low population complexity : unclassified mail

[DCCCOEIA1, John Ardis]

Dear Group,
This is my first post, so hello all!
I'm interested in complex systems that have low populations but complicated
participants. I understand these would be known as "Fat Agents" in the
contemporary complexity vernacular. There will be some adaptation, but
probably little I the way of identifiable emergent behaviour. I think two
competing agents would themselves - collectively - comprise a sort of CAS,
even if the mutual trajectory seemed to be dominated by chaos rather than
systemic adaptation.
The context is information warfare - intelligence, counter intelligence,
deception and counter deception (etc. etc.). My problem is that people
simplify things by throwing 95% of available information away, then they
promptly forget they simplified matters and they go on to treat complex
situations as a series of elementary, independent events. I need a language
and model to allow people to express and recognise complexity and valuable
components.
You'll see there are two sides to this; one agent striving to recognise,
express, understand or predict his own complexity (e.g. strengths, assets,
knowledge, liabilities, errors, potential), and striving to compete with
another agent, with one or both of them executing information operations
upon the other (there's more symmetry, of course, in abundance!).

I'm interested in seeing if there are ways to characterise or classify
complex systems - for example,
* Population
* Complexity of individual
* Is there emergence?
* How much adaptation is there?
* Is the adaptation stable?
* How noisy is the system?
* Does the system interact with other complex systems? (If so, how are
these characterised? Population? Complexity of individual etc. ...)

In my case, I have low population, high individual complexity, low
behavioural emergence, medium but pretty unstable adaptation, high noise,
system/system interaction (with significant similarities between systems).

I'm new to complexity theory and am probably well behind the curve on this
matter, so bear with me :-). I'd appreciate your thoughts.

Best regards,
Jas

UK MOD

[Stephen Guerin]

Hello John,

Welcome to Friam!

> I'm interested in seeing if there are ways to characterise or
> classify complex systems - for example...[snip]

I like your list of features for classification.

One shorthand we use to classify CAS that you might add is to ask where the
adaptation/learning takes place in the model. Three generic areas are:
 1) internal to the agents (eg genetic algorithm)
 2) in the agent interactions (eg edge weights in neural networks,
customer/vendor selection in supply networks)
 3) in the environment (eg pheromone fields in ant foraging)

Of course, models can have adaptation happening in multiple locations but
it's a start for classification...

So, from your brief description of your model, it sounds like most of the
learning is #1 - internal to the agents.

-Steve

> -----Original Message-----
> From: DCCCOEIA1, John Ardis [mailto:DCCCOEIA1 at dpa.mod.uk]
> Sent: Wednesday, May 17, 2006 8:34 AM
> To: 'Friam at redfish.com'
> Subject: [FRIAM] low population complexity : unclassified mail
>
>
>
> Dear Group,
> This is my first post, so hello all!
> I'm interested in complex systems that have low populations
> but complicated participants. I understand these would be
> known as "Fat Agents" in the contemporary complexity
> vernacular. There will be some adaptation, but probably
> little I the way of identifiable emergent behaviour. I think
> two competing agents would themselves - collectively -
> comprise a sort of CAS, even if the mutual trajectory seemed
> to be dominated by chaos rather than systemic adaptation.
> The context is information warfare - intelligence, counter
> intelligence, deception and counter deception (etc. etc.). My
> problem is that people simplify things by throwing 95% of
> available information away, then they promptly forget they
> simplified matters and they go on to treat complex situations
> as a series of elementary, independent events. I need a
> language and model to allow people to express and recognise
> complexity and valuable components.
> You'll see there are two sides to this; one agent striving to
> recognise, express, understand or predict his own complexity
> (e.g. strengths, assets, knowledge, liabilities, errors,
> potential), and striving to compete with another agent, with
> one or both of them executing information operations upon the
> other (there's more symmetry, of course, in abundance!).
>
> I'm interested in seeing if there are ways to characterise or
> classify complex systems - for example,
> * Population
> * Complexity of individual
> * Is there emergence?
> * How much adaptation is there?
> * Is the adaptation stable?
> * How noisy is the system?
> * Does the system interact with other complex systems?
> (If so, how are
> these characterised? Population? Complexity of individual etc. ...)
>
> In my case, I have low population, high individual
> complexity, low behavioural emergence, medium but pretty
> unstable adaptation, high noise, system/system interaction
> (with significant similarities between systems).
>
> I'm new to complexity theory and am probably well behind the
> curve on this matter, so bear with me :-). I'd appreciate
> your thoughts.
>
> Best regards,
> Jas
>
> UK MOD
>
>
>
>
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