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Knowledge Access

This is an edit of an email posted to the Bootstrap Association Unfinished Revolution mailing list. That list was for discussing how the ideas originally produced by Doug Engelbart and his team fit in with modern concepts of information processing and collaboration. This was written in 2002. There is an archived version

Elaborating on why (I think) knowledge access structures are far more important than knowledge representation formalisms.

There's a lot of context for this for which there isn't really space. If I seem to leave something out, feel free to ask me about it. These ideas are fairly rough, as I'm trying to synthesize pretty much everything I've learned in the last year or so into a simple lesson. Much or most of this is opinion. As far as I can tell none of this is particularly revolutionary, it has all been said in similar ways by other people in other places.

It is common in the information science field (that's the course of study I'm on at the moment) to categorize aspects of the human comprehension and communication of signal as data, information, knowledge and wisdom. What these things are is an open debate, and rests in the land of religion, but a model I like goes like this: the four are arranged as a pyramid with data on the bottom, information next, then knowledge, then wisdom.

Data is essentially perceived phenomena.

Information is data which has been placed in context or identified as having pattern. Factual statements about a context.

Knowledge is the discovery of new perspective through the synthesis of information. It is considered by some to be contained in individuals only, but the notion of institutional memory or knowledge throws this into doubt. It is the multi-faceted lens through which we perceive the world.

Wisdom is an ethical perspective, a distillation of knowledge that has value for a community.

Learning can be thought of as a process of gathering data, using learned perspectives to make it information, synthesizing the information into knowledge.

An adjustment in the view of this model reveals it to be dynamic. One person's knowledge can be someone else's information, from which they may create knowledge.

Okay, that's the stage. The conclusion I make is that knowledge generation is a synthetic, creative and perception altering process using information, all kinds of information, as a source of fuel.

I conceive of knowledge generation as a process of accretion. We start with some small concepts and gather new learning about them to form greater capacity to perceive. Sometimes concepts collide and we have the paradigm shift that educators love.

I do not think of knowledge generation as part of the scientific method. The scientific method is for proving or disproving recently generated knowledge. Knowledge creation might be considered hypothesis generation, except frequently we accept our knowledge without question, trusting the analogous process which has brought us to new knowledge.

Formal knowledge representations, by their nature, are closed systems and thus not knowledge at all. Some claim to be able to represent all things, but if they are in fact complete and precise languages they are, to me, by definition, resistant to synthesis. Interpretations of those people who dig them suggests they like them for precision in the processing of facts. Knowledge is not facts. Knowledge is interpretation (of all sorts of stuff, including facts).

That's problem one: formal knowledge representations are not what they think they are. Problem two is a simple question of usability and distribution of power, and leads directly into the conclusion.

Knowledge generation is a process of gathering and comparing. To effectively gather and compare one needs access to a lot of information. Human discourse is not a strict formal representation (although we could argue that language is in some ways a formal representation because it exists in social space, but that's a different discussion) so in order for it to be used in a formal representative system it needs to be translated to the new representation. There's no way to correctly automate this (with computers) and I don't think there ever will be.

This is because computers can only work with formal representations and translating human discourse is an intepretive or comparative or (if I'm getting the word right) analogous process.

Getting an army of people to translate discourse to a formal representation would be a huge process. Some might think it worthwhile. I don't. Here's why:

It is presumably possible to declare, within a subset of the population, that henceforth we will attempt to communicate with formal representations so we might engage in an orgy of precision. Unfortunately this won't work very well:

  • formal representations must be learned
  • formal representations are hard to use
  • formal representations are not very expressive or persuasive
  • formal representations exist for inference not communication

In addition to being hard to use, attempting to use formal representations for communication raises questions about elitism.

But most importantly: while a computer can easily be instructed to make inferences from a formal representation a human can't easily read them and thus can't do much in the way of knowledge generation. Yes, they can establish fact, but that's not the same thing (although it is an important thing).

Also, discourse exists to convince. Communication is an art. Facility with the language, especially nuance, makes the art. Precision is antithetical to nuance. Nuance creates interpretive resonances in the receiver which may cause them to adjust their perspective.

Formal representations are important in the way they can augment a human trying to make decisions. In a collaborative knowledge environment they are a crucial part of the picture but not the critical part.

As was made evident to me from the discussion on the portreview list, failed collaboration is the result of an inability of people to reach a shared change in perspective. A change in perspective is new knowledge. Here on the bootstrap lists the underlying motivation is solving complex and urgent problems. The belief is that through collaboration and augmentation we can make progress on problems that unaided we can not address.

Addressing information (or knowledge if you choose to use that term) is the key to creating knowledge. In order for me to learn a new thing I need to be able to compare some stuff. Likewise, in order for me to convince you, or for us reach a consensual new perspective, we need to share information, evaluate it, place it in context, and synthesize it. At the same time, we can be more effective if we can easily discover that we are treading covered ground.

So, whether the knowledge (actually information) is stored in a formal representation or in human discourse, being able to access it in effective ways is the crucial key to collaboration.

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