Agar, Abduction

[Nick Thompson]

Michael,

I think I, too, am a fan of abduction, even though I am not so sure I know what it is.  To me it means the use of metaphors to explain.  A great many years ago, when I was still in the monkey business, I was able to demonstrate that the "social structure" of a monkey "group" was the same, whether one convened it as a whole or only as a series of n(n-1)/2 pairs of monkeys, suggesting that a monkey social group is an aggregate property of the behavior of its pairs.  It was a startling observation, one I did not expect and one I did not altogether trust.  What it suggested is that a group of monkeys, maintained in individual cages, and paired for observation, and who never had physical contact with monkeys outside of those meetings, was a good metaphor (model) for the group operating as a group in the ordinary sense.  

This is an example of a very low level abduction.  Natural selection theory ... the idea that what happens in a breeders barnyard or stable etc. can be taken as a model for what happens in nature ... is an example of a very high level abduction.   Evolution ... the idea that the change in species through time is akin to the ramification of a trees branches at it grows upward to the light .... is another.  Good metaphors stimulate thought and experiment, but a metaphor maker has a deep responsibility to stipulate which parts of his metaphor are facetious ... designed for fun and cognitive promotion, not part of what Mary Brenda Hesse calls "the positive heuristic of the metaphor".  Famous authors of widely read books often get away with ignoring that responsibility, viz, Richard Dawkins and his Selfish Gene.  

So.  Are we talking about the same thing when we talk about abduction?  As a man with a stiff hip, abduction is a concept I can use some help with.  

Nick

----- Original Message -----
From: Michael Agar
To: nickthompson at earthlink.net
Cc: friam at redfish.com
Sent: 8/14/2006 1:00:53 PM
Subject: Popper misuse


Hi Nick. I'm actually an abduction fan myself.


I shouldn't have taken Popper's name in vain, since all I really meant was falsification. Validity comes out of the research that precedes a model, the model explores and clarifies a core argument of that research, then in science the argument is put to the test in any number of imaginative ways where procedures are explicit and capable of falsifying or at least complicating the argument. Not everyone's cup of code by a long shot, but one I find useful. I don't think very many people think this way either (:


Mike






On Aug 14, 2006, at 10:36 AM, Nicholas Thompson wrote:


Mike A. writes:


How do we make clear the core of a problem through constructing an
illustration of our own beliefs and assumptions


and say that's exactly what both great science and great art do.
Science then has the obligation to challenge it against new instances
of the problem in the classic Popperian way.


One trouble with Popper is, of course, that people just dont think that
way.  We engage in induction no matter how illogical it may be.  Somebody I
knew had a small animal skin.... ferret or something ... nailed to a board
at one end.  When you petted it, it arched its back, so to speak.  Should
we conclude that that is why cats arch their backs when you pet them????
Probably not.  


The other trouble with Popper is, as David Stove pointed out, that EVERY
DEDUCTIVE INFERENCE REQUIRES INDUCTION TO GET IT INTO THE REALM OF
PRACTICAL EXPERIMENT.  So, for instance, as we are busily nailing our live
cat to a board to test our deductive inference, we must assume that all our
operations have the same effects in the live cat and in the ferret skin
case, and this assumption is an INDUCTIVE STEP subject to all of Popper's
doubts about the possibility of induction.  


I think Stove concluded that we just had to suck it up and go back to
making rules for inductive inference, dubious as the whole enterprise is.  


So then the question would be, under what conditions do we accept that when
the simple agents that we send forth to do battle in our models product the
same collective behavior as the apparently real agents we see around us,
that the real agents actually behave by the same underlying rules as the
our created ones?  


Stove wrote a subsequent book on induction, but I havent read it.  Has
anybody???


[Original Message]
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Date: 8/14/2006 12:00:21 PM
Subject: Friam Digest, Vol 38, Issue 29


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Today's Topics:


   1. the odd question (Phil Henshaw) (Nicholas Thompson)
   2. The art of agent-based modeling (Jochen Fromm)
   3. Re: The art of agent-based modeling (Marcus G. Daniels)
   4. Re: The art of agent-based modeling (Jochen Fromm)
   5. Re: The art of agent-based modeling (mgd at santafe.edu)
   6. Re: The art of agent-based modeling (Michael Agar)




----------------------------------------------------------------------


Message: 1
Date: Sun, 13 Aug 2006 23:40:29 -0400
From: "Nicholas Thompson" <[hidden email]>
Subject: [FRIAM] the odd question (Phil Henshaw)
To: "Friam" <Friam at redfish.com>
Message-ID: <380-22006811434029151 at earthlink.net>
Content-Type: text/plain; charset="us-ascii"


Phil,


I hate it when one of my topics gets dropped, and therefore feel guilty
for being one of the DROPPERS, here.


Sometimes the discussions get so far reaching  and technical that I am
forced to "pass over them in silence" as Wittegenstein said.  


the only piece of your message that I have anything  nearly competent to
say about is  your  ....




"when modern science took an interest in complex systems it, concentrated
on theory rather than on carefully documenting the physical phenomenon."


I wonder if this isnt a common occcurence in science.  Think of
Evolutionary Biology   Darwinism has a much stronger hand on its theories
than it does on the things those theories explain.  Think for a moment
about  our realtive grasp on "natural selection" and "adaptation".  Natural
selection is supposed to the be "cause" of adaptation, yet we seem to
understand the cause much better than we understand the effect.   Ask an
evolutionary biologist to define adaptation: 90 percent will use the word
natural selection in their definitions, because they dont have clue what
they mean by adaptation.  


Thus, it doesnt surprise me that wise and sophisticated people can talk
about the theory of complexity without having a clue what they mean by it.


I got a group of people to gether at Clark a few years back to start a
research project on emergence in human social groups.  We were NEVER able
to come up with a phenomenon that everybody agreed was an instance of
emergence.    
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Message: 2
Date: Mon, 14 Aug 2006 14:14:55 +0200
From: "Jochen Fromm" <[hidden email]>
Subject: [FRIAM] The art of agent-based modeling
To: "'The Friday Morning Applied Complexity Coffee Group'"
<friam at redfish.com>
Message-ID: <000701c6bf9b$41129210$976fa8c0 at Toshiba>
Content-Type: text/plain; charset="US-ASCII"




One question I meet again and again if I try to
make meaningful agent-based simulations is:
- How do we simulate the core of a problem
  without merely constructing an illustration
  of our own beliefs and assumptions ?
In other words: How detailed should an agent-based
simulation be ? If the goal is "to capture the principal
laws behind the exciting variety of new phenomena that become
apparent when the many units of a complex system interact", as
Tamas Vicsek says in http://angel.eltehu/~vicsek/images/complex.pdf
then how do we design models that are complex enough but not too
complex ?


-If the simulation is too simple and matches your
 own theoretical ideas, then no matter how good these
 ideas are it is always easy to criticize that the
 simulation is either not realistic enough or only
 constructed to illustrate your own ideas and assumptions.
-If the simulation is too complex and matches
 official experimental data, everything takes a
 lot amount of time (creation, setup and execution of
 the experiment and finally the cumbersome analysis
 of the complex outcomes), and it becomes increasingly
 difficult to identify the principal laws, because it is
 easy to get lost in the data or bogged down in details


The "art of agent-based modeling" looks really like an art
to me, something only mastered by a few scientists (for instance
Axelrod). Grimm et al. propose 'pattern-oriented modeling',
Macy and Willer say "Keep it simple" and "Test validity".
What do you think is the best solution for this problem ?


Macy and Willer
"From Factors to Actors: Computational Sociology and Agent-Based Modeling"


http://www.casos.cs.cmu.edu/education/phd/classpapers/Macy_Factors_2001.pdf


Grimm et al.
"Pattern-oriented modeling of agent-based complex systems"
Science Vol. 310. no. 5750 (2005) 987-991
http://www.ufz.de/index.php?de=4976


-J.


-----Original Message-----
From: Michael Agar
Sent: Saturday, August 12, 2006 5:05 PM
To: The Friday Morning Applied Complexity Coffee Group
Subject: [FRIAM] complexity and society


[...] If you are considering a model, I like Axelrod's way of thinking  
about them. He sees them as "thought experiment labs" for a  
conclusion based on social research. So first of all the social  
research has to be solid to really do it properly. More often than  
not it isn't.


The lab let's you test arguments of the form, if people do things in  
particular ways properties will emerge at the level of society. By  
"test" I mean it lets you see if the conclusion can be "generated,"  
to use Epstein and Axtell's concept, in just the way your social  
research suggests that it can. It's a way of making the argument that  
underlies the conclusion explicit so it can be better evaluated, and  
it allows for exploration of the space of results that the same  
argument produces and alternative spaces given control parameter  
changes. It's a test of plausibility and an exercise in clarity,  
nothing more, nothing less. [...]








------------------------------


Message: 3
Date: Mon, 14 Aug 2006 08:07:05 -0600
From: "Marcus G. Daniels" <[hidden email]>
Subject: Re: [FRIAM] The art of agent-based modeling
To: The Friday Morning Applied Complexity Coffee Group
<friam at redfish.com>
Message-ID: <44E08389.5070109 at santafe.edu>
Content-Type: text/plain; charset=ISO-8859-1; format=flowed


Jochen,
-If the simulation is too complex and matches
 official experimental data, everything takes a
 lot amount of time (creation, setup and execution of
 the experiment and finally the cumbersome analysis
 of the complex outcomes), and it becomes increasingly
 difficult to identify the principal laws, because it is
 easy to get lost in the data or bogged down in details


This may be a false choice.   In the case of having some data of
moderate resolution, there's no point in making a hugely elaborate model
and simulation, because you'll never be able to validate beyond your
data anyway.   And if you don't validate, although the modeling still
may be useful as an thought experiment, it isn't science.  You have to
be able to say something that can be shown to be wrong.   If you do aim
to learn things about the world and then predict them it's not desirable
to have giant black box with lots of moving parts.   It's better, if at
all possible, to have a simple story and make the simulation nothing
more than apparatus to help extend the data so that the dynamics can be
studied by theoreticians.


Another mode of use for ABMs is to lower expectations of theoretical
traction and opportunistically look for ways a model makes useful
predictions and then modify the model in that direction over time.  
This is a risky and expensive craft, but one that might have high enough
payoffs to consider (e.g. national security).


It depends on the data and what is of interest.   If the data tells you
about a number of rare events, and it is these events is what you really
care about, then it may make sense to loosely model everyday behaviors
and focus on model microstructure that can create the rare events you
care about.


Finally, sometimes microstructure is known with clearly defined degrees
of freedom, and the dynamics are of interest.  Consider modeling a
factory where different assembly regimes are to be evaluated..  There's
no need to validate here because the whole exercise is to answer
what-ifs about realizable specific systems.


Marcus










------------------------------


Message: 4
Date: Mon, 14 Aug 2006 17:04:40 +0200
From: "Jochen Fromm" <[hidden email]>
Subject: Re: [FRIAM] The art of agent-based modeling
To: "'The Friday Morning Applied Complexity Coffee Group'"
<friam at redfish.com>
Message-ID: <000801c6bfb2$f76ede80$976fa8c0 at Toshiba>
Content-Type: text/plain; charset="US-ASCII"




Of course it is the essence of science to verify hypotheses
by experiments. Yet sometimes we have neither suitable
experimental data nor a solid theory, for example
in the case of very large agent-based systems (for instance
for the self-organization and self-management of large
internet applications on planetary scale, or the modeling
of historical processes with millions of actors). It is
hardly possible to examine these systems without simplified
models, and in this case the questions I mentioned seem to
be justified.


In traditional "factor-based" or "equation-based modeling"
we use differential equations and everything is based
on a soild theory: mathematics. This traditional modeling
has a century-long history and we know the suitable parameters,
equations and models. Agent-based modeling has a short history,
we don't know exactly the suitable parameters, agents and models,
and worst of all it is not based on a solid theoretical
theorem-lemma-proof science or calculus like mathematics.


What is missing is a solid science of ABM or a new science of
complexity - something in the direction of Wolfram's NKS idea
(exploring computational universes in a systematic way). Just
as formal, symmetrical and regular systems can be described by
mathematics and 'equation-based modeling', complex systems can
in principle be described by a 'NKS' and agent-based modeling
- which seems to be more an art than a science.


-J.








------------------------------


Message: 5
Date: Mon, 14 Aug 2006 09:50:59 -0600
From: [hidden email]
Subject: Re: [FRIAM] The art of agent-based modeling
To: The Friday Morning Applied Complexity Coffee Group
<friam at redfish.com>
Message-ID: <1155570659.44e09be35f52a at webmail.santafe.edu>
Content-Type: text/plain; charset=ISO-8859-1


Quoting Jochen Fromm <fromm at vs.uni-kassel.de>:


Just as formal, symmetrical and regular systems can be described by
mathematics and 'equation-based modeling', complex systems can
in principle be described by a 'NKS' and agent-based modeling
- which seems to be more an art than a science.


In context, I think is a verification issue.  


ABMs are useful for poking around a complicated system to see what
matters and
what doesn't by using a familiar and direct way of describing things, and
to
leave the abstractions for later.  ABMs complement traditional techniques
of
analysis by extending data.


The imperative programming languages that are typically used to make the
simulations are prone to a variety of programming mistakes but the
continue to
be used because 1) they are common and 2) they provide an easy way to
think
about side effects (e.g. modifications to a landscape).


Equation-based modelling is more like functional programming, e.g.
programming
languages like Haskell that are side-effect free.   I see ABMs moving to
these
kinds of programming languages so that components of a simulation can be
shown
to be correct, and preferably by automated means.  


As a practical matter, I think it isn't a big deal.  Unit testing during
development by experienced programmers/modelers does a good job of
shaking out
bugs.


Marcus






------------------------------


Message: 6
Date: Mon, 14 Aug 2006 09:58:05 -0600
From: Michael Agar <[hidden email]>
Subject: Re: [FRIAM] The art of agent-based modeling
To: The Friday Morning Applied Complexity Coffee Group
<friam at redfish.com>
Message-ID: <18528635-4305-4EB8-8D01-CA9C885E3826 at anth.umd.edu>
Content-Type: text/plain; charset=US-ASCII; delsp=yes; format=flowed




On Aug 14, 2006, at 6:14 AM, Jochen Fromm wrote:






- How do we simulate the core of a problem
  without merely constructing an illustration
  of our own beliefs and assumptions ?




I'd change this to


How do we make clear the core of a problem through constructing an  
illustration of our own beliefs and assumptions


and say that's exactly what both great science and great art do.  
Science then has the obligation to challenge it against new instances  
of the problem in the classic Popperian way.


Mike






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[Michael Agar]

Nick--Here's a blurb on abduction, part of a lecture I did earlier  
this year on how to tell if something is a "real" ethnography. The  
full pack of lies available on request. The delete key might be a  
better choice.

Mike



Peirce?s abductive logic formalizes this critical part of any  
ethnographic trajectory. Let me borrow from an unpublished paper by  
Michael Hoffman, an artificial intelligence researcher at Bielefeld  
University. Here, in Peirce?s own words, as quoted by Hoffman, is  
abductive logic:

The surprising fact, F, is observed
If H were true, F would be a matter of course
Hence, there is reason to suspect that H is true



The ?surprising fact F? echoes what I call ?rich points.? Rich points  
are the raw material of ethnographic research. They run the gamut  
from incomprehensible surprise to departure from expectations to  
glitches in an aggregate data set. As Peirce would have advocated,  
the purpose of ethnography is to go forth into the world, find and  
experience rich points, and then take them seriously as a signal of a  
difference between what you know and what you need to learn to  
understand and explain what just happened. People are said to be  
creatures of habit and seekers of certainty. Abduction turns them  
into the opposite.

How do we make sense of all these big and little ?F?s?? We don?t just  
box them in with old concepts in the style of inductive logic.  
Instead, we imagine ?H?s? that might explain them. We imagine. The  
surprise F, the rich point, calls on us to create, to think, to make  
up an antecedent H that does indeed imply the consequent. Where did  
that F come from? Well, what if? H?  Rather than reaching into the  
box and pulling out a concept ready at hand, we make up some new ones.

Any trajectory in the ethnographic space will run on the fuel of  
abduction. You?ll read or see how surprises came up, how they were  
taken seriously, and how they were explained using concepts not  
anticipated when the story started.

We need to reign in our enthusiasm a bit. Peirce wants some  
plausibility. Stephen King just wrote a new thriller where, the  
review said, a pulse transmitted through cell phones turns users who  
happen to be calling at the time into monsters. The plot appeals to  
me, but the likelihood that the story will turn into an actual news  
item is pretty slim. It?s probably an entertaining read, but a  
plausible scenario?

Peirce also wants us to follow up the abductive epiphany with some  
tedious work. And the tedious work looks a lot like old-fashioned  
science. We need to systematically collect, compare and contrast, try  
to prove the new H ? P link wrong, all that systematic drudgery,  
whether we?re in the lab or in the field. It reminds me of one of my  
favorite Einstein quotes, that he never made a significant scientific  
discovery using rational analytic thought. But he did a lot of work  
after the discovery to test it out. And it reminds me of Edison?s  
famous quote, since I mentioned his museum a while back--Genius is 1%  
inspiration and 99% perspiration. And it reminds me of why I like the  
first days of ethnographic work the best, because they are the most  
creative part where the learning curve accelerates exponentially.

Hoffman also emphasizes that the range of imagination in play is  
bounded by history. We can only stretch so far is the sad moral of  
the story. Vygotsky?s ?zone of proximal development,? about which I  
learned much from education colleagues during my visit, is a case in  
point. But still, some stretching is better than no stretching at  
all. That?s the message that abduction conveys.



On Aug 14, 2006, at 2:37 PM, Nicholas Thompson wrote:

> Michael,
>
> I think I, too, am a fan of abduction, even though I am not so sure  
> I know what it is.  To me it means the use of metaphors to  
> explain.  A great many years ago, when I was still in the monkey  
> business, I was able to demonstrate that the "social structure" of  
> a monkey "group" was the same, whether one convened it as a whole  
> or only as a series of n(n-1)/2 pairs of monkeys, suggesting that a  
> monkey social group is an aggregate property of the behavior of its  
> pairs.  It was a startling observation, one I did not expect and  
> one I did not altogether trust.  What it suggested is that a group  
> of monkeys, maintained in individual cages, and paired for  
> observation, and who never had physical contact with monkeys  
> outside of those meetings, was a good metaphor (model) for the  
> group operating as a group in the ordinary sense.
>
> This is an example of a very low level abduction.  Natural  
> selection theory ... the idea that what happens in a breeders  
> barnyard or stable etc. can be taken as a model for what happens in  
> nature ... is an example of a very high level abduction.    
> Evolution ... the idea that the change in species through time is  
> akin to the ramification of a trees branches at it grows upward to  
> the light .... is another.  Good metaphors stimulate thought and  
> experiment, but a metaphor maker has a deep responsibility to  
> stipulate which parts of his metaphor are facetious ... designed  
> for fun and cognitive promotion, not part of what Mary Brenda Hesse  
> calls "the positive heuristic of the metaphor".  Famous authors of  
> widely read books often get away with ignoring that responsibility,  
> viz, Richard Dawkins and his Selfish Gene.
>
> So.  Are we talking about the same thing when we talk about  
> abduction?  As a man with a stiff hip, abduction is a concept I can  
> use some help with.
>
> Nick
>  age]

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[Phil Henshaw-2]

I puzzled about why someone would muck up a perfectly good English word
with an entirely foreign meaning, but the philosophical use is well
described on http://www.philosophypages.com/dy/a.htm#abd , something
about using heuristics http://www.philosophypages.com/dy/h2.htm#heur
whatever they are.    It almost seems to describe the form of reasoning
that careful human thinkers probably used for most of the past million
years or so, and would probably still have a use for confusing problems.
 
 

Phil Henshaw                       ????.?? ? `?.????
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
680 Ft. Washington Ave
NY NY 10040                      
tel: [phone]                
e-mail: pfh at synapse9.com          
explorations: www.synapse9.com <http://www.synapse9.com/>    

-----Original Message-----
From: [hidden email] [mailto:[hidden email]] On
Behalf Of Nicholas Thompson
Sent: Monday, August 14, 2006 4:37 PM
To: Michael Agar
Cc: Friam
Subject: Re: [FRIAM] Agar, Abduction


Michael,
 
I think I, too, am a fan of abduction, even though I am not so sure I
know what it is.  To me it means the use of metaphors to explain.  A
great many years ago, when I was still in the monkey business, I was
able to demonstrate that the "social structure" of a monkey "group" was
the same, whether one convened it as a whole or only as a series of
n(n-1)/2 pairs of monkeys, suggesting that a monkey social group is an
aggregate property of the behavior of its pairs.  It was a startling
observation, one I did not expect and one I did not altogether trust.
What it suggested is that a group of monkeys, maintained in individual
cages, and paired for observation, and who never had physical contact
with monkeys outside of those meetings, was a good metaphor (model) for
the group operating as a group in the ordinary sense.  
 
This is an example of a very low level abduction.  Natural selection
theory ... the idea that what happens in a breeders barnyard or stable
etc. can be taken as a model for what happens in nature ... is an
example of a very high level abduction.   Evolution ... the idea that
the change in species through time is akin to the ramification of a
trees branches at it grows upward to the light .... is another.  Good
metaphors stimulate thought and experiment, but a metaphor maker has a
deep responsibility to stipulate which parts of his metaphor are
facetious ... designed for fun and cognitive promotion, not part of what
Mary Brenda Hesse calls "the positive heuristic of the metaphor".
Famous authors of widely read books often get away with ignoring that
responsibility, viz, Richard Dawkins and his Selfish Gene.  

So.  Are we talking about the same thing when we talk about abduction?
As a man with a stiff hip, abduction is a concept I can use some help
with.  
 
Nick
 

----- Original Message -----
From: Michael  <mailto:[hidden email]> Agar
To: nickthompson at earthlink.net
Cc: friam at redfish.com
Sent: 8/14/2006 1:00:53 PM
Subject: Popper misuse

Hi Nick. I'm actually an abduction fan myself.

I shouldn't have taken Popper's name in vain, since all I really meant
was falsification. Validity comes out of the research that precedes a
model, the model explores and clarifies a core argument of that
research, then in science the argument is put to the test in any number
of imaginative ways where procedures are explicit and capable of
falsifying or at least complicating the argument. Not everyone's cup of
code by a long shot, but one I find useful. I don't think very many
people think this way either (:

Mike



On Aug 14, 2006, at 10:36 AM, Nicholas Thompson wrote:


Mike A. writes:

How do we make clear the core of a problem through constructing an
illustration of our own beliefs and assumptions

and say that's exactly what both great science and great art do.
Science then has the obligation to challenge it against new instances
of the problem in the classic Popperian way.

One trouble with Popper is, of course, that people just dont think that
way.  We engage in induction no matter how illogical it may be.
Somebody I
knew had a small animal skin.... ferret or something ... nailed to a
board
at one end.  When you petted it, it arched its back, so to speak.
Should
we conclude that that is why cats arch their backs when you pet them????

Probably not.  

The other trouble with Popper is, as David Stove pointed out, that EVERY
DEDUCTIVE INFERENCE REQUIRES INDUCTION TO GET IT INTO THE REALM OF
PRACTICAL EXPERIMENT.  So, for instance, as we are busily nailing our
live
cat to a board to test our deductive inference, we must assume that all
our
operations have the same effects in the live cat and in the ferret skin
case, and this assumption is an INDUCTIVE STEP subject to all of
Popper's
doubts about the possibility of induction.  

I think Stove concluded that we just had to suck it up and go back to
making rules for inductive inference, dubious as the whole enterprise
is.  

So then the question would be, under what conditions do we accept that
when
the simple agents that we send forth to do battle in our models product
the
same collective behavior as the apparently real agents we see around us,
that the real agents actually behave by the same underlying rules as the
our created ones?  

Stove wrote a subsequent book on induction, but I havent read it.  Has
anybody???


[Original Message]
From: <[hidden email]>
To: <friam at redfish.com>
Date: 8/14/2006 12:00:21 PM
Subject: Friam Digest, Vol 38, Issue 29

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Today's Topics:

   1. the odd question (Phil Henshaw) (Nicholas Thompson)
   2. The art of agent-based modeling (Jochen Fromm)
   3. Re: The art of agent-based modeling (Marcus G. Daniels)
   4. Re: The art of agent-based modeling (Jochen Fromm)
   5. Re: The art of agent-based modeling (mgd at santafe.edu)
   6. Re: The art of agent-based modeling (Michael Agar)


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Message: 1
Date: Sun, 13 Aug 2006 23:40:29 -0400
From: "Nicholas Thompson" <[hidden email]>
Subject: [FRIAM] the odd question (Phil Henshaw)
To: "Friam" <Friam at redfish.com>
Message-ID: <380-22006811434029151 at earthlink.net>
Content-Type: text/plain; charset="us-ascii"

Phil,

I hate it when one of my topics gets dropped, and therefore feel guilty

for being one of the DROPPERS, here.


Sometimes the discussions get so far reaching  and technical that I am

forced to "pass over them in silence" as Wittegenstein said.  


the only piece of your message that I have anything  nearly competent to

say about is  your  ....



"when modern science took an interest in complex systems it,
concentrated

on theory rather than on carefully documenting the physical phenomenon."


I wonder if this isnt a common occcurence in science.  Think of

Evolutionary Biology   Darwinism has a much stronger hand on its
theories
than it does on the things those theories explain.  Think for a moment
about  our realtive grasp on "natural selection" and "adaptation".
Natural
selection is supposed to the be "cause" of adaptation, yet we seem to
understand the cause much better than we understand the effect.   Ask an
evolutionary biologist to define adaptation: 90 percent will use the
word
natural selection in their definitions, because they dont have clue what
they mean by adaptation.  


Thus, it doesnt surprise me that wise and sophisticated people can talk

about the theory of complexity without having a clue what they mean by
it.


I got a group of people to gether at Clark a few years back to start a

research project on emergence in human social groups.  We were NEVER
able
to come up with a phenomenon that everybody agreed was an instance of
emergence.    

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Message: 2
Date: Mon, 14 Aug 2006 14:14:55 +0200
From: "Jochen Fromm" <[hidden email]>
Subject: [FRIAM] The art of agent-based modeling
To: "'The Friday Morning Applied Complexity Coffee Group'"
<friam at redfish.com>
Message-ID: <000701c6bf9b$41129210$976fa8c0 at Toshiba>
Content-Type: text/plain; charset="US-ASCII"


One question I meet again and again if I try to
make meaningful agent-based simulations is:
- How do we simulate the core of a problem
  without merely constructing an illustration
  of our own beliefs and assumptions ?
In other words: How detailed should an agent-based
simulation be ? If the goal is "to capture the principal
laws behind the exciting variety of new phenomena that become
apparent when the many units of a complex system interact", as
Tamas Vicsek says in http://angel.eltehu/~vicsek/images/complex.pdf
<http://angel.elte.hu/~vicsek/images/complex.pdf>
then how do we design models that are complex enough but not too
complex ?

-If the simulation is too simple and matches your
 own theoretical ideas, then no matter how good these
 ideas are it is always easy to criticize that the
 simulation is either not realistic enough or only
 constructed to illustrate your own ideas and assumptions.
-If the simulation is too complex and matches
 official experimental data, everything takes a
 lot amount of time (creation, setup and execution of
 the experiment and finally the cumbersome analysis
 of the complex outcomes), and it becomes increasingly
 difficult to identify the principal laws, because it is
 easy to get lost in the data or bogged down in details

The "art of agent-based modeling" looks really like an art
to me, something only mastered by a few scientists (for instance
Axelrod). Grimm et al. propose 'pattern-oriented modeling',
Macy and Willer say "Keep it simple" and "Test validity".
What do you think is the best solution for this problem ?

Macy and Willer
"From Factors to Actors: Computational Sociology and Agent-Based
Modeling"


http://www.casos.cs.cmu.edu/education/phd/classpapers/Macy_Factors_2001.
pdf


Grimm et al.
"Pattern-oriented modeling of agent-based complex systems"
Science Vol. 310. no. 5750 (2005) 987-991
http://www.ufz.de/index.php?de=4976

-J.

-----Original Message-----
From: Michael Agar
Sent: Saturday, August 12, 2006 5:05 PM
To: The Friday Morning Applied Complexity Coffee Group
Subject: [FRIAM] complexity and society

[...] If you are considering a model, I like Axelrod's way of thinking  
about them. He sees them as "thought experiment labs" for a  
conclusion based on social research. So first of all the social  
research has to be solid to really do it properly. More often than  
not it isn't.

The lab let's you test arguments of the form, if people do things in  
particular ways properties will emerge at the level of society. By  
"test" I mean it lets you see if the conclusion can be "generated,"  
to use Epstein and Axtell's concept, in just the way your social  
research suggests that it can. It's a way of making the argument that  
underlies the conclusion explicit so it can be better evaluated, and  
it allows for exploration of the space of results that the same  
argument produces and alternative spaces given control parameter  
changes. It's a test of plausibility and an exercise in clarity,  
nothing more, nothing less. [...]




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Message: 3
Date: Mon, 14 Aug 2006 08:07:05 -0600
From: "Marcus G. Daniels" <[hidden email]>
Subject: Re: [FRIAM] The art of agent-based modeling
To: The Friday Morning Applied Complexity Coffee Group
<friam at redfish.com>
Message-ID: <44E08389.5070109 at santafe.edu>
Content-Type: text/plain; charset=ISO-8859-1; format=flowed

Jochen,

-If the simulation is too complex and matches
 official experimental data, everything takes a
 lot amount of time (creation, setup and execution of
 the experiment and finally the cumbersome analysis
 of the complex outcomes), and it becomes increasingly
 difficult to identify the principal laws, because it is
 easy to get lost in the data or bogged down in details


This may be a false choice.   In the case of having some data of
moderate resolution, there's no point in making a hugely elaborate model

and simulation, because you'll never be able to validate beyond your
data anyway.   And if you don't validate, although the modeling still
may be useful as an thought experiment, it isn't science.  You have to
be able to say something that can be shown to be wrong.   If you do aim
to learn things about the world and then predict them it's not desirable

to have giant black box with lots of moving parts.   It's better, if at
all possible, to have a simple story and make the simulation nothing
more than apparatus to help extend the data so that the dynamics can be
studied by theoreticians.

Another mode of use for ABMs is to lower expectations of theoretical
traction and opportunistically look for ways a model makes useful
predictions and then modify the model in that direction over time.  
This is a risky and expensive craft, but one that might have high enough

payoffs to consider (e.g. national security).

It depends on the data and what is of interest.   If the data tells you
about a number of rare events, and it is these events is what you really

care about, then it may make sense to loosely model everyday behaviors
and focus on model microstructure that can create the rare events you
care about.

Finally, sometimes microstructure is known with clearly defined degrees
of freedom, and the dynamics are of interest.  Consider modeling a
factory where different assembly regimes are to be evaluated..  There's
no need to validate here because the whole exercise is to answer
what-ifs about realizable specific systems.

Marcus





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Message: 4
Date: Mon, 14 Aug 2006 17:04:40 +0200
From: "Jochen Fromm" <[hidden email]>
Subject: Re: [FRIAM] The art of agent-based modeling
To: "'The Friday Morning Applied Complexity Coffee Group'"
<friam at redfish.com>
Message-ID: <000801c6bfb2$f76ede80$976fa8c0 at Toshiba>
Content-Type: text/plain; charset="US-ASCII"


Of course it is the essence of science to verify hypotheses
by experiments. Yet sometimes we have neither suitable
experimental data nor a solid theory, for example
in the case of very large agent-based systems (for instance
for the self-organization and self-management of large
internet applications on planetary scale, or the modeling
of historical processes with millions of actors). It is
hardly possible to examine these systems without simplified
models, and in this case the questions I mentioned seem to
be justified.

In traditional "factor-based" or "equation-based modeling"
we use differential equations and everything is based
on a soild theory: mathematics. This traditional modeling
has a century-long history and we know the suitable parameters,
equations and models. Agent-based modeling has a short history,
we don't know exactly the suitable parameters, agents and models,
and worst of all it is not based on a solid theoretical
theorem-lemma-proof science or calculus like mathematics.

What is missing is a solid science of ABM or a new science of
complexity - something in the direction of Wolfram's NKS idea
(exploring computational universes in a systematic way). Just
as formal, symmetrical and regular systems can be described by
mathematics and 'equation-based modeling', complex systems can
in principle be described by a 'NKS' and agent-based modeling
- which seems to be more an art than a science.

-J.




------------------------------

Message: 5
Date: Mon, 14 Aug 2006 09:50:59 -0600
From: [hidden email]
Subject: Re: [FRIAM] The art of agent-based modeling
To: The Friday Morning Applied Complexity Coffee Group
<friam at redfish.com>
Message-ID: <1155570659.44e09be35f52a at webmail.santafe.edu>
Content-Type: text/plain; charset=ISO-8859-1

Quoting Jochen Fromm <fromm at vs.uni-kassel.de>:


Just as formal, symmetrical and regular systems can be described by
mathematics and 'equation-based modeling', complex systems can
in principle be described by a 'NKS' and agent-based modeling
- which seems to be more an art than a science.


In context, I think is a verification issue.  

ABMs are useful for poking around a complicated system to see what

matters and

what doesn't by using a familiar and direct way of describing things,
and

to

leave the abstractions for later.  ABMs complement traditional
techniques

of

analysis by extending data.

The imperative programming languages that are typically used to make the

simulations are prone to a variety of programming mistakes but the

continue to

be used because 1) they are common and 2) they provide an easy way to

think

about side effects (e.g. modifications to a landscape).

Equation-based modelling is more like functional programming, e.g.

programming

languages like Haskell that are side-effect free.   I see ABMs moving to

these

kinds of programming languages so that components of a simulation can be

shown

to be correct, and preferably by automated means.  

As a practical matter, I think it isn't a big deal.  Unit testing during

development by experienced programmers/modelers does a good job of

shaking out

bugs.

Marcus



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Message: 6
Date: Mon, 14 Aug 2006 09:58:05 -0600
From: Michael Agar <[hidden email]>
Subject: Re: [FRIAM] The art of agent-based modeling
To: The Friday Morning Applied Complexity Coffee Group
<friam at redfish.com>
Message-ID: <18528635-4305-4EB8-8D01-CA9C885E3826 at anth.umd.edu>
Content-Type: text/plain; charset=US-ASCII; delsp=yes; format=flowed


On Aug 14, 2006, at 6:14 AM, Jochen Fromm wrote:




- How do we simulate the core of a problem
  without merely constructing an illustration
  of our own beliefs and assumptions ?



I'd change this to

How do we make clear the core of a problem through constructing an  
illustration of our own beliefs and assumptions

and say that's exactly what both great science and great art do.  
Science then has the obligation to challenge it against new instances  
of the problem in the classic Popperian way.

Mike



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End of Friam Digest, Vol 38, Issue 29
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