Hi everybody
I took some time to think about the reasons of little impact of SD on social situations. Most of the time SD studies are about big social organisations (state agencies, busyness). To be implemented the people in charged of these organisations must collaborate closely with the study. Unfortunately, the decision power in these organisations is diluted, varying with the time (example of elections), often more officious and hidden than official. To resume most of time nobody has a real power of decision that is more or less collective. But, of course, you will find people who can decide to launch or buy a study whose cost and risk is negligeable compared with its implementation. If you do a such a study, you have no real decision-taker to talk with and the model even of high quality will never be implemented. Not to mention that changes are difficult to trigger in big organisations due to their inertia.
More than 10 years ago I worked with a consultant on a study and he told me that in more than 10 years of DS practice, it was the first time he had the occasion to talk to a decision taker!
To resume, SD as long as it will not be possible to be used by decision takers or problem owner, like spreadsheets for example, has no chance to succeed in social situations and will never be used in big social problems within big social organisations due to the impossibility to work with identified real decision takers.
JJ
Some deep thinking here! Thanks.
Let me summarize your conclusion: “As long as government decision makers or problem owners do not personally use SD, SD will not succeed in solving big social problems within big social organisations.”
This implies that if a decision maker or problem owner does not know how to use a problem-solving tool, they must learn it themselves and apply it. They cannot do what is normally done: Hire consultants like Jack Homer to study the problem, or evaluate reports on the problem prepared by those outside government or by government problem solving projects. An example of the latter is the periodic IPCC reports on the climate change problem, prepared by thousands of scientists. These reports involve dozens of advanced tools (statistics, data analysis, ice core examination, carbon dating, econometric modeling, etc.) that are so specialized they are usually not personally used by political decision makers. That’s the purpose of specialized problem analysts.
Thus, I would conclude that while it would be helpful for government decision makers or problem owners to know SD, it is not a requirement for SD’s success on large social problems.
SPECIAL NOTE – The paper mentioned at the beginning of this thread is now complete. If runs 41 pages, which is too long. Now that we know what we want to say, we will shorten it in subsequent versions.
Jack Harich
Hi Jack.
What I explained in my post is not specific to SD. Any complex method will generate the same effect.
Big organisations have many decision takers and it is difficult to reach a consensus and common understanding on the problem, allowing a common agreement on the way to act. Not to mention that in big organisations, the decision takers and the problem owners are not the same people.
I recently read some parts of a book “discrete-event simulation and system dynamics for management decision making” and on page 223 about SD modelling, one could find the same figure that exists in Sterman’s business dynamics page 87 and 88 in the paragraph ‘modelling is iterative’. What is written here is rather evident and not specific to SD. But I have never seen any paper in 20 years reporting an experience about such an iterative process simply because to have such a process there must be an initial implementation, something that is rarely done. The question is then: is Sterman too academical, ignoring the constraints of reality, and proposing methods impossible to apply, or are practitioners not able to apply it for different reasons (lack of knowledge, experience, time, too low fees, etc…) or are clients not really motivated to follow such a long process.
Probably all these reasons at the same time.
Sterman’s method seems totally logical but to be applied he should have added to my opinion, that it is better to start with a very simple model, easy to understand by all the decision takers, generating policies easy to apply even if totally not optimised and rustic with the possibility to see easily the results after a certain delay.
I happened to meet 40 years ago in Grenoble the man who was building with 7 modelers a model based on Forrester method whose purpose was to model the French economy, solving all the government’s questions: level of taxes, level of interest rate, investments etc… He was proud to announce that he had already 60 thousand equations written in Pascal and that he expected the model once finished to have 150 thousand. The model was running on a big mainframe. All the economic papers where writing papers about this model. I heard still about this model for a certain time but after no more news! It was clear that nobody was able to use that model, the author included. Now if instead of building such a monstruous model, one had built a very simple one, with one stock, extremely easy to use and accept to apply simple policies for 5 years, and see the results and modify the model and apply new policies the way that Sterman is explaining it, I think that after 40 years and 8 successive modifications, one would now have in France a model that does a pretty good job something that is certainly not the case. But was it possible to do this very simple task?
Was it possible to propose a very simple model proposing simple policies? Everybody would have said: I could tell the same thing and propose such policies without any model at all, and it is not reasonable to wait 40 years to at last have something effective. People want quick results given by sophisticated methods because they do not believe that a simple method can generate quick results. A consultant will be judged by how sophisticated his work is, and will never dare to propose a method that is simple and requires a long time to become effective. This is what I wanted to explain but probably was badly formulated in my preceding post.
JJ
Wow, some very interesting thoughts!
Re: “Big organisations have many decision takers and it is difficult to reach a consensus and common understanding on the problem, allowing a common agreement on the way to act.”
Some struggle to make reasonably good decisions, and some do well. Those that do well, such as long-term industry leaders like Toyota or McKinsey Consultants, have an overall process that consistently works well for them. Thus, I don’t think the constraint is organization size. The limiting factor is process quality. Size is merely one more factor that must be well-managed. I have studied these processes to see what can be learned and applied to difficult large-scale social problems.
Re: “Modeling is iterative.” To me this mostly refers to model construction. An evolving model need not be implemented to learn most of how well it works. It can be run, inspected, and shown to experts. Laboratory experiments can be run. Natural real-world experiments can be run with past data. All this can serve as input to model improvement without “final” policy implementation. That’s the great benefit of simulation versus implementation. It’s a huge time and cost saver.
Sterman, Business Dynamics, p87 makes the point that “Iteration can occur from any step to any other step,” referring to a diagram with 5 steps. Bundled into step 5 is policy formulation, implementation (not stated but implied), and testing.
You write: “to have such an [iterative] process there must be an initial implementation.” The literature seems to disagree. In an iterative process, model iteration can occur due to insights from many steps, not just implementation.
Re: “it is better to start with a very simple model, easy to understand by all the decision takers, generating policies easy to apply even if totally not optimised and rustic with the possibility to see easily the results after a certain delay.” (Followed by a story about a very large model that was so large it ultimately failed and was abandoned.)
Great point, great story, I agree. This capitalizes on the well-known principle that well-running complex systems of any kind, technical or social, start simple and evolve to reach manageable complexity, which can take a long time. But countless cases exist where this principle was ignored, apparently due to hubris (unjustified excessive pride or confidence). In your story, the model builder’s high confidence was unjustified.
Re: “A consultant will be judged by how sophisticated his work is, and will never dare to propose a method that is simple and requires a long time to become effective.”
Sometimes yes, sometimes no. Here I think “method” means “policy recommendation.” There are many examples of simple policy changes that take a long time to become effective, such as universal education, curriculum changes, company or government hiring policies, John Maynard Keynes’s invention of fiscal policy to radically lessen the size of economic cycles, etc.
Another example is a company changing to the lean process, which is simply eliminating all forms of waste. Going lean take many years, especially since things often get worse before getting better. See this article.
A further example is a friend whose parents steered him to becoming a doctor. Simple decision. But it required 27 years of school-based education before his “final” graduation party, where the theme was “school is out forever!” 
Happy trails,
Jack Harich
Hi Jack.
It seems that we do not understand Sterman’s 88 page the same way.
You think that after the arrow into the real world, the process is finished. I think that it leads to a new start.
One should have Sterman’s opinion about this diagram.
Of course, as a system dynamics practitioner who sells his work, it is much more attractive to propose a one-shot model without having to experiment with it and modify it to start again a new cycle, and where the job is finished even if it is not implemented.
Not selling SD but using it on my own problems, I will never agree to anyone selling SD (or teaching, to resume earning one’s life with SD). This makes all discussions impossible. A car sales-man will never be objective about the car he sells.
But this discussion may suggest that what SD people think about SD, maybe very different from what SD user think. And the facts prove that. SD is scarcely used in practice and fails to demonstrate in practice the capability it pretends to have and that it would have if it was properly used.
In the last SD conference, I have not found among more than 200 papers, not one exposing a model that is expected to be implemented, or has been implemented, with or without success.
I have always been surprised how the field considers that his job is to make models without bothering about its eventual implementation.
But I will finish here, having not had in 20 years the opportunity to share ideas with an end SD user.
I wish you all the best and thank you for your answers.
JJ
Hi Jack
I saw that in your paper, you mention the world3.03 Vensim model. Are you interested or is anybody interested to know why I do not consider this model as reliable or just bug free? If anybody does not want to appear as considering this eventuality, he can contact me at sddp4119@gmail;com. JJ
Bonjour, JJ.
I would certainly appreciate this discussion of the World3 model, as long as it’s done in an objective, constructive manner. One consideration would be to define two of your terms, reliable and bug-free. This subject differs so much from the original topic of this thread that I would suggest starting a new thread.
Merci, Jack Harich
Hi Jack.
I think that discussing about the world3 is strongly related to the subject of this thread.
Reliable means confidence on the relative correctness of its outcome. I say relative because perfect correctness is impossible to achieve in social science.
Bug free means that every equation associated with a variable delivers the results expected by the name of the variable.
I think that the bug free condition is mandatory to hope reaching a minimum of relative correctness.
And this is not the case for the world3 model. The world3 model is a sophisticated model with a highly dynamic structure that reflects a specific point of view. Any other team would certainly build a different structure. To build such a model, one should build different models with different structures and compare their outcomes and eventually build a model averaging the different structures of the different models. I have a doubt about this having been done.
One should, too, build a very solid system of tests, embedded in the model or not, to test the coherency of the model relative to the reality it wants to describe (like the reality checks of Vensim).
I have not seen such tests. To resume, building a model with a relative reliability would need a considerable effort disproportional with its utility. The model describes an inexorable future with no specific policies impossible to apply due to the multiple policies and interest of the earth countries.
I think that there has been no effort to improve the reliability of the model, doing even partially the necessary actions described. Because if it had been done, I imagine that the authors would have done easier tests discovering eventual equation bugs in the model.
If you download the model, (preferably with any of the 32 bits Vensim versions, I had problems with the 64 ones) and set the model<setting<units equiv to ‘use stricte testing’ and press CTRL U it shows 9 errors that are not shown when stricte testing is off.
When stricte testing is off, Vensim forces a different dimension to DMNL values if it does not fit other values in the expression. For instance, 1 (dmnl) + 1 (person) will assume that 1 (dmnl) is in fact 1 (person) and will not report an error. If strict testing is on, it will report an error.
This test requires no effort and generates immediate results.
On the case of the World3, there are 9 errors reports when strict testing is on.
Sometimes with Vensim, it is necessary to test first Ctrl T before doing the Ctrl U to make the latter work. Everything explained earlier works the same (12 unit errors) with the two Richard’s models, that you can download as explained later on.
But you can use the 64 bit versions.
When I tested one model that was using the same ageing chain than the world 3 model, I did that test, and saw three variables that were in the first view of the model, the population view. It was the three maturation rates of the ageing chain. The error unit came from subtracting a 1/year mortality from a dimensionless unit. When one calculates the number of people maturing, one must calculate the number of people ageing from one cohort to the next. The people ageing are those who are still alive at the end of the time step period, one semester. The people still alive at the end of the period are those alive at the beginning of the period less those who died in the period which is equal to population less (population multiplied by the mortality per semester) and the mortality per semester equal the mortality per year multiplied by year per semester which is ½ years which is the time step.
And having the number of people still living at the end of the semester, one must divide it by the number of semesters of the cohort, to calculate the portion of people leaving the cohort for the next one. And the number of semesters is equal to the number of years multiplied by 2. And the result must be again multiplied by 2 to calculate the rate per year instead per semester. The result gives the equation population * (1 – (mortality of the cohort * time step) / size of the cohort in years. For the 0 to 14, the size is 15 years. The equation is dimensionally consistent). Population(year) * (1 (dimensionless) – (mortality of the cohort * time step (1/year * year ) ) / size of the cohort (year).
The equation is not only correct dimensionally but corrects the wrong result delivered by the original formulation. For instance if one forces with the original formulation, an epidemic on the 0 to 14 cohort, that kills half of the children in a semester, the maturation during this period is equal to 0, because the mortality per year is equal to 1, which makes the mortality per semester equal to 0.5.
But the maturation cannot be equal to 0, because some of the people still alive at the end of the semester are maturing. If one forces the total of people of the cohort dying, which makes the mortality per semester equal to 1, and the mortality per year equal to 2, The people of the next cohort are becoming younger: the maturing is negative. To avoid that error, it would have been better to make all the calculations, considering the mortality per semester or by time step. And the mortality per time step becomes dimensionless, it is a simple percentage. The error comes from a confusion between the mortality per year with the mortality per time step.
One can argue that the error came from changing the time step from one year to half a year. This is partially true, because in the case of a year time step, the results were correct, but the unit error was still there and should have warned the author of a possible bad formulation. To resume my point of view, I consider that the authors were poorly motivated by getting a really reliable model, because if it was the case, they would have made these very simple verifications. I am sure that there are many other equation bugs in the model. I do not even speak about verifying the structure of the model which is much more complicated.
I had recently finished a model and while analysing it, I found a big equation error. I then realized that I was working with a model which I was not 100 percent sure that there was no other bug equation and decided to verify the entire model of about 900 equations heavily subscripted.
I started by verifying the constants then the initials than the variable directly dependant from a constant or an initial etc…. When verifying an equation, I had already verified precedingly the input variables of the equation. That job took me 6 weeks, and I found at least 5 big mistakes changing the results of the model, changed multiple name variables that were badly named, and simplified multiple formulations making them easier to understand and particularly to verify.
In fact, during the months of building the model, I did not make sure that every single equation added or modified was hundred per cent bug free.
I have noticed that in all the public or not models that I took the time to verify, I have always very quickly found equation errors.
To my opinion, this state of the SD field, is damaging for its scientific reputation. For me SD, is a field based on solid scientific foundations, but that is not used scientifically. And it is one of the main reasons explaining the subject of this thread, even if it is not its root cause. The root cause comes to my opinion, from the fact that the authors considering that their models will never be implemented, do not see the interest of increasing considerably the difficulty of the task, by trying to build a really scientific work.
For me the solution, would be to concentrate on less macro topics much easier to solve and implement, before trying to solve more difficult ones.
Anyone interested can go the the Vensim forum www.ventanasystems.co.uk/forum/ and find two models that can be downloaded based on Richard models, and with the bis model, the only change to simulate an epidemic with the time of the epidemic and the level of the mortality and see the results of the maturation when setting the mortality to 1 or 2 per year. the model ter, has the proper equation for the maturation, and one can see the maturation is now correct, when one chooses a mortality.
I tried to upload these models, but the mdl extension is not accepted. My name on the Vensim forum is LAUJJL, if you do not find the post.
Best.
JJ
Thanks for your thoughts on the World3 model.
If you have found an error in someone’s model, then in my opinion the most productive thing to do is discuss this discovery with the creator of that model. Thank them for their work, explain what you found, and review why you feel that making the change would make it a better model. This is how scientists collaborate.
The fifty-year-old Limits to Growth World3 model is an iconic model with enormous impact. Countless negative criticisms have been directed against it. Yours is one more. But the only valid criticisms are those based on the purpose of the model. How well was the purpose achieved?
As stated on page 21 of Limits to Growth, 1972, the World3 model:
“…constitutes a preliminary attempt to improve our mental models of long-term, global problems…. Our world model was built specifically to investigate five major trends of global concern – accelerating industrialization, rapid population growth, widespread malnutrition, depletion of non-renewable resources, and a deteriorating environment. … The model we have constructed is, like every other model, imperfect, oversimplified, and unfinished. We are well aware of its shortcomings, but we believe that it is the most useful model now available for dealing with problems far out on the space-time graph.”
Based on that stated purpose, the model succeeded. It launched and focused a global realization that long-term environmental sustainability can be modeled, that that knowledge provides a firm foundation for policy, and that humanity must act now to avoid catastrophic collapse later. Technically, the model was so accurate (though that was not a goal, but a byproduct of good modeling practice) that Graham Turner, in On the Cusp of Global Collapse? Updated Comparison of The Limits to Growth with Historical Data, 2012, found that “The Limits to Growth standard run scenario produced 40 years ago continues to align well with historical data….”
In other words, have you found “a difference that makes a difference”? That is what science is most interested in. It is the question we must all ask ourselves, as we decide what to pursue and what not to pursue.
Hi
Thank you for your answer.
I have when I first saw that error, followed the right way to deal with that problem.
The date of this discovery is in 2011. During the 2011 Washington SD conference, Kaoru Yamaguchi asked me to help him testing his financial model of the USA exposing his theory of money as debt and gave me its source to be able to test it.
Coming back to Strasbourg, I started the testing. The first thing that I did, was unit testing putting the unit testing in Vensim to strict testing. It is something that does not require any work and can still help find some possible bugs. When doing the test, I found several unit errors and I started to verify the equations that were at the beginning of the model. I found very quickly the origin of the unit error, correcting an equation that was delivering wrong errors and not dimensionally consistent.
I sent an email to Kaoru, explaining very precisely the error and its correction and explaining how an extreme condition test made people in a population cohort become younger instead of getting older.
Kaoru contested the error, probably not making the effort to study my report and told me that it could not be an error, because he had copied the ageing chain in his model exactly from the world 3 model. I stopped testing Kaoru’s model, being disappointed by his reaction and the lack of trust of my work. But I looked at the World 3 model and found the same error in it. Not knowing the author of the world 3 model (probably a team-work) I exposed the error in the SD forum. I very quickly got and answer from Bob Eberlein who recognized my ‘refinement’ as correct, but finally did not recognize an error. Since this time the error has never been corrected in the World 3, even if the correction was very easy to perform: three equations to modify. I recognize that the SD community is not willing to recognize that the World 3 model might be bugged, but this is not my conception of a scientific behaviour.
Of course, the error has probably no effect on the general purpose and results of the World 3 model, but I have a serious doubt on a model where some basic easy tests have not been made and that would have needed much more difficult tests to perform. I could have judged that it is an affair of hazard, but I have observed for 20 years the papers published in the SD conferences and have always found bugs in the models published of a certain size that I took the time to study.
So, I think that I found a ‘difference that makes a difference’, because I do not think that the world 3 model was built correctly and deserves the critics you mentioned. And that if it had been built correctly it would be more recognized by the scientific community and would be less criticized.
I have always thought that before exposing complex theories, the SD community should start first to build models with equations that generate results coherent with the inputs of the equation and with the name of the variables: a variable badly named is as bad as a variable with a wrong equation.
Best.
JJ
Thanks, but you haven’t actually described what the World3 model “error” is and why it makes a significant difference. What nodes are involved? What equations need changing? What units need changing? Does the error affect graphical output or what? Why is error correction important?
The standard procedure in business projects when a discovery like this will be shared with many people is to write up a concise report. Updates can then be added as necessary. Without a report, you are reinventing the wheel every time you talk or write to someone about the error. The relevant knowledge is unorganized. That’s not efficient and wastes time.
Regarding: “Not knowing the author of the world 3 model (probably a team-work) I exposed the error in the SD forum. I very quickly got and answer from Bob Eberlein who recognized my ‘refinement’ as correct, but finally did not recognize an error.”
Bob is a sharp modeler, a wizard programmer, and a really nice guy. I trust his judgement. Without the report I don’t know the details of the situation, but it sounds like Bob, and before him Kaoru, thought the “error” was not that significant in terms of model impact. But yet to you it is significant. That’s the kind of thing the report should describe in detail. That report might be a good opportunity to start a new thread.
Hope this helps!
Hi Richard.
In my preceding posts, that you may have not read fully, I have given the full explanation of the equation errors and why they are important.
But to resume my opinion simply: I do not trust a model with unit errors, even they are strict unit testing errors because they may hide equations or conception errors, which is the case with the world3 model.
Regards.
JJ
Hi
I will resume my opinion about the reasons why SD does not solve big social problems, being myself, an SD user not selling the method to anybody. This is the opinion of a buyer of the method which will not be shared by other SD members who are all method sellers.
I have never seen an SD model about a macro-social problem that I judged as being able to deliver policies worth being applied.
Why?
For four main reasons:
First: SD is based on the principle of endogeneity that supposes that the main factors driving the reality modelled are endogenous in the model. Taking in account the complexity of social problems, finding the right boundaries guarantying this principle is difficult: too large boundaries generate huge models difficult to manage and costly, too narrow ones leave too many factors exogenous.
That choice must be justified by adequate studies, experimentations, taking into account the resources available and the purpose of the model to be sure that the boundaries chosen are the best possible choice, or are at least not too far from it.
I do not remember having ever seen such a study.
Second: SD is based on the principle of the homogeneity of the stocks, because of the general treatment of these stock their outputs in particular. This necessity obliges to split heterogeneous stocks into homogeneous ones. To what extent this splitting must be accomplished must be too justified, by different means, study, experience, taking into account the resources available, the purpose of the model, etc. like the first reason.
I do not remember having ever seen that done.
Third: probably the most important reason is about the structure of the model represented by its CLD. In general, models have a CLD, but the reader is more or less obliged to accept it, even if a lot of different structures can be imagined for the problem and the purpose of the model.
Here again, the CLD chosen must be justified, by different means, more or less of the same nature of the precedent ones. Different structures may generate very different behaviours, and it is critical to chose a good structure averaging all the other possible structures.
Fourth: in the models, there is no consideration with the capacity to implement concretely the policies proposed. This is too an important consideration. Models propose policies that should be implemented but not that can be implemented. The lack of general consideration for concrete implementation explains the difficulty of SD to exist as a practical method.
I have rarely seen models proposing policies to be implemented and in particular looking adapted to a possible implementation.
When one considers the three first reasons, the different possibilities should be studied as are studied the parameters with sensibility analysis, trying to find some central choice adapted to the purpose of the model and the resources available (financial, human etc.).
Respecting these three principles is of course extremely costly in time, money, and human resources and for this reason, is never respected. I think that this is the reason of the very low impact of SD on big social problems. It explains too the difficulty to respect the fourth reason exposed.
The profession, in its desire to solve big social problems, without having the resources needed, has made the choice of delivering models of low quality and level of scientific rigor.
I do not know the resources allocated to the building of the world3 model, and due to this limitation, I cannot blame its authors. They did what they could do. Comparing that model to my experience even if it is not one from an expert, I evaluate the cost of building such a model able to propose real implementable policies at a minimum of 10 million dollars and probably much more. I have too observed over the years, that the same limitation has generated practices that are far from scientific: lack of mathematical rigor for example and finally a kind of acceptance of the impossibility to build models that can be applied: SD is more generally used by researchers, with no purpose of any implementation. Why bother about rigor which is costly and time consuming, when one knows in advance that the model will never be applied?
I do not believe that the present trend towards low quality will change in the near future. I will never see this changing.
I observed too, the increase of influence of qualitative system thinking which is a consequence of the impossibility to achieve correct results with quantitative methods. I personally do not believe much in system thinking alone even if a good system thinking study is better than a bad quantitative one.
System thinking is good to expose a problem, help understanding its nature, difficulties, but this is always qualitative. I do not believe that it can deliver good policies without the help of quantification and the help of the rigor of mathematics.
The solution? Concentrate on problems that can be solved with the resources available, and there are plenty of them, and forget showing models that will never be implemented by anybody.
But due to the inertia and the already long time of existence of the field, I do not see the way to achieve this.
JJ
My point of view about the root cause of the lack of influence of SD on social problems is:
The complexity of the majority of models is far beyond the capacities of modelers and/or end users (clients, decision-takers, problem-owners) to build or use them.
The solution: keep models complexity within the above mentioned capacities.
JJ
Hello Jean-Jacques,
Merry Christmas, Happy New Year, and thanks for thinking your viewpoint over and offering a summary!
Looking back at this long thread, we covered a lot of ground, but did not reach any consensus on next steps. I wonder if we can do better with further discussion, using your “my point of view” summary.
Let me try to help by offering a reframing of your point of view. The purpose is to have a well-described problem to solve. This is often called the problem definition step. How about this:
The inability of analyses based on system dynamics to effectively solve difficult large-scale social problems, such as environmental sustainability, appears to be very much related to problem complexity. Present approaches to applying system dynamics to this class of problem fail due to overwhelming problem complexity.
This is because as models grow increasingly complex in order to mimic and correctly explain problem behavior, that complexity may pass a point where the model is too complex to understand, by modelers and/or model users. On easy, less complex problems, this point is not reached. This allows model examination to lead to effective problem solutions (aka policies). However, on highly complex problems, this point is reached long before the model is capable of offering the insights needed for effective solutions.
Now about your “The solution: keep models complexity within the above mentioned capacities.”
Reframing again, I think this means “The solution is to somehow keep models understandable (for modelers and users), no matter how complex the models become.” This is often called general solution strategy. It’s a starting point for further work. If sound, it can save a lot of time.
Do these two restatements of your viewpoint offer a good, well-focused starting point for productive discussion?
Thanks,
Jack Harich
Hi Jack
Thank you for your answer and happy Christmas and/or New Year for you and all those who will read this thread.
You have pretty well resumed the definition of the problem. I would add that it is not only the complexity of the problem that is the cause of the problem, but the way that the circumstances are taken into consideration during the modeling process. One of the main circumstances is the fact that clients think that not being themselves able to integrate all the factors influencing the reality, they expect modeling to do it effectively with success and think that if it does not do it, it will fail. They want the model to do what they cannot currently do.
The solution is to keep the model understandable for modelers and users but not how complex the model becomes. I think that the solution is to keep the model complexity within the capacities of the modelers and users. These capacities (time, experience, motivation, financial resources, number of people concerned etc.) vary from modelers to users. I think that it is better to understand a model generating policies highly suboptimal because of its simplicity but easy to implement than a model delivering more optimal policies but that are not well understood and more difficult to implement.
About the word of ‘understanding’ I mean intuitive understanding based on the knowledge and experience of reality and not the understanding of the mathematics of the model.
I think that we can start from that point, even if it needs still some clarification.
Best regards.
JJ
Hi JJ,
I appreciate your earnest efforts to tackle a problem that is central to the use of SD: Inability to solve difficult large-scale social problems, apparently due to the effect of high problem complexity on model complexity, understandability, and manageability.
However, I do see a problem. Every time I read one of your posts, I have to translate its words and concepts to correct well-articulated English. Other readers on this forum will probably have the same problem. This may explain why no one besides you and me are involved in this new discussion so far.
For example, you wrote below: “You have pretty well resumed the definition of the problem.” Resumed is the wrong word. It should be structured or articulated. But those two words have very different nuances. I have no idea which you are thinking.
The next sentence is: “I would add that it is not only the complexity of the problem that is the cause of the problem, but the way that the circumstances are taken into consideration during the modeling process.” I have no idea what “the way that the circumstances are taken into consideration during the modeling process” means. Is it your personal relationship to a client? Is it the the client’s unique situation? Is it the influence of the local economy, a nearby war, or what? Or is it perhaps the modeling process that is used?
The next sentence is: “They want the model to do what they cannot currently do.” I have no idea what this means. You need to be explicit about what “do” means.
And so on for most of the the remaining sentences.
May I suggest that if you wish to have an efficient and effective conversation about this problem on this forum, that you form a partnership with someone who knows your native language and knows English fluently, and have them rewrite your posts before posting. They should not just correct wording and grammar problems, but write in an articulate, easily understood manner. This is exactly what the System Dynamics Review requires of submitted papers. Non-native English speakers are suggested to use their English Language Editing service.
Please don’t be offended. I see great value in what you are trying to accomplish here. It would be tremendously helpful if on this forum we could work together and make some large progress on this problem.
Looking ahead, so far your problem solving approach is to start intuitively throwing out ideas on what you think are the causes and solutions. There are several problems with this approach: (1) There are no analytical, evidence-based reasons given for the causes and the solutions selected. (2) There are no concrete examples to clarify and begin to prove various claims, such as why this is a cause and why this solution strategy will likely work. (3) There is no central problem solving process used. There is only intuitive identification of causes and solutions.
Good news! Each of the above three problems is solvable. Once you have solved your non-native English speaker problem, the next step would be to choose a problem solving process to drive the project. This step can be made much easier by writing up a case that illustrates your problem well. Then we can work together to choose/design a process that fits the problem. I’d estimate you can do this in less than ten pages. Working with a case allows us to first solve the specific case, then other cases, and from there generalize to an overall solution that fits the entire class of problems.
The case(s) would contain sections like these:
- The client problem to solve.
- The history of trying to solving the problem via SD modeling.
- Exact description of where in the project history various types of failure occurred.
- Additional text as needed to completely describe the case.
- Relevant artifacts, like data, graphs, models.
If you want to proceed in this direction, I’d suggest starting a new thread, where you announce/describe the project and provide a link to the case PDF.
If you don’t want to proceed in this direction, fine. I can understand. But I hope you proceed, as there is lots of potential here.
Warm regards,
Jack Harich
Hi Jack
When using resume I meant summarize. Summarize is translated in French as ‘résumer’.
About the circumstances it is everything that can influence positively or negatively the modeling
process.
About ‘They want the model to do what they cannot currently do’
I meant They want the model to do (take everything into consideration) what they cannot currently
do (take everything into consideration).
But you set the purpose of this thread at a level that I will not be able to reach.
As you said English is not my native language and with so complex topics, it is a severe disadvantage.
You must understand too that I have not the motivation to write a 10 pages report.
I am not an academic used to write papers.
It is then better to stop my participation to this thread here.
Not to mention that I will probably not be able to post comments next year, having decided not
to renew my membership.
Best regards.
JJ
To summarize.
Before saving the world; system dynamicists should first buy a french fries stand and learn to peel potatoes.
JJ
Hi everybody.
One of the reasons of the difficulty to solve large social problems is the number of shareholders concerned with the problem and the fact that these shareholders have generally different point of views.
The solution is to reduce the number of shareholders and reduce their differences of point of view.
And one solution is to give to the regional people the power to decide about their problems. This is true democracy, something that you can see in Switzerland. And things are not so bad there.
Regards.
JJ