long everything happens within the unconscious mind. Without going to deep into philosophical debate I would limit myself to say that I find well-founded the adding of intuition into DIKW scale, and I could put it on the same level with creativity, innovation, and vision, all three dimensions transcend knowledge and have their important role in understanding and further in wisdom’s attainment. The role of intuition and creative is reinforced also within Edward de Bono’s Six Thinking Hats, red hat representing the intuition, while the green hat, the creativity.

 Vision and “one mindness” seem to come from the New Age vocabulary though they are not so far away from modern science, vision in technical terms is reflected in forecasting, approximating the future based on past trends, while “one mindness” is the principle or the state of art behind Noosphere. It’s true, in what concerns the human being and the actual stage of development, such terms sound to be inapplicable, on the other side when we consider that everything is in continuous evolution/involution, everything is possible. From the point of view of machines (computers), given their interconnectedness and processing power everything is possible.

When we look from a relational point of view, data are symbols with no explicit relation between them, information are data with an explicit relation, knowledge is relational patterns of information, wisdom is patterns of knowledge, intuition is the comprehension of patterns without sensing the patterns consciously, creativity is the discovery or transmutation of new patterns, and maybe it worth to mention also innovation, seen by [9] as the rearranging of relations. More difficult to define from the perspective of relations are the “one mindness” and symbols. The “one mindness” could be eventually regarded as a reflection of the interconnectedness and comprehensions of all existing patterns, including the divine patterns in case of humans. Symbols are at their turn a pattern but of a lower refinement, at form-energetic level. The complexity or relations or patterns denotes the overall complexity, though our senses already hide a higher level of complexity, and most probably here are the barriers of our spiritual and scientific worlds.

In what concerns the meaning, symbols can have or not meaning, or different meanings in different contexts, same could be said about data, information and eventually knowledge, but I would expect that at wisdom and upper levels meaning is fundamental, even conscious or unconscious. With the evolution from data to wisdom and further, the meaning and the relations become richer and more complex. And we can’t discuss about meaning without discussing about context. I tend to believe data have an extrinsic context given by the medium in which they occur, though that’s not necessarily relevant. As per my above given definition, information supposes the existence of a meaningful relation and thus the placement in one or more contexts, while with the integration within a context we could talk already about knowledge.  Probably wisdom acts like a bridge point between contexts, visions extrapolates contexts while in “one mindness” everything becomes one context.

Most of the resources I’ve seen focus mainly on the evolution from data to wisdom, though there is also a feedback mechanism, for example the more high quality data we collect the more are the chances to validate current theories or discover more generalized theories, enriching thus our luggage of information, possibly further evolved to upper layers. It’s interesting also to study the interaction between the various layers, for that having to take into account also the existing theories of learning, of dynamic systems, etc. 

Note:
    I left out understanding from this post because is a complex (philosophical) concept even if everybody has an idea what is about. Please note that I haven’t tried to review the existing DIKW models, but just to write down my thoughts, to create a basis for further posts.

References:
[1] Wikipedia. (2009). DIKW. [Online] Available from: http://en.wikipedia.org/wiki/DIKW (Accessed: 7 May 2009)
[2]  Visual Concept. (1998). Making Meaning. [Online] Available from: http://www.visual-concept.co.uk/makingmeaning.htm (Accessed: 10 February 2009)
[3] Zins, C. (2007). Journal of American Society for Information Science and Technology, 58(4): 279-293, 2007. Wiley InterScience. [Online] Available from: http://www.success.co.il/is/zins_definitions_dik.pdf (Accessed: 16 May 2009)
[4] (Ken) McLennan, K. J.. "Chapter 20 - Knowledge". The Virtual World of Work: How to Gain Competitive
[5] M. J. Coombs ,  H. D. Pfeiffer ,  R. T. Hartley (). e-MGR: an architecture for symbolic plasticity. Int. J. Man-Machine Studies http://www.cs.nmsu.edu/~hdp/PDF/plastic.pdf
[6] OneMind. (2009). Seven Levels of Interaction [Online] Available from: http://onemind.com/2009/12/19/ (Accessed: 7 June 2010)
[7] G. Czerepak. (2007) TIDIKW. [Online] Available from: http://relationary.wordpress.com/2007/07/14/dikw (Accessed: 8 June 2010)
[8] B. Russel. Knowledge and Wisdom
[9] [Online] Available from:  http://thingamy.typepad.com/sigs_blog/2010/01/information-knowledge-wisdom-and-innovation.html (Accessed: 14 June 2010)
[10] G. Bellinger, D. Castro, A. Mills (2004) Data, Information, Knowledge, and Wisdom. [Online] Available from: http://www.systems-thinking.org/dikw/dikw.htm (Accessed: 14 June 2010)