Now ‘models’ are replacing ‘views’

Think about accounts. All businesses have to produce financial accounts for their auditors, shareholders and regulators. Back in the bad old days of BC – Before Computers – those were often the only accounts produced. Now, of course, almost every business also produces a variety of routine management accounts plus ad hoc analyses as needed.

These are possible both because we have computers to do the work and because we keep lots of financial data in databases. This data constitutes a model of the business (see my post on Types of Model). To the degree that it’s a good model all the required accounts can be derived from it. The accounts are views of the model and there are an unlimited number of valid views.

This change from creating a few predefined views to creating a model is not restricted to accounting. In fact it’s pretty general.

The trend to model building
In the past when people wanted to communicate a design or understand a thing or process they created a view of the thing or process. These views included maps, accounts and blueprints and required special materials, tools and skills. Usually sets of these were needed to define a territory, business or design and it was difficult to keep them in synch. Each kind of view was defined by a list of allowed elements or features (a meta-model); other elements and features being either ignored or indicated by annotations.

Now an organization is increasingly likely to build a digital model of the thing of interest from which it can derive any number of views. The model is also defined by a list of allowed elements or features but a longer list than for any view. From the model we can produce both familiar and novel views and there is no synchronization problem.

Examples include:

• Maps: Many maps are possible for any territory. For instance they may show or omit roads, railways, and contours. Nautical charts show almost nothing on land but a great deal about the sea. Ordance Survey has digitised its map data and derives actual maps from this resource. Many companies now have Geographical Information Systems that allow them to combine their own data with that available publicly.
• Accounts: Databases of assets and transactions support many kinds of financial and management accounts.
• Engineering design: Traditionally engineers produced plans, front and side elevations and cross-sections. CAD models can yield both blueprints and lists of parts and jobs.
• Building design: Construct IT at Salford Univ. has proposed that building projects should be based on a shared database that fully defines the building.

Being digital these models support many kinds of analysis and processing that were either impossible or very expensive when only views were available, eg calculations of load, simulation of performance or experience.

• Civil engineers can show what their constructions will look like when complete.
• Aeronautical engineers can simulate airflow and thus calculate performance and fuel efficiency.

Back to accounting

However, most accounting ‘models’ are not good enough to simulate the consequences of changes in processes or trading conditions. Some organizations have built good enough models but not, generally, as part of their accounts.

Types of model

In The Starting Point, my first post on this blog, I argued that “(B) Much knowledge can be seen as … models.” That’s obvious to some degree but it raises the question of what constitutes a model.

There are at least four kinds of model: Structural, taxonomic, developmental and causal. There are also metamodels.

In The Starting Point I argued that “(A) Knowledge is most interesting and important where it is general.” Some models are very general. Thus most fundamental models in physics can apply anywhere in the universe. Some are entirely specific, eg the UK Treasury’s model of the UK economy applies only to the UK and probably for only a few years. For now I only note this distinction. It may be desirable to formalise it at some future point.

Structural models

Structural models show the structure of an actual or proposed object. They may be physical or virtual. Structural models are used in many areas including medicine, chemistry and the various kinds of engineering.

Doctors have created generic models of the body’s skeleton, nerves, blood vessels, etc. which are used in medical education and to guide surgery. In sensitive cases exploratory operations and non-invasive scans (using X-rays, ultrasound or MRI) are used to create models of an individual patient’s body. Another recent advance has been the construction of full-size

Chemists have long used structural models of molecules to help them reason about their properties and reactions. For many years they were drawn on paper or built using rods and balls but now they are increasingly likely to be electronic. Electronic models allow calculation of, eg, molecular shapes.

Engineers used to rely on drawings to communicate their designs to clients and those who have to build them. They increasingly use 3D models which also support design work and construction. During design they enable stress calculations, simulation of performance, compatibility, etc. They may generate lists of required materials, work allocations and control data for numerically-controlled tools.

Taxonomic models

A taxonomic model is a set of categories with allocation rules. These rules are usually text for use by a human classifier but may be executable. Thus, in 1999 the BBC automated the allocation of incoming news reports to its own 5,000 news categories. Journalists use these categories to select the reports they need. The system, News On-line (NEON), replaced the people who had previously done this job.

Some taxonomies are very simple. For instance, the states of matter are solid, liquid, gas and plasma. These are often stable.

Others are very large and may evolve continuously. Well-known large-scale taxonomies include:

• In science, the Periodic Table of the Chemical elements and the Linnaean taxonomy of living things.
• In business, Standard Industry codes (SIC), the UN Product Classification, the Yellow pages categories.
• In marketing, the Mosaic set of consumer profiles.
• In information retrieval, the Dewey decimal system and the Yahoo ontology.

We sometimes find that a causal model underlies a taxonomy, eg, blood groups. Sometimes this is known first; sometimes only later. Thus:

• The distinctness of the chemical elements reflects the quantum mechanics of atomic nuclei.
• The Linnaean taxonomy reflects the evolution of living things – a phenomenon that was not understood in Linnaeus’ time.
  
• The Mosaic profiles reflect people’s lifestyle choices and resources

This also applies to sub-atomic particles and blood groups but not (so far) to genres or Standard Industry codes (SIC), the Dewey decimal system or the Yahoo ontology.

Developmental models

A developmental model asserts that its subject, eg an organism or a market, must pass through a series of stages. There are many stages models. Amongst the best-known are those developed by Piaget in the area of child development.

A well-known business example is Geoffrey Moore’s market development model:

• Innovators
• Early adopters
• Chasm
• Early Majority
• Late Majority
• Laggards

(See Crossing the chasm by Geoffrey Moore).

Like a taxonomy a developmental model may be based on a causal model or may be purely empirical.

Causal models

Causal models show how events lead to consequences.

The most basic causal models are purely indicative – little more than a list of factors that predispose to a result.

At the next step up are empirical models. These models produce forecasts by extrapolating from previous experience. Many economic and financial planning models are of this kind.

The best causal models are mathematical and allow quantitative prediction of consequences. They may be tacit or explicit. Tacit causal models may be no more than correlations. Explicit causal models, eg Newtonian mechanics, include explanations.

Some models are hybrid. Typically they use theory-based formulae where they are available and empirical formulae elsewhere. The Dupuy Insitute's Tactical Numerical Deterministic Model (see separate posting) appears to be a hybrid.

A causal model requires a taxonomy as foundation. That is, the entities in the model must be clearly defined. Sometimes the taxonomy predates the causal model but some causal models, probably including the most significant ones, require some revision of the taxonomy.

Metamodels

A metamodel is a general model that says, for one or more specific models, which features are significant and, sometimes, how they are represented.

Suppose a database contains a digital model of a gearbox. Underlying the database is a schema, an executable digital listing of the kinds of data items and relationships used to store that model. This schema is the metamodel for the gearbox model and would be equally applicable to other gearboxes and, probably, to a wide range of engineered structures.

In principle there are metametamodels – but these are only needed by, for instance, people designing new database and knowledge management systems.

TNDM: A predictive model for warfare

The Dupuy Institute's Tactical Numerical Deterministic Model (TNDM) (Economist. Technology Quarterly; 17 Sept 05) is a forecasting system with an excellent track record in forecasting the durations and casualties in armed conflicts. The Institute has a database of raw information on which its analysts perform extensive statistical analysis to identify patterns and trends. TNDM is available commercially (for $93,000 in 2005) and has been bought by both governments and arms suppliers. The Swedish government uses TNDM to propose new kinds of weapons.

The model includes many factors some technical, eg types and characteristics of weapons and armour, some geographical, eg presence of rivers, some tactical, eg disposition of troops, some logistical and even the matter of morale.

TNDM is generally more accurate than other 'war forecasting' systems because:

• It's based on real data
  
• It's model is based rigorous analysis rather than the wishful thinking of arms manufacturers and military organisations.
• It's model has been repeatedly tested against actual experience.

The success of TNDM is significant because it deals with human behaviour (which is sometimes claimed to be wholly unpredictable) and because it shows the success of empirical, 'scientific' method in an area remote from the physical sciences.

The starting point

I've been thinking about knowledge and knowledge management intermittently for over ten years. As a computer and management consultant I started from the need to manage information and knowledge in organisations and from Peter Drucker's insight that 'knowledge work' was becoming central to the economy.

I believe there's a disconnect between the two. By knowledge work Drucker meant, roughly, work done by people with formal education at or above degree level. But most of what's written about 'knowledge management' (KM) has nothing to say about the kind of knowledge that is learnt in gaining a degree. I don't say that the KM literature is worthless - only that it's incomplete in an important respect.

My own thinking started from a definition and two propositions.

My definition

Knowledge is assertions that are based on evidence. Assertions don't become knowledge because they are widely believed or found in textbooks (or holy books) or endorsed by authority (whether political, organisational or religious) but because they are supported by logic and observation.

Notice that this is NOT the same as defining knowledge as "justified true belief". Truth is not part of my definition because it cannot be definitively known.

Scientific knowledge is the form of knowledge that most clearly exemplifies my definition and it's reasonable to seek illumination from the philosophy and practice of science. There is, of course, knowledge in other areas, eg history, marketing and business management.

Of course my definition begs the question as to how much evidence is needed to to convert an assertion into knowledge. I confess that I do not (yet) have an answer to this question of which I'm confident. My best current answer is to replace the opinion/knowledge dichotomy with a scale which has prejudice at one end and established knowledge at the other. This would be consistent with Russell's advice to 'give to each proposition that degree of belief justified by the evidence'.

Two propositions

A) Knowledge is most interesting and important where it is general. Thus Newton's laws of motion are more important than the orbital parameters of any one planet. And an effective customer segmentation scheme is more important than any one customer's spending pattern.

B) Much knowledge can be seen as a model of the domain concerned. Newton's laws of motion are a mathematical model and a great deal of science can be seen as models of various kinds.

Models, generally less precise and mathematical, are also common in management science and marketing.

Some knowledge, and this is especially true of tacit knowledge, is probably not models; it's certainly not explicit models. I'll return to this point in subsequent posts.

Implications
The processes of knowledge creation and verification are important parts of knowledge management. It's useful to see them as model building and testing.