Types in different disciplines


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This paper discusses types in a range of disciplines.


Types in evolutionary biology. 1

Types in psychology. 3

Types in business information systems. 3

Types in software engineering. 4

Types in enterprise architecture. 4

Types in philosophy. 5


Types in evolutionary biology

A mathematician’s history of the universe might say as follows.

All mathematical types (number, operator) existed at the beginning of the universe.

Such mathematical types (triangle) are universal and more fundamental than biological types (tastiness).

Mathematical concepts (like Turing machines) are more fundamental than biological concepts (tigers).


But in a biology-inspired history of the universe, the reverse applies.

It isn’t just that tastiness and tigers were initially more important to survival than triangles and Turing machines.

It is that before life, there were no descriptions, no intensional definitions, no types.
Life, when it emerged, depended on information processing.
To identify food, friends and enemies, actors needed to match what they observe against types (however fuzzy/soft).

So, animals created and used types before humans, and the ability to describe things evolved alongside biological evolution.


“A biological approach to human knowledge naturally gives emphasis to the pragmatist view that theories function as instruments of survival.

Darwinist evolution in biology is not goal-directed with a fixed forward-looking goal; rather, species adapt themselves to an ever changing environment.”



Evidently, intelligent actors can recognise newly perceived things as similar to remembered things.

Actors can perceive a thing they observe to be an instance of one or more types they recall.

Types such as food item and predator were recognised by actors long before humans.

Such types surely persist as non-verbal memories in the minds of many kinds of animal.


An evolution-based information theory does not require there to be firm or indisputable types.

Nor firmly discrete perceptions and memories, nor firmly discrete things, nor anything that could be called digital.

It requires only that perceptions chunk the universe into units that are *discrete enough* animals can monitor and manipulate them.

And can do this *well enough* to enable survival and replication.


Kant is reported to have said something like: “stuff is out there that isn’t entirely the product of my mind and I can be certain of that. I just can't be certain of what.”

We say: “stuff is out there, and wherever my model of that stuff helps me to predict or direct what happens (and so survive), I can be sure my model is good enough.”


To recognise a thing as an instance, an actor must discriminate, must distinguish a thing from the rest of the world.

An actor has to match a perception (a description of a thing) to one or more memories (descriptions that are types).

Thus, actors can and do perceive the continuous universe as chunked in to more or less discrete entities and events.


There is always some fuzziness in biological perception and memory of things.

But evolution tends to favour animals that are more efficient and effective at pattern matching.

So some species have grown skilled at typifying things and recognising instances.

Presumably, the things they need to monitor appear sharply discrete in their perception and memory.


Evolution led to social systems in which animals communicated descriptions (where food can be found).

When humans came along, they recognised and named more artificial types.

They built business systems built around types, such as customer, product, order and payment.

They defined types so firm they seem indisputably universal, such numbers and triangles

But notice that in this biological history, these so-called universal types are recent inventions.

And they will disappear when life (and machines devised by life forms) disappears.


In short, biological evolution led to animate actors that can remember types or patterns of real world behaviour.

These types are abstract descriptions that are useful in monitoring and direct repeated activities.

The types enable actors to divide the continuity of universe into instances they can act on appropriately

The types must model reality well enough to enable survival and reproduction of the actors

The type must be accurate enough, but they are never "right".

The evolution of better types (a better model of reality) can help new actors to supplant the old.

Types in psychology

Biological evolution led to intelligence.

Intelligence gives actors the ability to abstract a type (or pattern) from similar things.

Intelligence tests usually include questions that show two instances of a type and ask you to find a third.

You are asked to perceive a pattern in two items (two graphical images, or sequences of numbers or characters).

Then invent or detect third item that relates appropriately to the first two items (rotate a figure, continue a number series).

You may also be asked to demonstrate your verbal encoding ability through word recognition tests.

Types in business information systems

Consider the varieties of set in this table.

The set of


Might be classified as

Is definable by intensional definition as

Even numbers


Mathematical set

A number divisible by 2



Biological set

An animal whose DNA conforms to the lion genome (can breed with another)



Artificial set

A plant that is flowering, woody, thorny, and perennial



Legal set

A man in the registry of births, but not in the registers of deaths and marriages


Notice that the last set is defined with reference to business information systems.

How would you define the set of ballerinas?

This is hard to pin down; does it contain every female who ever who practiced the art of ballet - even if they were inept?

If it matters to us, then we will devise an information system, a searchable register of ballerinas.

For this, we need a definition of the ballerina type: such as “a female employed in a ballerina role by one or more companies recorded in our register of ballet companies”.

And so, we need and use information systems to tell us what things fit a type, belong to a dynamic set.


Humans have formalised the communications involved in business transactions - by typifying the facts to be communicated.

First they classify the entities and events of interest to a business (order, invoice, payment etc.) and define their elements as data types.

Then design information systems to record instances of those entities and events in data stores.

Types in software engineering

Now and then, people have a vision that software design will result in a universal definition of all types people are interested in.

Around 1990, the vision was of “globally distributed objects”, in which objects of different types would sit on different computers.

And those objects would interact (like biological entities do) in a single world-wide object-oriented program.

A decade later, the vision was of “universal description”, in which one computer would hold the specifications of all reusable software components.

This universal module specification library (call UDDI, in California and owned by Microsoft) was eventually deemed futile, and stood down.

Both visions failed for many reasons; one reason being the mistaken assumption that types are universal and objective.

Types in enterprise architecture

Now and then, people have a vision that an enterprise will be able to form a corporate data dictionary that gives unique names to all the entity and event types the business is interested in

Bill Kent (in “Data and Reality”) wrote on the difficulty of sharing domain-specific types universally.

“In an absolute sense, there is no singular objective reality.

But we can share a common enough view of it for most of our working purposes, so that reality does appear to be objective and stable.

But the chances of achieving such a shared view become poorer when we try to encompass broader purposes, and to involve more people.

This is precisely why the question is becoming more relevant today: the thrust of technology is

·         to foster interaction among greater numbers of people, and

·         to integrate processes into monoliths serving wider and wider purposes.

It is in this environment that discrepancies in fundamental assumptions will become increasingly exposed.”


The subjectivity of types is now widely recognised and accepted (see Domain-Driven Design, 2003, Evans).

There are infinite potential types, but most of the types used in human and computer activity systems are useful only within a domain.

Here “useful” means useful to survival or success in a domain rather than “right”.

Types in philosophy

The existence of universal concepts is a matter of philosophical debate.

What philosophers call “universals” may also be called “ideas”, “concepts”, “properties”, “qualities”, or “types”,

These words, though interchangeable in this context, are commonly associated with different verbs.

E.g. a particular thing embodies an idea or concept, or exhibits a property or quality, or instantiates a type.


This paper rejects the classical realist positions of Plato and Aristotle.

It favours a position closer to that of more modern philosophers, such as Kant, Hegel or Peirce.

The presumption here is that you can’t have a concept (tasty, triangular, scary) without a conceiver.

The idealist philosopher says we abstract universal concepts as a means to understand, describe and deal with particular things.

Descriptions typify particular things (observed and envisaged) as instances of conceptual types.




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