Australian General Semantics Society
AGS Monthly Seminar
Saturday 27 February 2010
"GS Formulations as a System"
How do we organise our GS knowledge?
I.e. What links can we make between formulations
and what main organising formulations or hubs can we find in GS, such as abstraction.
I hope this will help refresh our GS knowledge and act a a good introduction to GS as a system.
Led by David Hewson.
We “always” allow a little time for review of our lives and activities.
At this first Seminar Meeting for 2010, we had plenty of catching-up to do!
2. David’s Presentation - Objective
* To review GS formulations and get a better understanding of GS as a system.
* To help GS students organise their knowledge.
3. GS Formulation Networking
We started with some
exercises to show how communication depends on context. E.g. the
statement "Laurie should run" can be interpreted as about health and
exercise or about AGS politics and who should hold office this year, depending
on the context.
Another exercise showed up how the order we receive ideas in, also affects how we view them.
(See “Science and Sanity” p444-445 for more on this. DeBono’s book “Mechanism of Mind” also has some information on this.)
The motivation for the
talk came from a paper titled “A Network Model of Knowledge Acquisition”
They suggest the idea that organisation of knowledge in an individual’s brain can be considered a network. Network science can suggest how information can be organized in the brain. A hierarchy of three increasingly sophisticated network systems -- a random network, a small world network and a scale free network (to be discussed later) can be used to model the increasing sophistication of knowledge networks as they evolve during learning.
The aim is to help
students learn effectively, by helping them:
“... understand facts and ideas in a context of a conceptual framework and to organize their knowledge in ways that facilitate retrieval and application.”
Network science developed late in the 20th century. It grew out of our need to understand the structure and dynamics of the many complex networks in mathematics, physics, sociology, psychology and engineering. One famous general semanticist, Anatol Rapoport did some research in this area on social networks. One well known notion of “six degrees of separation” implies that an individual in a particular population can contact another individual through a directed chain of, on average, six contacts or less.
A network is a collection of objects called nodes, connected in pairs, by lines called links. For example a social network of your friends. You and each of your friends are nodes connected by links representing friendship. In a knowledge network the nodes represent formulations and the links represent relationships between them like: “part of” (e.g. the object level is part of the structural differential) or “kind of” (e.g. “fact-inference confusion” is a kind of identification between levels) or “manner” (e.g. deleting, distorting and generalising are different ways we can abstract.), etc.
A couple of properties
of networks are:
1) the average number of the links which serves as a measure of penetration of links into the network. E.g. “six degrees of separation”.
2) the cluster coefficient which measures how tightly the linked nodes are clustered together. So we have the notion that "a few large nodes carry most of the connections." The Web, for example, is "dominated by a few very highly connected nodes, or hubs... such as Google or Amazon.com.".
Networks of knowledge start off as random networks with few links and no organisation. They then move on to small world networks with more links and things begin to cluster. And finally it ends up as a scale free network, very organised around some main hubs.
So the three sorts of
1) A random network. This sort of network has nodes with few links and no overall structure. Imagine someone gives you 20% of a jigsaw puzzle pieces in a random order. The network you build from this would look like a random network. It would have mostly unlinked pieces (nodes) with a few clusters of partially linked pieces.
2) A small-world network is a type of network in which most nodes are not neighbours of one another (i.e. not linked to each other), but most nodes can be reached from every other by a small number of links. So a small world network, where nodes represent people and links show that people that know each other, captures the small world phenomenon of strangers being linked by at most 6 mutual acquaintances.
3) A scale free network develops out of a small world network when the most preferred nodes, called hubs, form centres about which most information is organised. I.e. the knowledge is clustered and organised by these hubs.
So teachers can help students identify hubs of knowledge and link what they are learning into these hubs. This will help them form the scale free network of the sophisticated learner.
We performed an exercise to see how we organise our GS knowledge. I.e. What links can we make between formulations and what main organising formulations or hubs can we find in GS? And here is our result:
Main hubs we found
(in the limited number of formulations we covered)
Structural similarity between maps and territory
Degree orientation v’s Either/Or
Extensional devices: Indexing, Dating, Etc, Quotes, Hyphens
Map territory analogy
GS assumptions (Map is not the territory, map is not all the territory, maps are self reflexive)
Happiness formula / IFD
Earlier experience affects later evaluating - order & context
And the group empahised ETC, to say that there are many more.
NB: This list will most likely change when a full analysis of GS formulations is carried out.
4. AGS Business
a. AGS National Conference in Melbourne 27-29 August,
b. UN Conference in Melbourne 30 August – 1 September,
c. Renewal of AGS memberships,
d. Issues relating to “How to Introduce GS to “new” people?
5. ~ Close ~
Sunday 14 March, at Bonnet Bay:
By Robert James.
~ 0 ~
(Updated 28 February 2010)