Agent Based Modeling's Role in Understanding Complexity

[Ann Racuya-Robbins-2]

I have just started reading Friam email and hope to participate in an
upcoming Barcamp. I want to understand better the promise of using ABM in
understanding complexity. I heard, I think it was Nicholas Thompson, say
that what he likes about ABM is that it allows him to see things he wouldn't
be able to see otherwise. (Forgive me Nick, if I got that wrong) This
potential is what interests me.but what "things" does it allow to be
seen.that is what I want to know-that is the conversation I would like to
engage.

Almost everything allows something else to be seen in a new light. I want to
know if ABM allows

-new understanding in the way say dreams and dreaming allows us to explore
and understand ourselves and each other in a way we wouldn't be able to do
otherwise

or

-does ABM allow us to see something more like the numerical result of p
divided out for 10 years. That is does ABM allow us to do things we don't
have the interest or patience to spend our time doing.

 

Both can be useful but what is it we are looking for? Can ABM bring us
closer together in understanding each other? How? In what way?

 

 

Best wishes,

Ann Racuya-Robbins

Founder and CEO

World Knowledge BankR

www.wkbank.com

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[glen ep ropella]

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admin at wkbank.com wrote:
> -new understanding in the way say dreams and dreaming allows us to explore
> and understand ourselves and each other in a way we wouldn't be able to do
> otherwise
>
> or
>
> -does ABM allow us to see something more like the numerical result of p
> divided out for 10 years. That is does ABM allow us to do things we don't
> have the interest or patience to spend our time doing.

Both, though the tendency would be toward the latter because that's
exactly what computers are good at, doing things we don't have the time
or interest to do.

Ultimately, however, you have to remember that the "M" in ABM directly
implies that an ABM (because it's a model) is MERELY a form of rhetoric,
more specifically, it's a form of deductive _logic_.  And in that sense,
all ABMs, as rhetoric, commit the fallacy of "begging the question"
(a.k.a. "assuming one's conclusions", petitio principii).  Nothing can
come out of it that you didn't already program into it.

So, in that sense, an ABM won't tell you _anything_ you didn't already
know in some form or another.

And that means that the only thing an ABM is good for is taking your
premises and munging them back and forth (using logic of some sort) to
spit them out at you in some other form.  (And, yes, this is true even
if there's outside input from, say, a human or some external stochastic
process, at various points in the execution.)

Now, many people use this deductive machinery to estimate or predict the
consequences of some initial conditions.  And where the internal logic
of the model is acyclic (no feedback), that's a valid use.  But, regular
old models (not ABM) are just as good or better at that sort of thing
than ABM.

ABM is particularly good at munging your premises and spitting them out
in different forms when the logic is (or can be) _cyclic_.  I.e. when
there are feedback loops or self-reference inside.  The canonical
example for such cyclic logic is in a system where the environment for
some "agents" is defined (collectively) by another set of "agents" and
vice versa.

And if seen in that light, regular old modeling (non ABM) _could_ help
find new understanding by exploring dreams (or any psychologically
relevant acyclic construct).  But, because dreams (and psychological
constructs in general) are social constructs developed in cycles with
many dreamers as well as a semantic grounding in a cyclically
constructed ecology, I'd say that of all the types of modeling one might
use, ABM will be much more helpful in exploring ourselves than other
forms of modeling.

> Both can be useful but what is it we are looking for? Can ABM bring us
> closer together in understanding each other? How? In what way?

Respectively:  Cycles, specifically co-evolution and stigmergy.  Yes.
By helping us to formulate, execute, and criticize those hypothetical
(generative and phenomenal) cycles.

Note, however, that the TENDENCY is to use ABM in the same old way
traditional modelers use computers, to do work we're too lazy or
ill-equipped to do, like counting to high numbers and such.  My guess is
that if you examine 1000 ABMs, you are likely to find only a handful
that explicitly study causal cycles.  In other words, most ABMs should
not be ABMs at all.  And in the handful of cases where cycles are
studied, they are probably studied from a reductionist point of view,
wherein herculean attempts are made to _isolate_ various pathways
(cyclic or not), which defeats the very purpose of an ABM.  After all,
it's the _braided_ or woven nature of causal networks (in contrast to
causal _chains_) that gave rise to ABM to begin with.

Anyway, the domain is ripe with abuse.  But don't let the poverty of
most ABMs convince you that ABM is vacuous as an approach/perspective.
You just have to view each individual ABM with a critical eye and think
to yourself, "What cyclic reasoning does this model reify?"

- --
glen e. p. ropella, 971-219-3846, http://tempusdictum.com
And therefore the victories won by a master of war gain him neither
reputation for wisdom nor merit for valour.   -- Sun Tzu, "The Art of War"

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[Phil Henshaw-2]

Well, there's a sort of a minority 'out crowd' kind of view, that what
ABM's let you see you wouldn't otherwise is how nature is behaving that
ABM's aught to emulate but can't for various general and specific
reasons.   The idea that playing with models suggests new ways to play
with models is still there in this minority view, it's just that the
purpose of that is quite different.
 
 

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/>    
-- "it's not finding what people say interesting, but finding what's
interesting in what they say" --

-----Original Message-----
From: [hidden email] [mailto:[hidden email]] On
Behalf Of admin at wkbank.com
Sent: Wednesday, March 12, 2008 4:44 PM
To: friam at redfish.com
Subject: [FRIAM] Agent Based Modeling's Role in Understanding Complexity



I have just started reading Friam email and hope to participate in an
upcoming Barcamp. I want to understand better the promise of using ABM
in understanding complexity. I heard, I think it was Nicholas Thompson,
say that what he likes about ABM is that it allows him to see things he
wouldn?t be able to see otherwise. (Forgive me Nick, if I got that
wrong) This potential is what interests me
but what ?things? does it
allow to be seen
that is what I want to know?that is the conversation I
would like to engage.

Almost everything allows something else to be seen in a new light. I
want to know if ABM allows

?new understanding in the way say dreams and dreaming allows us to
explore and understand ourselves and each other in a way we wouldn?t be
able to do otherwise

or

?does ABM allow us to see something more like the numerical result of p
divided out for 10 years. That is does ABM allow us to do things we
don?t have the interest or patience to spend our time doing.

 

Both can be useful but what is it we are looking for? Can ABM bring us
closer together in understanding each other? How? In what way?

 

 

Best wishes,

Ann Racuya-Robbins

Founder and CEO

World Knowledge Bank?

www.wkbank.com

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[Kenneth Lloyd]

To add to Phil remarks, there seems to be different views on what is an
'agent' in ABM (let's forget the 'what is a model stuff' for now).  I
distinguish between agents that are intrinsically part of a system, and that
exhibit intelligence - either natural human intelligence or weak artificial
intelligence.  They can evaluate knowledge sufficiency in contexts and can
probabilistically anticipate and adapt to perturbations. Compare this to how
others refer to agents - or what I call automata - as deterministic or
probabilistic, rules based entities that act within a system.  If these
agents or automata exist outside the system, providing data or modulation of
the system, these elements are referred to as actors.
 
Of course, I am one of 'those' model creating and simulating folks - so be
forewarned.
 
Ken


  _____  

From: [hidden email] [mailto:[hidden email]] On Behalf
Of Phil Henshaw
Sent: Wednesday, March 12, 2008 4:09 PM
To: 'The Friday Morning Applied Complexity Coffee Group'
Subject: Re: [FRIAM] Agent Based Modeling's Role in Understanding Complexity


Well, there's a sort of a minority 'out crowd' kind of view, that what ABM's
let you see you wouldn't otherwise is how nature is behaving that ABM's
aught to emulate but can't for various general and specific reasons.   The
idea that playing with models suggests new ways to play with models is still
there in this minority view, it's just that the purpose of that is quite
different.
 
 


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/>    
-- "it's not finding what people say interesting, but finding what's
interesting in what they say" --

-----Original Message-----
From: [hidden email] [mailto:[hidden email]] On Behalf
Of admin at wkbank.com
Sent: Wednesday, March 12, 2008 4:44 PM
To: friam at redfish.com
Subject: [FRIAM] Agent Based Modeling's Role in Understanding Complexity



I have just started reading Friam email and hope to participate in an
upcoming Barcamp. I want to understand better the promise of using ABM in
understanding complexity. I heard, I think it was Nicholas Thompson, say
that what he likes about ABM is that it allows him to see things he wouldn?t
be able to see otherwise. (Forgive me Nick, if I got that wrong) This
potential is what interests me
but what ?things? does it allow to be
seen
that is what I want to know?that is the conversation I would like to
engage.

Almost everything allows something else to be seen in a new light. I want to
know if ABM allows

?new understanding in the way say dreams and dreaming allows us to explore
and understand ourselves and each other in a way we wouldn?t be able to do
otherwise

or

?does ABM allow us to see something more like the numerical result of p
divided out for 10 years. That is does ABM allow us to do things we don?t
have the interest or patience to spend our time doing.

 

Both can be useful but what is it we are looking for? Can ABM bring us
closer together in understanding each other? How? In what way?

 

 

Best wishes,

Ann Racuya-Robbins

Founder and CEO

World Knowledge Bank?

www.wkbank.com

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[glen ep ropella]

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Ken Lloyd wrote:
> I distinguish between agents that are intrinsically part of a system,
> and that exhibit intelligence - either natural human intelligence or
> weak artificial intelligence. [...] Compare this to [...] what I call
> automata - as deterministic or probabilistic, rules based entities
> that act within a system. [...]

What do you claim is the difference between a weak artificial
intelligence algorithm and a rules-based algorithm?

- --
glen e. p. ropella, 971-219-3846, http://tempusdictum.com
There's no sense in being precise when you don't even know what you're
talking about. -- John Von Neumann

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[Ann Racuya-Robbins-2]

Thanks to all who responded.

 

"After all, it's the _braided_ or woven nature of causal networks (in
contrast to causal _chains_) that gave rise to ABM to begin with." glen e.
p. ropella

 

Could you or someone recommend a good ABM (as in the above quotation) that I
might study?

 

I thought glen's description of bayesian was very clear. Could glen or
someone else give a similarly clear and intuitive description of Bayesian
Monte Carlo or Markov Chain Monte Carlo method?

 

 

Ann Racuya-Robbins

World Knowledge BankR A Virtual Democratic Country

www.wkbank.com

 

============================================================

FRIAM Applied Complexity Group listserv

Meets Fridays 9a-11:30 at cafe at St. John's College

lectures, archives, unsubscribe, maps at http://www.friam.org

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[Kenneth Lloyd]

Ann,
 
Get you pencil and paper ready ...
 
http://www.sigevolution.org/issues/pdf/SIGEVOlution200702.pdf
 
Christopher Bishop, Neural Networks for Pattern Recognition, Oxford
University Press, 1995
 
Klaus Mosegaard - Monte Carlo Analysis of Geophysical Inverse Problems,  
http://wwwrses.anu.edu.au/~malcolm/papers/pdf/SamMos02.pdf 
 
Klaus Mosegaard, 1998: Resolution Analysis of General Inverse Problems
through Inverse Monte Carlo Sampling: Inverse Problems 14, pp. 405-426.
 
James Scales, M. Smith, and S. Treitel, Introductory Geophysical Inverse
Theory, Samizdat Press, Golden, CO USA, 2001,
 <http://acoustics.mines.edu/jscales/gp605/snapshot.pdf>
http://acoustics.mines.edu/jscales/gp605/snapshot.pdf
 
See the many books and papers by Mosegaard and Tarantola
 
The network aspects are generally covered in Newman, Barabasi and Watts, The
Structure and Dynamics of Networks, Princeton Series on Complexity
 
Ken
 
=============================
Kenneth A. Lloyd
CEO and Director of Systems Science
Watt Systems Technologies Inc.
Albuquerque, NM USA
kalloyd at wattsys.com
kenneth.lloyd at incose.org  - MBSE Complex, Adaptive & Stochastic Systems
kenneth.lloyd at nmug.net - Director of Education
www.wattsys.com <http://www.wattsys.com/>
 
 <http://www.linkedin.com/pub/7/9a/824> http://www.linkedin.com/pub/7/9a/824

 
This e-mail is intended only for the addressee named above. It may contain
privileged or confidential information.
If you are not the addressee you must not copy, distribute, disclose or use
any of the information in it.
If you have received it in error please delete it and immediately notify the
sender.

 


  _____  

From: [hidden email] [mailto:[hidden email]] On Behalf
Of admin at wkbank.com
Sent: Tuesday, April 08, 2008 10:16 AM
To: 'The Friday Morning Applied Complexity Coffee Group'
Subject: Re: [FRIAM] ABM,Baysian and Monte Carlo Method's Role in
Understanding Complexity



Thanks to all who responded.

 

"After all, it's the _braided_ or woven nature of causal networks (in
contrast to causal _chains_) that gave rise to ABM to begin with." glen e.
p. ropella

 

Could you or someone recommend a good ABM (as in the above quotation) that I
might study?

 

I thought glen's description of bayesian was very clear. Could glen or
someone else give a similarly clear and intuitive description of Bayesian
Monte Carlo or Markov Chain Monte Carlo method?

 

 

Ann Racuya-Robbins

World Knowledge BankR A Virtual Democratic Country

www.wkbank.com

 

============================================================

FRIAM Applied Complexity Group listserv

Meets Fridays 9a-11:30 at cafe at St. John's College

lectures, archives, unsubscribe, maps at http://www.friam.org

-------------- next part --------------
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[Ann Racuya-Robbins-2]

Thank you Ken,

 

I am wondering though if someone has an executive summary?

 

Were any of your citations specific agent based models?

  _____  

From: [hidden email] [mailto:[hidden email]] On Behalf
Of Ken Lloyd
Sent: Tuesday, April 08, 2008 10:51 AM
To: 'The Friday Morning Applied Complexity Coffee Group'
Subject: Re: [FRIAM] ABM,Baysian and Monte Carlo Method's Role in
Understanding Complexity

 

Ann,

 

Get you pencil and paper ready ...

 

http://www.sigevolution.org/issues/pdf/SIGEVOlution200702.pdf

 

Christopher Bishop, Neural Networks for Pattern Recognition, Oxford
University Press, 1995

 

Klaus Mosegaard - Monte Carlo Analysis of Geophysical Inverse Problems,  

http://wwwrses.anu.edu.au/~malcolm/papers/pdf/SamMos02.pdf 

 

Klaus Mosegaard, 1998: Resolution Analysis of General Inverse Problems
through Inverse Monte Carlo Sampling: Inverse Problems 14, pp. 405-426.

 

James Scales, M. Smith, and S. Treitel, Introductory Geophysical Inverse
Theory, Samizdat Press, Golden, CO USA, 2001,

 <http://acoustics.mines.edu/jscales/gp605/snapshot.pdf>
http://acoustics.mines.edu/jscales/gp605/snapshot.pdf

 

See the many books and papers by Mosegaard and Tarantola

 

The network aspects are generally covered in Newman, Barabasi and Watts, The
Structure and Dynamics of Networks, Princeton Series on Complexity

 

Ken

 

=============================

Kenneth A. Lloyd

CEO and Director of Systems Science

Watt Systems Technologies Inc.

Albuquerque, NM USA

kalloyd at wattsys.com

kenneth.lloyd at incose.org  - MBSE Complex, Adaptive & Stochastic Systems

kenneth.lloyd at nmug.net - Director of Education

www.wattsys.com <http://www.wattsys.com/>

 

 <http://www.linkedin.com/pub/7/9a/824> http://www.linkedin.com/pub/7/9a/824


 

This e-mail is intended only for the addressee named above. It may contain
privileged or confidential information.
If you are not the addressee you must not copy, distribute, disclose or use
any of the information in it.
If you have received it in error please delete it and immediately notify the
sender.

 

 


  _____  


From: [hidden email] [mailto:[hidden email]] On Behalf
Of admin at wkbank.com
Sent: Tuesday, April 08, 2008 10:16 AM
To: 'The Friday Morning Applied Complexity Coffee Group'
Subject: Re: [FRIAM] ABM,Baysian and Monte Carlo Method's Role in
Understanding Complexity

Thanks to all who responded.

 

"After all, it's the _braided_ or woven nature of causal networks (in
contrast to causal _chains_) that gave rise to ABM to begin with." glen e.
p. ropella

 

Could you or someone recommend a good ABM (as in the above quotation) that I
might study?

 

I thought glen's description of bayesian was very clear. Could glen or
someone else give a similarly clear and intuitive description of Bayesian
Monte Carlo or Markov Chain Monte Carlo method?

 

 

Ann Racuya-Robbins

World Knowledge BankR A Virtual Democratic Country

www.wkbank.com

 

============================================================

FRIAM Applied Complexity Group listserv

Meets Fridays 9a-11:30 at cafe at St. John's College

lectures, archives, unsubscribe, maps at http://www.friam.org

-------------- next part --------------
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[Kenneth Lloyd]

Ann,
 
Are you looking for information on desktop (generally, serial processing or
dual multi-processing, threaded) applications - such as NetLogo or StarLogo?
Or are you looking for information on ABM using MPP (massively parallel
processing) using multiple GPU (graphics cards) for the mathematical vector
and matrix simulations of "intelligent" agents (natural, human or weak
artificial)?
 
The sigevolution.org site shows how evolutionary genetic algorithms evolve
into patterns (such as swarm behavior, or entangled behavior as ref: in the
Ropella e-mail).  Some of this work is from E-Plex and their research into
Complex Pattern Producing Networks (CPPN's)
http://eplex.cs.ucf.edu/.  
Do not be mislead by the triviality of their examples - this can be powerful
stuff.
 
Another place to start is the Swarm wiki
http://www.swarm.org 
at the University of Michigan.
 
The term Agent Based Modeling (ABM) seems to cast a wide net - from "simple"
cellular automata to phenotypical behavior of genotypically evolved and
generated, quasi-intelligent artificial organisms (often referred to as
complex adaptive systems). I guess I would need to know at what level you
wish to understand agent based modeling of complex systems in order to
recommend an executive summary (which may be an oxymoron).  By this I mean,
Stephen Wolfram's A New Kind of Science may be considered by some to be an
executive summary on ABM. Specifically see page 991, Implications for
Everyday Systems (Notes for Chapter 8) - Issues of Modeling - which ties up
my recommendations with a pretty bow.  This is as close as I could come to
an executive summary.
 
Referring to your original e-mail, IMO, the Bayesian approach is meaningless
without the application of the Inverse Theory to refine it. I recommend
Scales (see original post), because it is simple, clear, and reachable.
 
Having said this, I'm afraid I haven't been very helpful.
 
=============================
Kenneth A. Lloyd
CEO and Director of Systems Science
Watt Systems Technologies Inc.
Albuquerque, NM USA
kalloyd at wattsys.com
kenneth.lloyd at incose.org  - MBSE Complex, Adaptive & Stochastic Systems
kenneth.lloyd at nmug.net - Director of Education
www.wattsys.com <http://www.wattsys.com/>
 
 <http://www.linkedin.com/pub/7/9a/824> http://www.linkedin.com/pub/7/9a/824

 
This e-mail is intended only for the addressee named above. It may contain
privileged or confidential information.
If you are not the addressee you must not copy, distribute, disclose or use
any of the information in it.
If you have received it in error please delete it and immediately notify the
sender.

 


  _____  

From: [hidden email] [mailto:[hidden email]] On Behalf
Of admin at wkbank.com
Sent: Tuesday, April 08, 2008 12:17 PM
To: 'The Friday Morning Applied Complexity Coffee Group'
Subject: Re: [FRIAM] ABM,Baysian and Monte Carlo Method's Role in
Understanding Complexity



Thank you Ken,

 

I am wondering though if someone has an executive summary?

 

Were any of your citations specific agent based models?


  _____  


From: [hidden email] [mailto:[hidden email]] On Behalf
Of Ken Lloyd
Sent: Tuesday, April 08, 2008 10:51 AM
To: 'The Friday Morning Applied Complexity Coffee Group'
Subject: Re: [FRIAM] ABM,Baysian and Monte Carlo Method's Role in
Understanding Complexity

 

Ann,

 

Get you pencil and paper ready ...

 

http://www.sigevolution.org/issues/pdf/SIGEVOlution200702.pdf

 

Christopher Bishop, Neural Networks for Pattern Recognition, Oxford
University Press, 1995

 

Klaus Mosegaard - Monte Carlo Analysis of Geophysical Inverse Problems,  

http://wwwrses.anu.edu.au/~malcolm/papers/pdf/SamMos02.pdf 

 

Klaus Mosegaard, 1998: Resolution Analysis of General Inverse Problems
through Inverse Monte Carlo Sampling: Inverse Problems 14, pp. 405-426.

 

James Scales, M. Smith, and S. Treitel, Introductory Geophysical Inverse
Theory, Samizdat Press, Golden, CO USA, 2001,

 <http://acoustics.mines.edu/jscales/gp605/snapshot.pdf>
http://acoustics.mines.edu/jscales/gp605/snapshot.pdf

 

See the many books and papers by Mosegaard and Tarantola

 

The network aspects are generally covered in Newman, Barabasi and Watts, The
Structure and Dynamics of Networks, Princeton Series on Complexity

 

Ken

 

=============================

Kenneth A. Lloyd

CEO and Director of Systems Science

Watt Systems Technologies Inc.

Albuquerque, NM USA

kalloyd at wattsys.com

kenneth.lloyd at incose.org  - MBSE Complex, Adaptive & Stochastic Systems

kenneth.lloyd at nmug.net - Director of Education

www.wattsys.com <http://www.wattsys.com/>

 

 <http://www.linkedin.com/pub/7/9a/824> http://www.linkedin.com/pub/7/9a/824


 

This e-mail is intended only for the addressee named above. It may contain
privileged or confidential information.
If you are not the addressee you must not copy, distribute, disclose or use
any of the information in it.
If you have received it in error please delete it and immediately notify the
sender.

 

 


  _____  


From: [hidden email] [mailto:[hidden email]] On Behalf
Of admin at wkbank.com
Sent: Tuesday, April 08, 2008 10:16 AM
To: 'The Friday Morning Applied Complexity Coffee Group'
Subject: Re: [FRIAM] ABM,Baysian and Monte Carlo Method's Role in
Understanding Complexity

Thanks to all who responded.

 

"After all, it's the _braided_ or woven nature of causal networks (in
contrast to causal _chains_) that gave rise to ABM to begin with." glen e.
p. ropella

 

Could you or someone recommend a good ABM (as in the above quotation) that I
might study?

 

I thought glen's description of bayesian was very clear. Could glen or
someone else give a similarly clear and intuitive description of Bayesian
Monte Carlo or Markov Chain Monte Carlo method?

 

 

Ann Racuya-Robbins

World Knowledge BankR A Virtual Democratic Country

www.wkbank.com

 

============================================================

FRIAM Applied Complexity Group listserv

Meets Fridays 9a-11:30 at cafe at St. John's College

lectures, archives, unsubscribe, maps at http://www.friam.org

-------------- next part --------------
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