Talk:Complex socio-ecological systems/System dynamics

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The Forrester and Sterman pieces offer a systems approach to thinking that will likely serve as the backdrop for the rest of the semester’s discussions. Having recently taken an ecological and general systems course, I’m much more comfortable assessing the ideas of the two authors, and yet I still have some reservations. I’ll get to those in a moment, but first I’ll add that I attended a talk last night given by prominent futurist Ray Kurzweil; I did not stay for the duration of the event. As a technologist, Kurzweil assumes that all developments and innovations in the world are exponentially faster, bigger, and therefore better than ever before. He states a tautology that because these developments are better, the world is better, and that the world will be bettered further by better technologies until we reside in a utopia of nanotech blood thinners and iPods that can adhere to your belly fat and shock you when you’ve eaten too much. Oh frabjous day! Or more syllogistically: cooler technologies equal democracy equal a better world. Very problematic indeed, as many scholars have noted (check out Evgeny Morozov’s The Net Delusion to uncover the darker side of blogging and social media).

Forrester, and to a greater extent Sterman, are more critical of this approach. In Forrester’s understanding of the finite capacity of urban environments, and indeed the world, he posits that the technologists of his era (Kurzweil was at MIT in the ‘60s, it should be noted) failed to understand that population growth and pollution will either have to be developed proactively or they will massively handicap our species’ ability to enjoy a decent quality of life sooner rather than later (his model outputs have the numbers). Sterman is slightly more sanguine (the ‘70s was the era when neo-Malthusianism was in vogue, after all), but he too bemoans the inability of the average, or above average as the case may be, person to understand concepts of stocks and flows, model assumptions (“all models are wrong”) and generative thinking based on an understanding of soft and hard variables. Indeed, Sterman represents a greater comfort with the reflexivity of systems thinking and the recognition that should feedbacks and reorganizations be acknowledged, as might be stated in “resiliencese,” there is much hope for us yet.

It’s hard to refute either’s premise that finite resources and space limit exponential growth, conceptualized as being perpetual in neo-classical economics. It is much easier to point out that many of the metrics that Forrester claims Malthus was correct about (turns out the latter was 4/5, apparently) are considered pretty faulty nowadays; that birth control overlaid against demographic patterns in the industrialized and developing world illustrate a consistent positive correlation, as one small example. And in Sterman’s assessment, while he goes at lengths to portray the faultiness in assigning so many assumptions to his models, and how each and everyone should develop a recognition of modeling and simulation to reach their potential, he falls into the trap that many systems thinkers can’t seem to crawl out of: If the world’s problems are so darn complex, and what is needed is a greater ability to adapt to that complexity at definable scales, then how do we possibly hope to manage any of this with national governance structures? Supernational? In some sense, Sterman is asking for a greater degree of comfort with uncertainty, but offers conflicting solutions on how best to deal with this complexity given the errant outputs of most models.

At least both are open to the idea that stocks and flows change over time, that complexity is fluid by its very nature but that it must be captured in some way, which is far more sound than some of the assertions of the Daedalian technologists. (Sam Schramski)

_____ My first reaction on starting the Forrester paper is to question the underlying assumption that the human mind is “not adapted to interpreting how social systems behave.” It strikes me that every time a group has created a new and unique form of government, the attempt is there to understand the social system and create a predictive and adaptive leadership to a social system. Perhaps my reaction is because I am not used to the terminology in social sciences, and regardless, my reaction really has little to do with the rest of the paper. I find the writer’s rhetoric distracting, however. I’m honestly not always certain if his point is more that models are useful, or that it sucks to live right now in the first half of the paper. I notice that in his figure 6, pollution and quality of life, after an initial building point (which seems to be to be directly in line with how economists think of resource extraction) follows an oscillating pattern much like predator/prey interactions, where a build in one component in a positive feedback loop (ultimately destabilizing) is followed by a lagging build in the other, until the second component has a controlling influence (negative feedback) on the first. I infer from this that there are possible positive feedbacks to quality of life that are controlled by the negative feedback of some environmental, here pollution, factor. I also find it interesting that population growth and capital investment also exhibit these oscillations. By the end of the paper, I assume his main message is that the use of models is good and necessary and all that. I would agree, but I’m surprised he doesn’t spend any time on the types of models and their usefulness for various situations. For example, we might be able to very tightly predict social behavior using black-box models (Briesh and Iacobucci 1995, Briesch and Rajagopal 2010, for the example of neural network models), but they aren’t necessarily useful in developing or analyzing hypotheses about a system because the underlying model behavior is not based on any theoretical framework. And the inverse being also true, where a model might be based on some pretty advanced theory and exhibit interesting behavior when variables are altered, but does a poor job of predicting anything.

For Sterman’s paper: “As systems thinkers, we must constantly strive to break down the false barriers that divide us…” I like that quote, very much. I’m given to think of Bob Costanza’s work and the economic concept of externalities. Except that to systems thinking, externalities aren’t external at all, they were simply unaccounted for. While those externalities, like environmental degradation, might not be a component of the corporate economic world, they’re certainly a part of the system that includes all economic activity, social behavior, and policy decisions. It makes me wish all economists were also systems thinkers. And I have to admit, I sometimes struggle with stocks and flows, just like his students. The paper also has me musing about questions of scale, and the boundaries of a system. While something I’ve thought about before, I feel it’s always useful to think about the scale of the boundaries we put on a system as dictated by the questions we ask. Sometimes an ecosystem approach is the best, and sometimes a landscape approach is better. It’s a mistake to dictate the boundaries before the question; the best science always seems to be done when the question drives the scale of the modeled (theoretical or otherwise) system. To link this paper back to the Scheffer paper from last week, it strikes me with his atmospheric C concentration example that he’s talking about thresholds in non-linear relationships, which are often difficult to place (i.e., quantify), and can result in precipitous regime shifts, i.e., alternative configurations for a state. That said, recent work has focused on a rise in variance and skew as being a precursor to an incipient regime shift, and therefore helps define thresholds (see Carpenter and Brock 2006 and Guttal and Jayaprakash 2008 for examples). Then in his section of “All decision are based on models…and all models are wrong” I’m struck that he’s really reminding scientists to act like scientists, and to not be dogmatic about their own models. Good advice to remember at all times.

On a final note, it’s interesting how writing style itself can dictate how much we like a paper. For example, I didn’t care for the Forrester paper because of style more than content, but based on Sterman’s paper, I think “here’s a person I’d love to have a beer with.”

  • R.Briesch and D. Iacobucci, 1995. Using neural networks to compare theoretical models: An application to persuasive communications. American Marketing Association. 6: 177-302.
  • R. Briesch and P. Rajagopal. 2010. Neural networks applications in consumer behavior. J. of Consumer Psychology 20: 381-389.
  • S. Carpenter and W. Brock. 2006. Rising variance: a leading indicator of ecological transition. Ecology Letters 9: 311-318.
  • Guttal, V., and C. Jayaprakash. 2008. Changing skewness: an early warning signal of regime shifts in ecosystems. Ecology Letters 11:450-460.

DWatts 16:31, 13 January 2011 (UTC)


Sterman:

I had mixed reactions to this reading. On one hand, Sterman points out many things that make sense to me, such as 1) how our mental models shape our understanding of the society and environment; 2) How based on their mental models, people make truth-claims; 3) How boundaries, such as disciplinary boundaries, can limit one's understanding; 4) How human perception is limited; 5) How many truths are in the eyes of the beholder; 6) Importance of critical thinking - challenging one's models of thinking; 7) How we are shaped and shape the world (this sounds like C. Wright Mills arguments about using the sociological imagination, that is, understanding that we write our history, but our history is also written for us). 8) Importance of some concepts, such as flows, stock, nonlinearity, and time-lag to understand phenomena.

One the other hand, Sterman sounds over-optimistic about the systems thinking model, as if a systems thinking model would solve all problems. Sterman argues that there is policy resistance because people fail to understand complexity and think in terms of systems, that is, people have too narrow models. However, I am not sure how much understanding complexity would help reduce this problem. After all, isn't system thinking another model, and aren't all models wrong? Also, understanding complexity goes hand-in-hand with recognizing the limitations of the human mind.

Systems thinking does not sound empowering, because it adds lots of complexity. For example, recognizing that "we are both shaped by and shape the world" means that there are many things that we have little control over. On the other hand, the author rejects the type of reasoning that attributes causality to "unpredictable and uncontrolled" forces. These two arguments are contradictory.

I am left with the question of whether systems thinking help us make decisions. Some people might resist making truth claims because they recognize complexity and the limitations of our mental models. However, other people will make truth claims. And, for better or worse, the people who make truth claims are the ones that seem to be influencing policy-making. Sterman recognizes this tension. He points out that system thinking "cannot be an excuse for indecision."

Reading this article, I often felt that Sterman sounded like a social constructivist, who recognizes that knowledge is socially constructed, and that knowledge depends on each person's mental models. At other moments, I felt that he was a fundamentalist that argues that systems thinking is the only way, it is the only truth.

Sam: Thanks for sharing your thoughts about Ray Kurzweil. Kurzweil is among those who believe that technology will solve-all and that modernization is the only way. Often technology-believers fail to see that many of our current problems came about as a result of the advancement of technology.

flaleite 04:05, 14 January 2011 (UTC)

suggestions[edit source]

Hello, the following is just a suggestion, nothing mandatory. I try to post remarks on identi.ca (which then sends to twitter) while reading. I'll add tag #cses to the notices, so it can be found easier: #cses (identi.ca) + #cses (twitter).
Perhaps that stimulates collaboration? Let's see
And if some are versed with wikis, would be great, if you can add some info to Wikipedia (e.g. [1]) or here at Wikiversity ----Erkan Yilmaz uses the Wikiversity:Chat 16:51, 8 January 2011 (UTC)