Agar's Bold Conjectures and Refutations

[Nick Thompson]

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: <friam-request at redfish.com>
> To: <friam at redfish.com>
> Date: 8/14/2006 12:00:21 PM
> Subject: Friam Digest, Vol 38, Issue 29
>
> Send Friam mailing list submissions to
> friam at redfish.com
>
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> When replying, please edit your Subject line so it is more specific
> than "Re: Contents of Friam digest..."
>
>
> 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" <nickthompson at earthlink.net>
> 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.    
> -------------- next part --------------
> An HTML attachment was scrubbed...
> URL:
/pipermail/friam_redfish.com/attachments/20060813/be24b22f/attachment-0001.h
tml

>
> ------------------------------
>
> Message: 2
> Date: Mon, 14 Aug 2006 14:14:55 +0200
> From: "Jochen Fromm" <fromm at vs.uni-kassel.de>
> 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.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. [...]
>
>
>
>
> ------------------------------
>
> Message: 3
> Date: Mon, 14 Aug 2006 08:07:05 -0600
> From: "Marcus G. Daniels" <mgd at santafe.edu>
> 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" <fromm at vs.uni-kassel.de>
> 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: mgd at santafe.edu
> 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 <magar at anth.umd.edu>
> 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
>
>
>
> ------------------------------
>
> _______________________________________________
> Friam mailing list
> Friam at redfish.com
> http://redfish.com/mailman/listinfo/friam_redfish.com
>
>
> End of Friam Digest, Vol 38, Issue 29
> *************************************

[Stephen Guerin]

Nick writes:
> 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?

I think a good modeling team will have two parallel tracks going when
constructing/validating the model:

The first would be qualitative research at the micro level. Observing agent
behaviors in the real world. While there is the challenge of many-to-many
relationship between micro rules and macro behavior, grounding your model in
qualitative research at the micro level can help reduce the ensemble of possible
micro rules.

The second parallel track would be at the macro level where the observer may
rely more on Pierce's abductive logic
(http://en.wikipedia.org/wiki/Abductive_reasoning) than inductive/deductive
reasoning to generate hyptotheses about the micro rules that may generate the
macro dynamics. To me, the generative character of abductive logic is where the
creative bit is that resists reduction to methodology in the practice in ABM...

-Steve

[Michael Agar]

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: <friam-request at redfish.com>
>> To: <friam at redfish.com>
>> Date: 8/14/2006 12:00:21 PM
>> Subject: Friam Digest, Vol 38, Issue 29
>>
>> Send Friam mailing list submissions to
>> friam at redfish.com
>>
>> To subscribe or unsubscribe via the World Wide Web, visit
>> http://redfish.com/mailman/listinfo/friam_redfish.com
>> or, via email, send a message with subject or body 'help' to
>> friam-request at redfish.com
>>
>> You can reach the person managing the list at
>> friam-owner at redfish.com
>>
>> When replying, please edit your Subject line so it is more specific
>> than "Re: Contents of Friam digest..."
>>
>>
>> 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" <nickthompson at earthlink.net>
>> 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.
>> -------------- next part --------------
>> An HTML attachment was scrubbed...
>> URL:
> /pipermail/friam_redfish.com/attachments/20060813/be24b22f/
> attachment-0001.h
> tml
>>
>> ------------------------------
>>
>> Message: 2
>> Date: Mon, 14 Aug 2006 14:14:55 +0200
>> From: "Jochen Fromm" <fromm at vs.uni-kassel.de>
>> 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.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. [...]
>>
>>
>>
>>
>> ------------------------------
>>
>> Message: 3
>> Date: Mon, 14 Aug 2006 08:07:05 -0600
>> From: "Marcus G. Daniels" <mgd at santafe.edu>
>> 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" <fromm at vs.uni-kassel.de>
>> 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: mgd at santafe.edu
>> 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 <magar at anth.umd.edu>
>> 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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>> http://redfish.com/mailman/listinfo/friam_redfish.com
>>
>>
>> End of Friam Digest, Vol 38, Issue 29
>> *************************************
>
>

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