Posted by Erling Moxnes <Erling.Moxnes@ifi.uib.no>
Thanks to Krys, Alan and Jean-Jacques for replies to my question about
optimisation. We seem to agree that a danger with the optimisation technique
is that it may lead to overly simplified models. Alan points out that this
was the explicit, historical reason for Forrester to be sceptical of
optimisation. Alan also makes clear that SD is not a branch of optimisation.
However, none of the three replies really answer my question. I was not
asking about reasons not to use optimisation techniques. I asked for reasons
not to formulate problems as ""optimisation problems"", while performing
standard system dynamics studies.
I agree with Krys that stakeholders may have different views on the goal,
and with Krys and Jean-Jacques that in most situations there are multiple
criteria that should be considered. This imposes challenges for the analyst.
This challenge may be ""solved"" by focusing on one aspect only, for instance
the economist may focus on a simple measure of profits and the system
dynamicist may focus on stability. However, both analysts may get it only
half right. Thus, both may benefit from framing the problem as a
multicriteria optimisation problem, even if they end up doing what they
usually do. However, by using this frame, they may at least force themselves
to argue why stability or why profits do not matter in their respective
analyses. Or, even better, they may end up augmenting their studies to
include important aspects that would otherwise have been left out.
Going back to the original theme for this thread, ""evaluating expected
modeling benefits"", it seems to me that if the analyst considers all the
most important criteria for the stakeholders (using the mantra ""what should
ideally be 'optimised'""), the benefits of the analysis would become
apparent. The standard policy could be compared with a policy that comes out
of the model study, and stakeholders can compare the different criteria and
judge the benefits for themselves. Now, this is of course what many or most
system dynamicists do. My point is that we may become even better at this if
we frame our problem statements in terms of multicriteria optimisation.
My best,
Erling Moxnes
-- The System Dynamics Group University of Bergen, Norway http://www.ifi.uib.no/sd/
Posted by Erling Moxnes <Erling.Moxnes@ifi.uib.no>
posting date Thu, 21 Apr 2005 12:38:15 +0200
Problem Solving versus Optimization
-
Erling Moxnes Erling.Moxnes ifi.
- Junior Member
- Posts: 5
- Joined: Fri Mar 29, 2002 3:39 am
-
Jean-Jacques Laublé jean-jacques
- Senior Member
- Posts: 68
- Joined: Fri Mar 29, 2002 3:39 am
Problem Solving versus Optimization
Posted by =?iso-8859-1?Q?Jean-Jacques_Laubl=E9?= <jean-jacques.lauble@wanadoo.fr>
Hi Erling.
I think that there will never be a definitive answer to your question,
because it depends on the problem you are working on.
The tendency is always to generalize something that has worked on something
to everything else, instead of trying to understand why one solution worked
and could not have worked if things had been different.
I too did not get any answer to my original question about expected
profits . which was very practical and problem oriented, and that was
transformed into a theoretical question. There is probably too no real
answer to my question.
It is too dependant on what you mean by optimization.
Any problem solving tends always to optimize or at least better something.
The problem of optimizing is that it reduces the ability to invent something
radically different because you are obliged to define a goal and by doing
this you freeze partially the situation avoiding any deep modification.
Optimizing is looking for a perfection that sometimes does not exist. There
is not one unique way to build the future.
Two examples:
I have spent about 2000 hours of work on a pricing optimization model, and
have lately understood that the way I had formulated the problem (an
optimization with dynamics), there was no practical answer worth to be
considered. There was probably a theoretical answer but to make that
theoretical model real was not possible for me.
Any problem can in theory be solved with SD, as reality is made of a
multitude of stocks that vary during the time depending on what they get and
what they loose 'the rates'.
On the contrary I had once a problem that I solved in about 4 hours with the
optimization features of Vensim to my complete satisfaction. It is in fact
the only model I was satisfied with so far. It models a German company named
Accenta that proposes to its clients when they buy something, to give them
about 7 % of the value of the good and they will pay them back the entire
value of the good plus the 7% the following 10 years of course with an
increasing yearly payback.
It is obviously a bubble. It is legal in Germany as long as there is a small
but important remark that says that if the payment is impossible it will be
postponed until it will be completely paid.
The business has splendid results because their promises are not considered
as debts.
The problem I solved very simply was the necessary increase of their
turnover to be able to carry on paying back what they had promised. I found
about 48% a year.
It was then easy to find at what level of increase they were only able to
refund the 7% plus the interests, that was the condition for the customer
not to loose anything depending on any interest rate. Other calculations are
easy to do. If the rate of increase is of 20% for example, how much will
increase the refund duration? Etc.
One can then add, the effect of an eventual postponing of the refund on
their turnover that is depending on the confidence of their clients and
build in advance counteracting policies.
Comparing both problems, I have approximately understood why I could solve
quickly one completely, while I failed to solve the second. The first has
too many dependant and non dominant factors and the second is very simple
and can even be formulated to a certain extent with no SD at all.
Regards.
J.J. Laublé Allocar
Strasbourg France
Posted by =?iso-8859-1?Q?Jean-Jacques_Laubl=E9?= <jean-jacques.lauble@wanadoo.fr>
posting date Thu, 21 Apr 2005 16:58:33 +0200
Hi Erling.
I think that there will never be a definitive answer to your question,
because it depends on the problem you are working on.
The tendency is always to generalize something that has worked on something
to everything else, instead of trying to understand why one solution worked
and could not have worked if things had been different.
I too did not get any answer to my original question about expected
profits . which was very practical and problem oriented, and that was
transformed into a theoretical question. There is probably too no real
answer to my question.
It is too dependant on what you mean by optimization.
Any problem solving tends always to optimize or at least better something.
The problem of optimizing is that it reduces the ability to invent something
radically different because you are obliged to define a goal and by doing
this you freeze partially the situation avoiding any deep modification.
Optimizing is looking for a perfection that sometimes does not exist. There
is not one unique way to build the future.
Two examples:
I have spent about 2000 hours of work on a pricing optimization model, and
have lately understood that the way I had formulated the problem (an
optimization with dynamics), there was no practical answer worth to be
considered. There was probably a theoretical answer but to make that
theoretical model real was not possible for me.
Any problem can in theory be solved with SD, as reality is made of a
multitude of stocks that vary during the time depending on what they get and
what they loose 'the rates'.
On the contrary I had once a problem that I solved in about 4 hours with the
optimization features of Vensim to my complete satisfaction. It is in fact
the only model I was satisfied with so far. It models a German company named
Accenta that proposes to its clients when they buy something, to give them
about 7 % of the value of the good and they will pay them back the entire
value of the good plus the 7% the following 10 years of course with an
increasing yearly payback.
It is obviously a bubble. It is legal in Germany as long as there is a small
but important remark that says that if the payment is impossible it will be
postponed until it will be completely paid.
The business has splendid results because their promises are not considered
as debts.
The problem I solved very simply was the necessary increase of their
turnover to be able to carry on paying back what they had promised. I found
about 48% a year.
It was then easy to find at what level of increase they were only able to
refund the 7% plus the interests, that was the condition for the customer
not to loose anything depending on any interest rate. Other calculations are
easy to do. If the rate of increase is of 20% for example, how much will
increase the refund duration? Etc.
One can then add, the effect of an eventual postponing of the refund on
their turnover that is depending on the confidence of their clients and
build in advance counteracting policies.
Comparing both problems, I have approximately understood why I could solve
quickly one completely, while I failed to solve the second. The first has
too many dependant and non dominant factors and the second is very simple
and can even be formulated to a certain extent with no SD at all.
Regards.
J.J. Laublé Allocar
Strasbourg France
Posted by =?iso-8859-1?Q?Jean-Jacques_Laubl=E9?= <jean-jacques.lauble@wanadoo.fr>
posting date Thu, 21 Apr 2005 16:58:33 +0200
-
George A Simpson gsimpso4 csc.co
- Junior Member
- Posts: 9
- Joined: Fri Mar 29, 2002 3:39 am
Problem Solving versus Optimization
Posted by George A Simpson <gsimpso4@csc.com>
There is an article in the April 2005 Scientific American that addresses
your point: ""Shaping the Future"" by Steven Popper, Robert Lempert, and
Steven Bankes. The point they make is that real decision-making is more
sophisticated than simple optimisation.
The question is changed from ""what set of policies gives the optimum return
on investment (or other outcome metric)?
It becomes ""what actions today will best shape the future to our liking"".
What decision-makers are seeking is ROBUST policies, not necessarily
""optimal"" ones. Therefore the role of SD modelling is to allow a large
number of scenarios to be explored, identifying ones that have a good
outcome at low risk.
The authors also argue that incremental policy decisions, allowing for the
uncovering of new evidence and new influences, is a more powerful strategy
than one-shot optimisation.
We're using SD to probe the threads of the multiverse, trying to find thick
threads that lead in the direction we want to go.
..george...
Dr. George Simpson, Principal Consultant, CSC
CSC Alliance: Performance Engineer
CSC House, Fleet
Posted by George A Simpson <gsimpso4@csc.com>
posting date Thu, 21 Apr 2005 13:20:03 +0100
There is an article in the April 2005 Scientific American that addresses
your point: ""Shaping the Future"" by Steven Popper, Robert Lempert, and
Steven Bankes. The point they make is that real decision-making is more
sophisticated than simple optimisation.
The question is changed from ""what set of policies gives the optimum return
on investment (or other outcome metric)?
It becomes ""what actions today will best shape the future to our liking"".
What decision-makers are seeking is ROBUST policies, not necessarily
""optimal"" ones. Therefore the role of SD modelling is to allow a large
number of scenarios to be explored, identifying ones that have a good
outcome at low risk.
The authors also argue that incremental policy decisions, allowing for the
uncovering of new evidence and new influences, is a more powerful strategy
than one-shot optimisation.
We're using SD to probe the threads of the multiverse, trying to find thick
threads that lead in the direction we want to go.
..george...
Dr. George Simpson, Principal Consultant, CSC
CSC Alliance: Performance Engineer
CSC House, Fleet
Posted by George A Simpson <gsimpso4@csc.com>
posting date Thu, 21 Apr 2005 13:20:03 +0100
-
Bill Harris bill_harris facilita
- Junior Member
- Posts: 19
- Joined: Fri Mar 29, 2002 3:39 am
Problem Solving versus Optimization
Posted by Bill Harris <bill_harris@facilitatedsystems.com>
>> Now, this is of course what many or most
>> system dynamicists do. My point is that we may become even better at this if
>> we frame our problem statements in terms of multicriteria optimisation.
I've found the ideas behind soft systems (the multiple CATWOEs, the
""rich picture"") to help in breaking away from the single criterion
approach.
Bill
- --
Bill Harris http://facilitatedsystems.com/weblog/
Facilitated Systems Everett, WA 98208 USA
http://facilitatedsystems.com/ phone: +1 425 337-5541
Posted by Bill Harris <bill_harris@facilitatedsystems.com>
posting date Thu, 21 Apr 2005 09:03:19 -0700
>> Now, this is of course what many or most
>> system dynamicists do. My point is that we may become even better at this if
>> we frame our problem statements in terms of multicriteria optimisation.
I've found the ideas behind soft systems (the multiple CATWOEs, the
""rich picture"") to help in breaking away from the single criterion
approach.
Bill
- --
Bill Harris http://facilitatedsystems.com/weblog/
Facilitated Systems Everett, WA 98208 USA
http://facilitatedsystems.com/ phone: +1 425 337-5541
Posted by Bill Harris <bill_harris@facilitatedsystems.com>
posting date Thu, 21 Apr 2005 09:03:19 -0700
-
Doucet Freek doucet oasis.nl
- Newbie
- Posts: 1
- Joined: Fri Mar 29, 2002 3:39 am
Problem Solving versus Optimization
Posted by ""Doucet, Freek"" <doucet@oasis.nl>
Dear Mr. Moxnes,
I'm not quite sure what your question was in your QUERY, but I know of a
concept that might be of interest to you. It is related to your request for
reasons not to formulate problems as ""optimisation problems"" and it can be a
starting point for framing problem statements (I'm not quite sure about the
""multicriteria optimisation"" though). Since I know only little about your
background, I recognize that I run the risk that I'm not writing you
anything new at all. If that's the case: I didn't mean to waste your time.
In plant control a widely used way to look at the equipment and procedures
in there layers:
1 to stabilize (to make sure that things remain save, i.e. focus on not
geeting out of hand)
2 to control (to make sure that the plant goes where you want it to go, i.e.
focus on effectiveness and on benefits)
3 to optimize (to make sure that the plant uses as little scarce resources
as possible, i.e. focus on effiency and on costs)
Each layer is related to the state of the plant. E.g. If a plant is on fire,
than you apply the stabilizing procedures, and turn off the controlling and
optimizing procedures.
The impact of recognizing these layers is that you first define the state of
the system, and than within the relevant layer your most urgent problem. I
believe that SD has most impact on in the stabilizing layer, since stability
problems often come from unwanted and unrecognized possitive feedback loops.
E.g . nuclear power plant that heat up, denial of missing proven reserves
leading to more denial, flaws in accounting practices leading to more flaws,
etc.
In short, a reason not to frame problems as optimization problems is that
there might be bigger gains in first looking at stabilization problems.
Hope this helps. Let me know if you wish to further discuss.
Kind regards,
Freek Doucet.
Posted by ""Doucet, Freek"" <doucet@oasis.nl>
posting date Fri, 22 Apr 2005 10:29:47 +0200
Dear Mr. Moxnes,
I'm not quite sure what your question was in your QUERY, but I know of a
concept that might be of interest to you. It is related to your request for
reasons not to formulate problems as ""optimisation problems"" and it can be a
starting point for framing problem statements (I'm not quite sure about the
""multicriteria optimisation"" though). Since I know only little about your
background, I recognize that I run the risk that I'm not writing you
anything new at all. If that's the case: I didn't mean to waste your time.
In plant control a widely used way to look at the equipment and procedures
in there layers:
1 to stabilize (to make sure that things remain save, i.e. focus on not
geeting out of hand)
2 to control (to make sure that the plant goes where you want it to go, i.e.
focus on effectiveness and on benefits)
3 to optimize (to make sure that the plant uses as little scarce resources
as possible, i.e. focus on effiency and on costs)
Each layer is related to the state of the plant. E.g. If a plant is on fire,
than you apply the stabilizing procedures, and turn off the controlling and
optimizing procedures.
The impact of recognizing these layers is that you first define the state of
the system, and than within the relevant layer your most urgent problem. I
believe that SD has most impact on in the stabilizing layer, since stability
problems often come from unwanted and unrecognized possitive feedback loops.
E.g . nuclear power plant that heat up, denial of missing proven reserves
leading to more denial, flaws in accounting practices leading to more flaws,
etc.
In short, a reason not to frame problems as optimization problems is that
there might be bigger gains in first looking at stabilization problems.
Hope this helps. Let me know if you wish to further discuss.
Kind regards,
Freek Doucet.
Posted by ""Doucet, Freek"" <doucet@oasis.nl>
posting date Fri, 22 Apr 2005 10:29:47 +0200
-
richarp bellsouth.net
- Junior Member
- Posts: 2
- Joined: Fri Mar 29, 2002 3:39 am
Problem Solving versus Optimization
Posted by <richarp@bellsouth.net>
The question """"what actions today will best shape the future to our liking?""
sounds to me a bit like the philosophy behind adaptive management, but I
suppose that adaptive managers might also ask, ""How might we best shape
ourselves to be in a position to like the future?""
Proponents of adaptive management, I think, are wary of optimization because
it seems to imply a sense of rigidity. They point to changes ocurring
simultaneously at several scales and suggest that the set of variables one
might try to optimize today may not be appropriate for optimizing tomorrow.
One could still think of this in terms of optimization by seeing the
challenge of management as a dynamic set of optimization problems--that
is, with the variables to be optimized are always open to change.
Alternatively, one could view it as optimizing a more abstract variable
like resilience.
I must admit that I don't have extensive knowledge of optimization theories,
but the dynamic aspect included in this adaptive management view is different
from what I generally associate with optimization.
Cheers,
Richard Plate
Posted by <richarp@bellsouth.net>
posting date Fri, 22 Apr 2005 13:34:28 -0400
The question """"what actions today will best shape the future to our liking?""
sounds to me a bit like the philosophy behind adaptive management, but I
suppose that adaptive managers might also ask, ""How might we best shape
ourselves to be in a position to like the future?""
Proponents of adaptive management, I think, are wary of optimization because
it seems to imply a sense of rigidity. They point to changes ocurring
simultaneously at several scales and suggest that the set of variables one
might try to optimize today may not be appropriate for optimizing tomorrow.
One could still think of this in terms of optimization by seeing the
challenge of management as a dynamic set of optimization problems--that
is, with the variables to be optimized are always open to change.
Alternatively, one could view it as optimizing a more abstract variable
like resilience.
I must admit that I don't have extensive knowledge of optimization theories,
but the dynamic aspect included in this adaptive management view is different
from what I generally associate with optimization.
Cheers,
Richard Plate
Posted by <richarp@bellsouth.net>
posting date Fri, 22 Apr 2005 13:34:28 -0400
-
Jack Homer jhomer comcast.net
- Newbie
- Posts: 1
- Joined: Fri Mar 29, 2002 3:39 am
Problem Solving versus Optimization
Posted by ""Jack Homer"" <jhomer@comcast.net>
George Simpson cites the article ""Shaping the Future"" by Popper, Lempert,
and Bankes in the April edition of Scientific American.
http://www.sciam.com/article.cfm?chanID ... 414B7FFE9F
I have read the article and, while sympathetic with much of what it says,
was chagrined to find that it promotes the authors' ostensibly new approach
by contrasting it with a flat mischaracterization of Limits to Growth (and
by extension, System Dynamics). True, one of the authors is an economist
(the others are a physicist and computer scientist, all three at the RAND
Institute), and mischaracterization of LTG/SD by economists is certainly
nothing new. But what I found upsetting is that the authors felt compelled
to denigrate LTG/SD even when their approach sounds so similar to what we
do: evaluating alternative scenarios with simulation, and seeking to inform
decision-makers about tradeoffs and uncertainties rather than forecasting
single futures. Why couldn't they credit LTG/SD as a worthy predecessor
upon which they would like to build, instead of knocking it down and
promoting what they are doing as if it is something brand new
(""fundamentally rethinking the role of analysis"", as they say)? Consider
this quote from the article:
""The (in)famous report The Limits to Growth from the early 1970s is the
perfect example of how the standard tools of analysis often fail to mediate
such debates. A group of scientists and opinion leaders called the Club of
Rome predicted that the world would soon exhaust its natural resources
unless it took immediate action to slow their use. This conclusion flowed
from a then state-of-the-art computer model of the dynamics of resource
use.....
But the model was not wrong; it was just used incorrectly. Any computer
model is, by definition, a simplified mirror of the real world, its
predictions vulnerable to some neglected factor. The model developed for The
Limits to Growth revealed some important aspects of the challenges faced by
society. In presenting the analysis as a forecast, the authors stretched the
model beyond its limits and reduced the credibility of their entire research
program.""
Many ridiculous things have been said about LTG/SD, and this adds to that
list. But because the article is in a magazine with large circulation and
seems so dishonest and self-serving, I'm thinking that it deserves a
response, perhaps a letter to the editor of Scientific American, and perhaps
coming from Dennis Meadows and Jorgen Randers. (I haven't checked with
Dennis and Jorgen on this and have no idea whether they would have the time
or patience for it.) Does anyone second that idea, or have a different one?
Jack Homer
Posted by ""Jack Homer"" <jhomer@comcast.net>
posting date Fri, 22 Apr 2005 15:00:09 -0400
George Simpson cites the article ""Shaping the Future"" by Popper, Lempert,
and Bankes in the April edition of Scientific American.
http://www.sciam.com/article.cfm?chanID ... 414B7FFE9F
I have read the article and, while sympathetic with much of what it says,
was chagrined to find that it promotes the authors' ostensibly new approach
by contrasting it with a flat mischaracterization of Limits to Growth (and
by extension, System Dynamics). True, one of the authors is an economist
(the others are a physicist and computer scientist, all three at the RAND
Institute), and mischaracterization of LTG/SD by economists is certainly
nothing new. But what I found upsetting is that the authors felt compelled
to denigrate LTG/SD even when their approach sounds so similar to what we
do: evaluating alternative scenarios with simulation, and seeking to inform
decision-makers about tradeoffs and uncertainties rather than forecasting
single futures. Why couldn't they credit LTG/SD as a worthy predecessor
upon which they would like to build, instead of knocking it down and
promoting what they are doing as if it is something brand new
(""fundamentally rethinking the role of analysis"", as they say)? Consider
this quote from the article:
""The (in)famous report The Limits to Growth from the early 1970s is the
perfect example of how the standard tools of analysis often fail to mediate
such debates. A group of scientists and opinion leaders called the Club of
Rome predicted that the world would soon exhaust its natural resources
unless it took immediate action to slow their use. This conclusion flowed
from a then state-of-the-art computer model of the dynamics of resource
use.....
But the model was not wrong; it was just used incorrectly. Any computer
model is, by definition, a simplified mirror of the real world, its
predictions vulnerable to some neglected factor. The model developed for The
Limits to Growth revealed some important aspects of the challenges faced by
society. In presenting the analysis as a forecast, the authors stretched the
model beyond its limits and reduced the credibility of their entire research
program.""
Many ridiculous things have been said about LTG/SD, and this adds to that
list. But because the article is in a magazine with large circulation and
seems so dishonest and self-serving, I'm thinking that it deserves a
response, perhaps a letter to the editor of Scientific American, and perhaps
coming from Dennis Meadows and Jorgen Randers. (I haven't checked with
Dennis and Jorgen on this and have no idea whether they would have the time
or patience for it.) Does anyone second that idea, or have a different one?
Jack Homer
Posted by ""Jack Homer"" <jhomer@comcast.net>
posting date Fri, 22 Apr 2005 15:00:09 -0400
-
Bob Cavana Bob.Cavana vuw.ac.nz
- Junior Member
- Posts: 7
- Joined: Fri Mar 29, 2002 3:39 am
Problem Solving versus Optimization
Posted by ""Bob Cavana"" <Bob.Cavana@vuw.ac.nz>
George Simpson cites the article ""Shaping the Future"" by Popper, Lempert,
and Bankes in the April edition of Scientific American.
http://www.sciam.com/article.cfm?chanID ... 414B7FFE9F
hello Jack
i would like to 'second' your suggestion that a letter be sent to the editor
of Scientific American, to clarify the comments made by the 3 Rand Institute
employees in relation to the 'Limits to Growth' research, and by implication
to the field of System Dynamics [although SD is not explicitly stated in the
article].
the quote from the article that you posted on this listserve were presented
in a section in the article under the heading 'The Perils of Prediction'.
this implies that the purpose of the 'Limits to Growth' was 'prediction'
which it was clearly not (and the incorrect implication that SD models are
concerned with 'prediction'). in the next section under the heading 'Grappling
with the Future' the authors state: ""Conscious of this failing, analysts have
turned to techniques such as scenario planning that involve exploring
different possible futures rather than gambling on a single prediction.""
this also incorrectly implies that the 'Limits to Growth study (and SD) is
concerned with 'single prediction' rather than with policy, strategy or
scenario analsyis.
i agree with you Jack, that ""because the article is in a magazine with large
circulation and seems so dishonest and self-serving, I'm thinking that it
deserves a response, perhaps a letter to the editor of Scientific American,
and perhaps coming from Dennis Meadows and Jorgen Randers...""
If Dennis and Jorgen are not in a position to respond, then i think SDS
should consider putting a response together on behalf of its members
(i am happy to assist here!).
but firstly how do other SD community members intepret this article?
regards,
Bob Cavana
Posted by ""Bob Cavana"" <Bob.Cavana@vuw.ac.nz>
posting date Sun, 24 Apr 2005 11:06:39 +1200
George Simpson cites the article ""Shaping the Future"" by Popper, Lempert,
and Bankes in the April edition of Scientific American.
http://www.sciam.com/article.cfm?chanID ... 414B7FFE9F
hello Jack
i would like to 'second' your suggestion that a letter be sent to the editor
of Scientific American, to clarify the comments made by the 3 Rand Institute
employees in relation to the 'Limits to Growth' research, and by implication
to the field of System Dynamics [although SD is not explicitly stated in the
article].
the quote from the article that you posted on this listserve were presented
in a section in the article under the heading 'The Perils of Prediction'.
this implies that the purpose of the 'Limits to Growth' was 'prediction'
which it was clearly not (and the incorrect implication that SD models are
concerned with 'prediction'). in the next section under the heading 'Grappling
with the Future' the authors state: ""Conscious of this failing, analysts have
turned to techniques such as scenario planning that involve exploring
different possible futures rather than gambling on a single prediction.""
this also incorrectly implies that the 'Limits to Growth study (and SD) is
concerned with 'single prediction' rather than with policy, strategy or
scenario analsyis.
i agree with you Jack, that ""because the article is in a magazine with large
circulation and seems so dishonest and self-serving, I'm thinking that it
deserves a response, perhaps a letter to the editor of Scientific American,
and perhaps coming from Dennis Meadows and Jorgen Randers...""
If Dennis and Jorgen are not in a position to respond, then i think SDS
should consider putting a response together on behalf of its members
(i am happy to assist here!).
but firstly how do other SD community members intepret this article?
regards,
Bob Cavana
Posted by ""Bob Cavana"" <Bob.Cavana@vuw.ac.nz>
posting date Sun, 24 Apr 2005 11:06:39 +1200
-
Justin Lyon justin1028 yahoo.com
- Junior Member
- Posts: 17
- Joined: Fri Mar 29, 2002 3:39 am
Problem Solving versus Optimization
Posted by Justin Lyon <justin1028@yahoo.com>
I too have read the article and find the authors to be
strong supporters of simulation and system dynamics
for improving decision making.
You will note that they base their scenarios on what I
_think_ is a system dynamics simulation called
Wonderland.
Does anyone else know if in fact there work is based
on a system dynamics simulation?
The source code is freely available.
More information here:
http://www.rand.org/publications/MR/MR1626/
and here:
http://journal-ci.csse.monash.edu.au//c ... rbert.html
It seems like an SD model to mee. I shudder to think
of the difficulty in doing this with Simulink and
Matlab.
Seems like it would be much easier in Powersim Studio?
[ Host's note: The wonderland model and a number of comments on
it are available on Tom Fiddman's model library site
http://www.sd3.info/models/ ]
Warmly,
Justin Lyon
Posted by Justin Lyon <justin1028@yahoo.com>
posting date Mon, 25 Apr 2005 02:14:49 -0700 (PDT)
I too have read the article and find the authors to be
strong supporters of simulation and system dynamics
for improving decision making.
You will note that they base their scenarios on what I
_think_ is a system dynamics simulation called
Wonderland.
Does anyone else know if in fact there work is based
on a system dynamics simulation?
The source code is freely available.
More information here:
http://www.rand.org/publications/MR/MR1626/
and here:
http://journal-ci.csse.monash.edu.au//c ... rbert.html
It seems like an SD model to mee. I shudder to think
of the difficulty in doing this with Simulink and
Matlab.
Seems like it would be much easier in Powersim Studio?
[ Host's note: The wonderland model and a number of comments on
it are available on Tom Fiddman's model library site
http://www.sd3.info/models/ ]
Warmly,
Justin Lyon
Posted by Justin Lyon <justin1028@yahoo.com>
posting date Mon, 25 Apr 2005 02:14:49 -0700 (PDT)