 |
 |
Hi Ken, I'm puzzled by your description of system dynamics. It bears very little relation to my experience in using system dynamics modelling and being part of the international systems community. Perhaps there is something I'm missing, but your explanation doesn't ring true to my experience and understanding. For example: One of the key reasons for using systems dynamics modelling is that its predictive power *doesn't* depend on having significant amounts of data. Instead it requires a) the sufficient and often simple models of causal relation between factors; and b) data about single real world 'snapshots' with which to test the correlation of the system dynamics model with the real world. Second, the measure of predictability typically used in the system dynamics field is *usefulness* - the dynamics models are created because observing their outputs is more useful than guessing the future in situations that are too complicated for humans to manage 'in mind' or 'in discussion'. Third, one of the useful attributes of system dynamics modelling is that it *doesn't* require clear boundaries, nor does it require exactly modelling the behaviour or relationships of each and every factor involved. In that sense, you could see it as very like Physics in which the properties of situations are idealised to only contain the dominant influences. Fourth, a great advantage of systems dynamics modelling is that it is relatively cheap and fast to undertake, particularly compared with trend data analysis or attempts to classically mathematically model situations: the conditions where you really do need to collect each and every bit of data about the situation. Fifth, in my experience there are only two flavours of system dynamics: 'system dynamics' and 'non-linear systems modelling' (the heavy duty mathematical approach from which systems dynamics emerged that originated in non-linear control theory). There is however, a wide variety of software for system dynamics modelling. Sixth, using system dynamics for design requires very little resources. For most projects it certainly *doesn't* require large expert teams. For example, the last time I had contact with the CSIRO project to redesign the planning policies for tourism and economic development of the Ningaloo Reef (which uses system dynamic modelling as one of the main predictive tools) it had the system dynamic modelling undertaken at Curtin University being managed by a research fellow and a PhD student. Seven, the need for iterations is primarily only found in agent-based modelling, which might be considered a sort of bottom-up way software for system dynamics modelling Eighth, if you feel system dynamics is so poor at predictability, I'd be interested to know your explanation as to why IBM has been and is investing so heavily in system dynamics modelling for prediction of design outcomes for planners, policy makers and national governments - see, for example: http://www-03.ibm.com/press/us/en/pressrelease/35204.wss http://engagingcities.com/article/system-dynamics-planning-smarter-cities http://eyeonibm.com/2011/08/09/building-smart-cities-through-simulation/ http://www.ibm.com/smarterplanet/us/en/smarter_cities/overview/ IBM have been backing the use of system dynamics for making predictions for 40 years or so! (http://news.google.com/newspapers?nid=1982&dat=19740107&id=5SVHAAAAIBAJ&sji d=czMNAAAAIBAJ&pg=2748,883053 ) Systems dynamics modelling is already in widespread use for prediction to support decision making across several areas of interest to design professionals. This has been so since the 70s. A quick google search indicates bodies of literature describing projects using systems dynamics in a predictive capacity in: Health service policy design, rework management in the construction industries, management of national education systems, investment strategies (national level downwards); power systems managment (electricity, gas and oil); public health promotion systems; shipping design and investment; entrepreneurial support systems; supply chain behaviour; industry investment incentives; effects of environmental policymaking; behaviour of industrial/product design industries; design of short life cycle products; sustainability design; collaborative design processes; teaching and learning; R&D systems; foresight; and strategic planning, to name a small subset. I remain puzzled at your explanation. From my experience, system dynamics is a useful tool for addressing complicated and complicated design situations. I look forward to your clarification. Best wishes , Terry --- Dr Terence Love Director, Love Services Pty Ltd PO Box 226, Quinns Rocks Western Australia 6030 Tel: +61 (0)4 3497 5848 Fax:+61 (0)8 9305 7629 [log in to unmask] -- -----Original Message----- From: PhD-Design - This list is for discussion of PhD studies and related research in Design [mailto:[log in to unmask]] On Behalf Of Ken Friedman Sent: Tuesday, 20 August 2013 9:13 PM To: [log in to unmask] Subject: Re: Epistemologically Valid Theory and Dynamic Modeling Hi, Terry, This is just a quick note to say that I will respond to your note on systems dynamics in a day or so. I've been down with a cold, and between trying to stay on top of my obligations while reviewing some literature, it is taking me time to write a cogent reply. I do, indeed, know about systems dynamics. When I respond, however, I will explain why systems dynamics is not appropriate for "understanding or predicting the dynamics of the outcomes of" the specific issue of "national change to design education in India." One cannot use systems dynamics in every instance and systems dynamics is not appropriate for any intervention. There are several flavors of systems dynamics. While some versions of systems dynamic are suited to heuristic modeling for understanding, they may not be suited to "predicting the ways things will change over time in a highly interacting political environment." Predictive modeling is extremely difficult. As I will explain in my next post, systems dynamics has achieved nothing like the level of predictive capacity of physics, chemistry, or molecular biology. The predictive power of physics differs to that of systems dynamics by several orders of magnitude. There are good reasons for this, and I will explain them. The mathematical operations are not at issue. The issue involves connections between data and symbols, between symbols and what they represent in the world. The joke about economists - "economists use this kind of modeling on limited aspects of economic systems, but there are many jokes about throwing out the data that don't fit the theory" - wasn't a joke about systems theory or systems dynamics. It was a joke about the notion that we have workable systems for dynamic modeling of complex adaptive structures in the real world of organizations embedded in the context of national political life that enable valid predictions. Systems dynamics does permit valid predictions, in cases with clear boundaries and limits. I will describe these in my post. I have myself done some work in systems dynamics. This was forty years ago when I studied organizations and agents of change for my PhD. Most people in my cohort of PhD students were headed toward careers in psychology, anthropology, or urban planning. My project team had the outliers - I was one. The others included the deputy mayor of one of America's largest cities and the captain of an aircraft carrier earning a PhD in human behavior on the way to an admiral's flag. One clear aspect about the different flavors of systems dynamics is that predictive systems dynamics requires significant amounts of data. Accurate and responsible predictions usually require a large, expert project team with sufficient time to gather data and model it through many iterations well before the results are due. Relatively short-term projects do not permit this unless the financial stakes are so great that they justify investing in large teams, massive data, and heavy computing power. Frequent changes to governments, stakeholders, policies, and decision-makers affect most contexts such as those that Ranjan describes. These can benefit from visualisation for understanding and heuristic flavors of system dynamics, but they generally don't permit or justify the costs or work required for predictive modeling. The reason I did not think of systems dynamics in the context of your post is simple. The capacity to engage and deploy systems dynamics for predictive modeling rests on far more than educational background. The linked constraints of time, money, and quality - the iron triangle - come into play. You specified prediction as well as understanding. To me, that ruled out systems dynamics. I asked, "What kinds of symbols permit us to undertake dynamic predictive modeling of complex adaptive structures in the real world of organizations embedded in the context of national political life? . If you have not done this kind of work, can you suggest some responsible publications that demonstrate such a system or show it in operation?" I was hoping for working examples. None of the papers in the links you posted meet these criteria. Neither do your papers. These papers do not involve predictive dynamic symbolic modeling of the kind you described to Ranjan or to me. Rather, they are papers stating your views about systems dynamics. They bear roughly the relation to quantitative, predictive systems dynamics that papers on philosophy of science bear to physics, chemistry, or molecular biology. Systems dynamics has many values and uses. Some are heuristic. Some involve understanding. The most difficult form of systems dynamics involves predictive modeling. Most of the people who do this kind of work publish their findings in peer-reviewed journals. I've observed that the articles in systems dynamics journals are modest and limited in their claims, and the systems dynamics literature is very carefully bounded. There are also systems dynamics people who work in consulting, government, and other fields - I'd guess that what they have in common is an appreciation of the different uses of systems dynamics and the different kinds of systems dynamics that are available for those uses. There is also a certain measure of humility that seems to typify anyone who really works with these systems in a professional way. By the time you take on responsibility for a capital ship of more than 100,000 tons plus 80 or 90 fighter jets and advanced missile systems to protect them, you grow cautious about the power of prediction. We had a standing joke in our team, attributed to the physicist Niels Bohr (and to others): "Prediction is very difficult. Especially about the future." Yours, Ken Ken Friedman, PhD, DSc (hc), FDRS | University Distinguished Professor | Swinburne University of Technology | Melbourne, Australia | [log in to unmask]<mailto:[log in to unmask]> | Mobile +61 404 830 462 | Home Page http://www.swinburne.edu.au/design/people/Professor-Ken-Friedman-ID22.html<h ttp://www.swinburne.edu.au/design> Academia Page http://swinburne.academia.edu/KenFriedman About Me Page http://about.me/ken_friedman Guest Professor | College of Design and Innovation | Tongji University | Shanghai, China ----------------------------------------------------------------- PhD-Design mailing list <[log in to unmask]> Discussion of PhD studies and related research in Design Subscribe or Unsubscribe at https://www.jiscmail.ac.uk/phd-design -----------------------------------------------------------------