Tuesday, 1 January 2008

Machines and tacit knowledge

The knowledge management (KM) literature distinguishes between tacit and explicit knowledge. Explicit knowledge is the knowledge you can state. You can answer questions on it (so public examinations and pub quizes are both tests of explicit knowledge).

Tacit knowledge is the knowledge you have that you can't state. The usual example is riding a bicycle. Although I can do it I can't explain how I do it - or not in a way that will help you to learn to do it.

Similarly, many professionals have skills that can be seen as examples of tacit knowledge. For instance, the best sales staff can choose the right approach to each customer. The best writers can choose the best way to make each point. The best negotiators adopt the right tactics in each negotiation. The figure shows a few examples. (Method for human-powered flight is shown as passive because I don't have the required muscles for it.)

In each case the best practitioners outperform the others on objective measures. Their tacit knowledge has real business value for them and their employers.

This distinction matters because tacit and executable knowledge must generally be taught in different ways. Insofar as tacit knowledge can be taught at all it's taught through experience rather than lectures.

Most KM experts assume that this distinction is only relevant to human knowledge. All other knowledge, eg that in books, is explicit. Underlying this assumption is the further assumption that all non-human forms of knowledge are passive. That is, they can only be put to work by a person who must first make the knowledge their own.

Both these assumptions are wrong.

Knowledge can be embodied in things and machines in several ways and in some of these it can be applied by the machine. The next figure shows the possibilities.

Jigs and machine tools
A jig (when not an Irish folkdance)is a template or guide used to ease the making of multiple items to the same design. The jig thus embodies part of the design. In the pre-industrial age jigs were made by craftsmen for their own use but industrialisation led to specialisation and the making of jigs by craftsmen for use by less-skilled workers. The jig therefore replaced part of the workers' knowledge.

During the 20th century more and more of the workers' skill was replaced by machines. Jigs and semi-automatic machines were succeeded by numerically-controlled machine tools and robots. In most cases the tools and robots do not contain the design of the thing being made in an explicit form; you can't answer questions about the design by inspecting the controlling programs - or not easily. But they do contain it tacitly.

Many programs can perform tasks which, if done by a person, would require knowledge. Consider the setting of the premium for motor insurance. After you have input your details, mileage, motoring convictions and so forth to a website the insurance company runs a program which calculates a premium.

The formula used to calculate the premium is clearly present in the program and can be extracted by study. (In practice the amount of study needed may be very great. Cases in which it has proved too great for the programmers attempting the task are far from unknown.)

Before such programs existed premiums were decided by underwriters, who applied their skill and knowledge, or by clerks who applied formulae supplied by underwriters. Today's programs stand in the same relation to the underwriters as the clerks of yesteryear. They contain explicit knowledge and are active (ie executable).

Artificial neural networks
Artificial neural networks (ANN) are combinations of programs and data that mimic very simple brains. Unlike ordinary programs ANNs must be taught by being trained repeatedly on large sets of data. They can be taught to perform a variety of tasks including some, eg risk assessment, that are valuable in business. Once they have been taught they can perform these tasks reliably.

However, the knowledge that appears to be being used by an ANN cannot be found within it. This knowledge is therefore executable and tacit.

Calling this knowledge tacit is more than an analogy. ANNs work best on tasks for which humans also need tacit knowledge, eg bicycle riding. These are tasks on which people cannot be instructed and computers cannot readily be programmed. People and ANNs learn these tasks by repeated trials. There are good reasons to think that brains do work somewhat like ANNs when learning these tasks.

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