The evolution of descriptive types


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Actors use types to recognise and describe things. Where do types come from?


Things become discrete in perception and memory. 1

Lower animals recognise family resemblances and vague (polythetic) types. 1

Higher animals  communicate descriptions with reference to simple types (food, friend, enemy) 2

Humans develop verbal languages to describe things in terms of types. 2

Humans develop precise (monothetic) types. 3

Humans learn to enumerate and count things of a type. 3

Humans develop artificial intelligence to create new types. 3


Things become discrete in perception and memory

Physicists consider our world to be embedded in a four-dimensional space-time continuum.

It is called a “continuum” because it is assumed that space and time can be subdivided without any limit to size or duration.

Although the real world is continuous, we perceive, describe and model that world in terms of discrete entities and events.

This work proposes the discreteness of things in models of the world evolved through biological evolution.

“At a conscious level, animals tend to interpret the world as discrete things.

We pattern match, then label (not necessarily verbal), then move our limited attention on, which is part of a survival strategy, to categorise things as safe or dangerous.” Ron Segal in discussion with the author

Lower animals recognise family resemblances and vague (polythetic) types

An animal that is able to recognise instances of a type (food item, friend, enemy) is better equipped to survive.

To begin with, animals must have recognised only fuzzy family resemblances, say attractive or repulsive.


Wittengenstein’s notion of a “family resemblances” (like many of his ideas) is not universally accepted.

These papers associate the notion of family resemblances with the concept of a “polythetic type”.

A polythetic type a broad set of criteria that are neither necessary nor sufficient to identify an instance.

Each instance of the type must possess some of the defining characteristics, but none has to be found in every instance.

Higher animals  communicate descriptions with reference to simple types (food, friend, enemy)

The survival of a social group depends on actors sharing concepts, like where food can be found.

This implies a shared understanding of types like food, direction and distance.

Many animals can communicate facts about things of interest by gestures and/or noises.


To typify is to describe something.

Less obviously, the converse is true; to describe something is to typify it.

Once you have described one thing, there is no limited to the number of similar things that may exist or be created to the description you made.

This applies all the way up from the simple descriptions/types (binary digit), to large and complex descriptions/types (the specification of a Boeing 737).

Humans develop verbal languages to describe things in terms of types

Evolution led to human-level self-awareness and verbalisation, to the use of words to describe things of interest.

Through verbalisation, we define and communicate information about countless types of things.

Consider for example the types: river, rose bush, woman, football match, planet, number and triangle.

All such types are logical abstractions, they are constructs of intelligence.


A type is an abstraction from one or more things we perceive or describe as being similar.

It gives us least a partial idea, model or description of a discrete thing we are interested in.

It describes what is true of an instance, by defining one or more properties to be found in a member of a set.

A set can be defined by extension: by enumerating the members of the set (e.g. by pointing to all the rose bushes in my garden).

More usefully to us, what typifies set members can be defined by intension, by listing properties shared by instances of the type.


This thing

is an instance that

this type

A rose

exhibits the properties of

Rosa: a plant that is bushy, thorny, flowering, woody, and perennial.


A type is a kind of template or pattern, a generalisation to which things may conform.

Fairly obviously, a thing that instantiates a named type (a rose) may have more properties than those in the intensional definition of that type (rose).

Far less obviously, that thing may have less properties than the named type, if we allow a type to be fuzzy or polythetic.

Humans develop precise (monothetic) types

Having developed the concept of a type, humans learnt how to manipulate it.

A monothetic (Aristotelian) type is a set of characteristics that are both necessary and sufficient in order to identify instances of that type.

Mathematicians developed monothetic types like number and triangle, and set theory.

E.g. The necessary and sufficient description of a prime number is that that is divisible only by 1 and itself.


This thing

is an instance that

this type


instantiates the qualities of

Even number: a number divisible by 2.

A planet

manifests the idea of

Planet: a massive body that orbits a star.

Humans learn to enumerate and count things of a type

As soon as we have a type in mind, it becomes possible to count instances of that type.

The basis of mathematics


<create and use>      <abstract quantities of>

People                 <observe and envisage >   Instances of Types


Numbers are mathematics and so much else, leading ultimately to define types in software.

All software depends for communication on the definition of types, but what about creating new types.

Humans develop artificial intelligence to create new types

In the last century, humans developed machines that can create instances of types in signals.

The by-products of evolution now include computers that can read process types and perform process instances, read data types and create data instances


The latest step is for the machine to create and use types – which is the ability required for artificial intelligence.

There are robots that can perform the magic of abstracting a general type of thing from observations of particular things.

“Theoretical results in machine learning mainly deal with a type of inductive learning called supervised learning.

In supervised learning, an algorithm is given samples that are labeled in some useful way.

For example, the samples might be descriptions of mushrooms, and the labels could be whether or not the mushrooms are edible.

The algorithm takes these previously labeled samples and uses them to induce a classifier.

This classifier is a function that assigns labels to samples including the samples that have never been previously seen by the algorithm.

The goal of the supervised learning algorithm is to optimize some measure of performance such as minimizing the number of mistakes made on new samples.” Wikipedia



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