C a l l    f o r   C o n t r i b u t i o n s
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Special Issue of the ACM Transactions on Autonomous and Adaptive Systems on
Self-Adaptive Systems: Models & Algorithms
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---

--------
Scope
--------
The present special issue is concerned with concepts and techniques which can
rely on metaphors of nature and which are inspired from biological and cognitive
plausibility. Being the basis for many modeling approaches and computational
techniques, this two-fold plausibility offers a very promising foundation for
investigating the adaptivity of intelligent systems that evolve in dynamically
changing environments. The modeling approaches involve a large spectrum of
theories ranging from learning theory to nature inspired optimization
meta-heuristics.

Of particular importance are those approaches that allow for incremental update
of the systems over time. Such approaches are often referred to as sequential or
constructive. Despite the existing literature on adaptivity, the notion of
"incrementally" as a property of self-adaptation, self-organization,
self-monitoring and self-growing has not yet been well studied. This special
issue aims at presenting the latest advances of self-adaptivity with focus on
modeling approaches and computational methods that suit dynamically changing
environments. The special issue is intended for a wide audience including neural
network scientists, mathematicians, physicists, engineers, computer scientists,
biologists, economists and social scientists. The special issue will cover
various topics of self-adaptivity, self-organization, self-monitoring and
self-growing concepts. It also aims at presenting a coherent view of these
issues and a thorough discussion about the future research avenues. A sample of
the targeted topics, which is suggestive rather than exhaustive, includes:

Theories and Algorithms
- Self growing systems
- Adaptation in changing environments
- Online adaptive and life-long learning
- Plasticity and stability of systems
- Incremental adaptive neuro-fuzzy systems
- Incremental and single-pass data mining
- Incremental classification systems
- Incremental clustering
- Concept drift in incremental systems
- Self-monitoring in incremental systems
- Incremental diagnostics
- Novelty detection in incremental learning
- Incremental feature selection and reduction
- Adaptive decision systems
- Principles of self-organization
- Methodologies of self-organization
- Dynamic optimization
- Accommodating adaptation in:
     * Neural networks
     * Evolutionary computation
     * Swarm intelligence
     * Fuzzy systems
Applications: Adaptivity and learning in
- Smart systems
- Ambient / ubiquitous environments
- Distributed intelligence
- Intelligent agent technology
- Robotics
- Industrial applications
- Internet applications
- E-commerce, etc

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Schedule
----------
Submission due date: December 15th, 2009
Notification Acceptance: May 15th, 2010
Submission of camera ready version: July 15th., 2010
Publication: Early 2011

------------
Submission
------------
Manuscripts should be submitted to the Special Issue of TAAS on "Self-Adaptive
Systems: Models & Algorithms" following the formatting guidelines of the journal
available at: http://taas.acm.org/ and must be sent to one of the guest editors.
The editors will check whether the submissions are within the scope of the
special issue before they go through the review process.

--------------
Guest Editors
--------------
Hamid Bouchachia, University of Klagenfurt, Austria, hamid@...
Nadia Nedjah, State University of Rio de Janeiro, Brazil, nadia@...

COMPLEX SYSTEMS AND SOCIAL SIMULATIONS
SUMMER SCHOOL 2009

Central European University, Budapest, Hungary
JULY 13 - 24, 2009

www.sun.ceu.hu/complex

Application deadline: 5 March, 2009

Course Directors:
Laszlo Gulyas, Collegium Budapest / ELTE, Department of History and
Philosophy of Science, Hungary
Gyorgy Kampis, Collegium Budapest, Focus Group on the Philosophy of
Complexity / ELTE, Department of History and Philosophy of Science,
Hungary

Faculty:
Petra Arhweiler, University of Hamburg / University of Bielefeld,
Faculty of Sociology, Germany
Albert-Laszlo Barabasi, Northeastern University, Department of Physics
/ Center for Cancer Systems Biology, Dana Farber Cancer Institute,
Harvard University, US
Flaminio Squazzoni, (to be confirmed)
Ferenc Jordan, Animal Ecology Research Group of the Hungarian Academy
of Sciences (HAS), Budapest, Hungary
Imre Kondor, Collegium Budapest, Hungary
Scott Page, University of Michigan, Ann Arbor, USA
Klaus G. Troitzsch, University of Koblenz-Landau, Germany
Balazs Vedres, CEU, Center for Network Science, Budapest, Hungary
________________________________________

The summer school is aimed at providing a state-of-the-art cutting-
edge scientific and research-oriented training for junior faculty,
young researchers, postdoctoral fellows, MA and Ph.D. students, and
professionals from European and overseas universities and research
institutes on complex systems and social simulations.

The term Complex Systems (CSS) denotes an interdisciplinary research
methodology currently successful in the social sciences and elsewhere.
CS research originated from physics and nonlinear systems some decades
ago but its models have soon permeated such distant fields as economy,
political science or more recently sociology. As implied by the name,
a CS is essentially a system of many complicated interactions. Complex
Systems methodology has developed sophisticated yet well understood
tools to cope with this challenge. In social systems the essence of CS
is the characterization of the distributed dynamics of how the
interaction of many actors and variables leads to predictable
phenomena, which often involve hierarchy, emergence, dynamic
structures and large scale transitions.

Each day in the course focuses on one tool of this encompassing
methodology. CS methods include various mathematical models (nonlinear
systems, networks, statistical approaches), computer simulations (e.g.
systems dynamics, agent-based modeling). CS simulations are highly
computation intensive and pose problems of supercomputing and
parallelization.

The CSSS course offers lectures, tutorials and discussions on the
whole spectrum of the above. Lectures are from leading experts,
specifically focusing on CS concepts, modeling and (social)
simulation, followed by discussion.

:-) Only a few tumbleweeds are left which tumble
across the site.. I have setup a new blog here:
http://blog.cas-group.net/
A wordpress blog offers better functionality
for embedding pictures and videos, and the
Yahoo! groups have improved very little over
the years. After all the embarrassing operations
of Yahoo! in the last months I thought it is
maybe time to try something else..
As you see, this group is still open for
discussions, though.

-J.


--- In CAS-Group@yahoogroups.com, "tedsaid21" <tedsaid@...> wrote:
>
> Hey ... what's going on with the group?  Did everyone move and not
> tell me?  The last message is dated Oct. 14th.
>
> (Sheesh ... can't a guy skip a month or two without getting left out.)
>
> cheers,
>
> Ted
>

Hey ... what's going on with the group?  Did everyone move and not
tell me?  The last message is dated Oct. 14th.

(Sheesh ... can't a guy skip a month or two without getting left out.)

cheers,

Ted

I would like to inform you that CASoN 2009 Conference will take place
in June from the 24th to the 27th in Fontainebleau, France. It is a
conference technically sponsorded by IEEE with the cooperation of ACM
SIGAPP. The official website of the conference can be view on the
following address: http://www.mirlabs.org/cason09/

I believe that almost every biological, social and economic system is
a complex system. Hence they are ubiquitous.
E.Ahmed

--- In CAS-Group@yahoogroups.com, "Jochen Fromm" <Jochen.Fromm@...>
wrote:
>
>
> Determining the fractal dimension of an image is not
> difficult, you can use for example the box counting method.
> In complex networks, often the degree distribution is
> used to characterize a network (the degree of a node
> in a network is the number of connections it has to
> other nodes). A scale-free network is for example a
> network whose degree distribution follows a power law.
>
> -J.
>
> --- In CAS-Group@yahoogroups.com, "michael_mckosky" <mmckosky@>
wrote:
> >
> > Jochen,
> > (I will try to respond again, sorry if the first attempt got to
you)
> > I was hoping to see something having to do with the
dimensionality of
> > complexity.
> > I had in mind the method used to identify/confirm the work of
Jackson
> > Pollack, were his work had a characteristic fractional dimension,
and
> > his imitators had a lower dimension.
> > I also visualized the connection graphs of the Internet web
sites. It
> > seems to me that some sort of dimensional measure could be taken
to
> > give an idea of complexity, related not only to the number of
parts but
> > to the connections amongst the parts.
> > Mike
> >
>

Determining the fractal dimension of an image is not
difficult, you can use for example the box counting method.
In complex networks, often the degree distribution is
used to characterize a network (the degree of a node
in a network is the number of connections it has to
other nodes). A scale-free network is for example a
network whose degree distribution follows a power law.

-J.

--- In CAS-Group@yahoogroups.com, "michael_mckosky" <mmckosky@...> wrote:
>
> Jochen,
> (I will try to respond again, sorry if the first attempt got to you)
> I was hoping to see something having to do with the dimensionality of
> complexity.
> I had in mind the method used to identify/confirm the work of Jackson
> Pollack, were his work had a characteristic fractional dimension, and
> his imitators had a lower dimension.
> I also visualized the connection graphs of the Internet web sites. It
> seems to me that some sort of dimensional measure could be taken to
> give an idea of complexity, related not only to the number of parts but
> to the connections amongst the parts.
> Mike
>

Jochen,
(I will try to respond again, sorry if the first attempt got to you)
I was hoping to see something having to do with the dimensionality of
complexity.
I had in mind the method used to identify/confirm the work of Jackson
Pollack, were his work had a characteristic fractional dimension, and
his imitators had a lower dimension.
I also visualized the connection graphs of the Internet web sites. It
seems to me that some sort of dimensional measure could be taken to
give an idea of complexity, related not only to the number of parts but
to the connections amongst the parts.
Mike

I agree, it depends how you look at it. It's all in a human mind. Give
me any system that you can think of and I bet that someone is capable
of creating a classical model of it and a complex model of it.
Sometimes the classical approximation works best, sometimes the
complex one works best. But that is tied to human purpose: what is the
model *for*.

Cheers,
Telmo.

On Thu, Oct 9, 2008 at 8:41 PM, Jochen Fromm <Jochen.Fromm@...> wrote:
> Are complex systems the most frequent systems
> we can find? It depends where you look at. On
> a very large scale, the Italian researcher is correct.
> If you consider the entire galaxy or universe,
> then the systems by which we are surrounded in daily
> life are very complex systems. Entropy increases
> everywhere, only in small pockets we can find
> exceptions. The earth can be regarded as such
> an exception, like an island in a variety
> of simple systems.
>
> On the earth itself one can find complex systems
> everywhere - especially at places where life is.
> Complexity can be found everywhere where evolution
> is at work,
>
> * in all living organisms which are subject
> to evolution
>
> * in all evolving complex adaptive systems which
> have a long historical background
>
> * in software development, esp. where systems have
> grown over a long period of time
>
> Designed systems can be complex, too, but more often
> "complicated" is a better description. Complicated
> systems are like machines, they are designed with
> a certain purpose, every part as a certain function,
> and one key defect (in one of the many critical parts)
> brings the entire system to a halt.
>
> -J.
>
> --- In CAS-Group@yahoogroups.com, "gaia" <gaia.scagnetti@...> wrote:
>>
>> Dear all,
>> I have wrote this question to another group about complexity
>> but i would like to have your opinion/help on this issue:
>>
>> I'm writing my PhD thesis in Communication Design for complex
>> systems social integration. In the second chapter I quote this
>> sentence from an Italian researcher on complexity (I made
>> a quick translation):
>> "If you consider all the systems by which we are surrounded, the
>> complex systems are regarded as exceptions, like islands in a
>> variety of simple or complicated systems." It sounded a good
>> point to me, as I'm very tired of everyone saying that
>> everything is complex and I was trying to take some clear
>> position in saying which characteristics a system should have
>> to be considered as complex.
>> But Yesterday I had a long discussion about it with another
>> researcher; he thought that if we look at the world at least
>> everything is complex, or, in other words, that complex systems
>> are more frequent than the simple/complicated ones.
>> My position was that is different to say what is complex as an
>> adjective and what is a complex system as a particular kind of
>> system. And that I do think that the most of our world is
>> complex but when I talk about complex system I refer to such
>> kind of systems that has specific characteristics (high number
>> of elements, connections, non linearity, feedback and loop,
>> etc etc).
>> He answer that the sun-earth-moon system is complex even if
>> it doesn't have a high number of elements.
>>
>> As all the researchers, I'm never sure of anything and now
>> I'm in doubt. What do you think about it?
>> Complex systems are the islands in a variety of simple
>> or complicated systems, or are the most frequent systems we
>> can find?
>>
>> Gaia
>>
>
>

Dear all,
I have wrote this question to another group about complexity
but i would like to have your opinion/help on this issue:

I'm writing my PhD thesis in Communication Design for complex systems
social integration.
In the second chapter I quote this sentence from an Italian researcher
on complexity (I made a quick translation):
"If you consider all the systems by which we are surrounded, the
complex systems are regarded as exceptions, like islands in a variety
of simple or complicated systems." It sounded a good point to me, as
I'm very tired of everyone saying that everything is complex and I was
trying to take some clear position in saying which characteristics a
system should have to be considered as complex.
But Yesterday I had a long discussion about it with another
researcher; he thought that if we look at the world at least
everything is complex, or, in other words, that complex systems are
more frequent than the simple/complicated ones.
My position was that is different to say what is complex as an
adjective and what is a complex system as a particular kind of system.
And that I do think that the most of our world is complex but when I
talk about complex system I refer to such kind of systems that has
specific characteristics (high number of elements, connections, non
linearity, feedback and loop, etc etc).
He answer that the sun-earth-moon system is complex even if it doesn't
have a high number of elements.

As all the researchers, I'm never sure of anything and now I'm in doubt.

What do you think about it?
Complex systems are the islands in a variety of simple or complicated
systems, or are the most frequent systems we can find?

Gaia

Are complex systems the most frequent systems
we can find? It depends where you look at. On
a very large scale, the Italian researcher is correct.
If you consider the entire galaxy or universe,
then the systems by which we are surrounded in daily
life are very complex systems. Entropy increases
everywhere, only in small pockets we can find
exceptions. The earth can be regarded as such
an exception, like an island in a variety
of simple systems.

On the earth itself one can find complex systems
everywhere - especially at places where life is.
Complexity can be found everywhere where evolution
is at work,

* in all living organisms which are subject
   to evolution

* in all evolving complex adaptive systems which
   have a long historical background

* in software development, esp. where systems have
   grown over a long period of time

Designed systems can be complex, too, but more often
"complicated" is a better description. Complicated
systems are like machines, they are designed with
a certain purpose, every part as a certain function,
and one key defect (in one of the many critical parts)
brings the entire system to a halt.

-J.


--- In CAS-Group@yahoogroups.com, "gaia" <gaia.scagnetti@...> wrote:
>
> Dear all,
> I have wrote this question to another group about complexity
> but i would like to have your opinion/help on this issue:
>
> I'm writing my PhD thesis in Communication Design for complex
> systems social integration.  In the second chapter I quote this
> sentence from an Italian researcher on complexity (I made
> a quick translation):
> "If you consider all the systems by which we are surrounded, the
> complex systems are regarded as exceptions, like islands in a
> variety of simple or complicated systems." It sounded a good
> point to me, as I'm very tired of everyone saying that
> everything is complex and I was trying to take some clear
> position in saying which characteristics a system should have
> to be considered as complex.
> But Yesterday I had a long discussion about it with another
> researcher; he thought that if we look at the world at least
> everything is complex, or, in other words, that complex systems
> are more frequent than the simple/complicated ones.
> My position was that is different to say what is complex as an
> adjective and what is a complex system as a particular kind of
> system. And that I do think that the most of our world is
> complex but when I talk about complex system I refer to such
> kind of systems that has specific characteristics (high number
> of elements, connections, non linearity, feedback and loop,
> etc etc).
> He answer that the sun-earth-moon system is complex even if
> it doesn't have a high number of elements.
>
> As all the researchers, I'm never sure of anything and now
> I'm in doubt. What do you think about it?
> Complex systems are the islands in a variety of simple
> or complicated systems, or are the most frequent systems we
> can find?
>
> Gaia
>

Dear all,
I have wrote this question to another group about complexity
but i would like to have your opinion/help on this issue:

I'm writing my PhD thesis in Communication Design for complex systems
social integration.
In the second chapter I quote this sentence from an Italian researcher
on complexity (I made a quick translation):
"If you consider all the systems by which we are surrounded, the
complex systems are regarded as exceptions, like islands in a variety
of simple or complicated systems." It sounded a good point to me, as
I'm very tired of everyone saying that everything is complex and I was
trying to take some clear position in saying which characteristics a
system should have to be considered as complex.
But Yesterday I had a long discussion about it with another
researcher; he thought that if we look at the world at least
everything is complex, or, in other words, that complex systems are
more frequent than the simple/complicated ones.
My position was that is different to say what is complex as an
adjective and what is a complex system as a particular kind of system.
And that I do think that the most of our world is complex but when I
talk about complex system I refer to such kind of systems that has
specific characteristics (high number of elements, connections, non
linearity, feedback and loop, etc etc).
He answer that the sun-earth-moon system is complex even if it doesn't
have a high number of elements.

As all the researchers, I'm never sure of anything and now I'm in doubt.

What do you think about it?
Complex systems are the islands in a variety of simple or complicated
systems, or are the most frequent systems we can find?

Gaia

An interesting article, but unfortunately
I don't think it is possible. The illustration
in the article shows the 20 amino acids
used to make proteins and the eight nucleic acids
used to code DNA and RNA (although there are
only 4 basic ones: Adenosine, Guanosine, Uridine,
and Cytidine). This is all well known. The question
remains what the functions of all the possible
genes and proteins are.

Yet the "glycome" as a set of Glycans similar to
the genome (set of genes) and proteome (set of
proteins) is interesting and new.

Obviously animals have a strong desire to
get as much sugar and fat as they can. These
are the things that taste well, and they are the
things we are made off: glycans/sugars are used
for the structure inside the cell, lipids/fat
for the structures outside the cell - the cell
membranes.

While genes are used to encode how the body is
built, sugar and fat are actually building it.
Sugar also seems to be a kind of fuel for the
body - it is the main energy source. The function
of fat includes energy storage and providing
structure.

I have doubts about the promise the article makes
(that these cellular components – the glycans and
lipids – may now hold the keys to uncovering the
origins of many grievous diseases that continue
to evade understanding). I don't think that
the origin of many serious diseases (for instance
cancer) is a single mysterious molecule that
hasn't been identified yet. Even if there are
only 68 molecular building blocks used to construct
the fundamental components of cells, there are
endless possibilities how one can arrange these
building blocks.

-J.

--- In CAS-Group@yahoogroups.com, "Telmo Menezes" wrote:
>
> Could a computer scientists examine these 68 molecules and try to
> extract from their interactions the "juice" of complexity?
>
> Cheers,
> Telmo Menezes.
>
> ----------
>
> Do 68 Molecules Hold the Key to Understanding Disease?
>
> "These 68 building blocks provide the structural basis for the
> molecular choreography that constitutes the entire life of a cell,"
> said Marth. "And two of the four cellular components are produced by
> these molecular building blocks in processes that cannot be encoded by
> the genes. These cellular components – the glycans and lipids – may
> now hold the keys to uncovering the origins of many grievous diseases
> that continue to evade understanding."
>
> From:
> http://ucsdnews.ucsd.edu/newsrel/health/09-0868Molecules.asp
>

Could a computer scientists examine these 68 molecules and try to
extract from their interactions the "juice" of complexity?

Cheers,
Telmo Menezes.

----------

Do 68 Molecules Hold the Key to Understanding Disease?

"These 68 building blocks provide the structural basis for the
molecular choreography that constitutes the entire life of a cell,"
said Marth. "And two of the four cellular components are produced by
these molecular building blocks in processes that cannot be encoded by
the genes. These cellular components – the glycans and lipids – may
now hold the keys to uncovering the origins of many grievous diseases
that continue to evade understanding."

From:
http://ucsdnews.ucsd.edu/newsrel/health/09-0868Molecules.asp

I have created two new blogs:

4 Lines of Code
http://4loc.wordpress.com/

CAS-Group Blog
http://blog.cas-group.net/

The first is a personal blog about software development
and Ruby on Rails - what I am doing as my day job.

The second is a blog about complex adaptive systems,
just like this group. There is also a new wiki at
http://www.cas-group.net/wiki/

Although finally some interesting discussions have
take place, I plan to abandon the Yahoo group in the
long term, and this is the first step to do it.
I will post mainly in the blogs and less in this
group, let me now if you want to be become a
contributor or co-author.

-J.

Jochen Fromm wrote:
> Buzzword of the day: "Creative Destruction", meaning
> one thing is replaced by another from within. Sounds
> a bit like cancer..
>

The late military strategist John Boyd wrote a white paper titled "Creation
and Destruction" as a process of analysis (taking things apart) and
re-synthesis (putting things together in new ways) as a necessary part of
adapting to an uncertain and changing world.

<http://www.chetrichards.com/modern_business_strategy/boyd/destruction/destructi\
on_and_creation.htm>

Chuck Spinney, one of Boyd's proteges, later put together a set of
interpretive pages.

http://www.chetrichards.com/modern_business_strategy/spinney/ev_epis/evolutionar\
y_1.htm

Chet Richards, another Boyd colleague/protege hosts the website
(http://www.chetrichards.com/c2w/) that the two papers above are on. He
wrote a recent book on application of Boyd's concepts titled "Certain to
Win". Frans Osinga, in "Science, Strategy and War" goes more deeply into the
numerous and various sources Boyd used (There's also a bibliography linked
to the first paper above). Osinga's book is best available directly from the
publisher. It's an interesting read.
(http://routledgehistory.com/books/Science-Strategy-and-War-isbn9780415459525)

One might think of the concept as being more like creative scavenging then
like cancer. Or as dismantling existing structures that are losing
effectiveness to recycle the resources in an adaptive manner.

Of course, a lot of what's written is going to simply reflect off the
surface veneer and miss the depth's.

...Keith

--

-----------------------------------------------------
    Keith Eric Grant
    Freelance Physicist, Writer, & Massage Instructor
    keg@...

    Ramblemuse Associates
    Pleasanton, CA 94566

    http://www.ramblemuse.com/
    http://www.ramblemuse.com/newdesign/resume.html
    http://www.linkedin.com/in/ramblemuse
-----------------------------------------------------

Which of the following social networks
will become a Facebook for scientists?

Nature Network
http://network.nature.com/

SciLink
http://www.scilink.com/

ResearchGATE
https://www.researchgate.net/

SciBog
http://scientists.scibog.com/

Buzzword of the day: "Creative Destruction", meaning
one thing is replaced by another from within. Sounds
a bit like cancer..

Definitions for "creative Destruction":
"a process which revolutionizes the structure of a
system from within, incessantly destroying the old
one, while incessantly creating a new one"
http://www.investopedia.com/terms/c/creativedestruction.asp

"a process of industrial transformation that
accompanies radical innovation"
http://www.telecom-marketing.com/index.php?q=node/7

Richard Feynman on "Social Sciences"
http://www.youtube.com/watch?v=_EZcpTTjjXY

Feynman on the "Inconceivable complexity of nature"
http://www.youtube.com/watch?v=AU8PId_6xec

I am not sure, actually it is quite simple to
write a community site such as Yahoo Groups. Using
modern scripting languages (Ruby or Python), together
with a suitable framework (Ruby on Rails or Django),
you can do this in a few days or weeks. I have something
as Yahoo groups itself in mind, only for complex systems
and with more social network functions (for example
friendships to see what you friends are doing, and
of course tagging and rating functionality, etc.).

-J.

>
> Jochen, I think this is a good idea and I think I'd like to
> participate, but I'll admit I don't know exactly what you are
> intending and how it differs from this forum :-)
>

A major evolutionary transition is for Kevin Padian
for example the transition from fins to legs. This is
not a major evolutionary transition (a 'landmark transition')
in the sense of Eörs Szathmáry and John Maynard Smith, see
http://www.nature.com/nature/focus/maynardsmith/pdf/1995.pdf
It is just a major evolutionary apdative change, yet
nevertheless interesting:

"We have the data that substantiate the major changes
in evolutionâ€"from the origin of jaws and bones
to the emergence of vertebrates on land, from the
radiation of dinosaurs to the origin of birds and
flight, from the assembly of the myriad features of
jaws, ears, brains, and teeth in ancient mammalian
relatives to the radiation of placentals and marsupials
over the past 70 million years."

Trickle-down evolution: an approach to getting major
evolutionary adaptive changes into textbooks and curricula
Padian Integr. Comp. Biol. 48 (2008) 175-188
http://radar.oreilly.com/trickle-down%20evolution-4.pdf

-J.

Regarding invented/artificial problems vs "natural" ones...

I think I do understand what Jochen means when he says P vs NP is
invented whereas "intelligence" is a true mystery.  But I also agree
with Telmo in questioning this distinction.  Ultimately, when we are
analyzing any sort of system, we do so using imperfect, artificial
models of the system which we hope capture the essence of what's
important.

In the case of intelligence, I do think it's questionable (as I've
stated in a previous reply) that we have even an adequate model of
what it is; perhaps our model of intelligence is so far from
corresponding to anything in the underlying system(s) in questions
that we should back off and start with a different sort of model.

In the case of P v NP, there is the compounded problem that the
underlying system that it models is a model that we invented (i.e. the
system of computable functions).  And we would be right to question
how well that underlying model/system corresponds to things that we
believe exist in the universe.

Just to illustrate this last and subtle point, a very smart friend of
mine recently point out that in the actual universe there are no known
examples of a perfect circle.  Yet we take it for granted that perfect
circles exist and that reasoning we do with the model of perfect
circles then carries over to objects we find in the real world.

Sorry for the late reply here.

Regarding the threads on human intelligence we humans often don't have
a clue as to what really goes on in our minds.  We tend to think that
most of what happens during our waking hours is some form of
conscious, analytical reasoning, when in fact mostly it's pattern
recognition, and subconscious spreading activation.  The small
minority of activity that could be considered symbolic reasoning
(either linguistic, spatial or otherwise) is far from sophisticated
and often quite faulty (c.f. vast literature on cognitive biases).
Compounding matters further, we seem much better at post hoc
rationalization than we are at open-ended and open-minded rational
analysis, which leads to a false sense that we are very good at
reasoning.

None of this is intended to indict humans as a whole or anyone in
particular, just that if we want to understand better why animals
can't "think" and what it will take to create AI that's got the
"juice", we need to understand -- and be realistic about -- what we
are comparing animals and putative AI to.

How does the brain work in five words or less
http://www.comedycentral.com/videos/index.jhtml?videoId=81914