Notes from one of the closing plenaries at the 27th System Dynamics Conference::

Using C-ROADS to Support Analysis of International Climate Change Proposals by Andrew Jones, John Sterman, Thomas Fiddaman, Travis Franck, Elizabeth Sawin

Following on nicely from the presentation by Moxnes, in this presentation John Sterman talked about climate change, modelling, and decision making.

By way of introduction, he showed graphs of data derived from the International Panel on Climate Change (IPCC) models plotted alongside real world measurements. At the moment, we’re doing worse than the worst-case scenario predicts.

Next, he presented a study showing that when asked, 60% of MIT graduate students thought that stabilizing emissions would stabilize the level of carbon dioxide in the atmosphere, which is flat out wrong1. Stabilization can only occur when net emissions equals net removal, which means that we either have to decrease emissions, or somehow increase removal. To make things worse, a large number of policy makers and other supposed experts made the same mistake.

Having scared us a bit, Sterman moved on to talk about why climate change is such a hard policy nut to crack:

  • The science of climate change consists of large numbers of causal linkages of varying magnitudes whose dynamic interactions can only be understood through complex models. These are:
    • Far too complex for humans to reason through unaided, and so intuition about climate change is usually misleading.
    • Technically complex and require large amounts of computation to run.
    • Not transparent to policy makers, who only see results based on parameters chosen by experts whose judgement they might question.
    • Not accessible to policy makers who might otherwise experiment with policy proposals in order to gain an intuitive approximation of what’s going on.
  • Arguments about climate change incorporate statistical reasoning, another area in which human intuition is unreliable.
  • Policy proposals often rely on varying time frames, use different units, and are expressed relatively from different base figures. It’s difficult to reason about a 20% reduction from 1990 levels compared to a 25% reduction from 2004 levels, particularly given other possible parameters.

Of course, there’s political reasons for climate change being hard, too, such as the requirement for all actors to participate for a policy to truly be effective. But in the end, the biggest problem is simply education and the availability of information.

So, Sterman’s group have been working on a model they called C-ROADS, standing for Climate Rapid Overview And Decision Support. It’s an interactive System Dynamics model that captures most of the feedback loops involved in climate change and behaves similarly to the IPCC models. Since it’s interactive and relatively simple to use, it fills a gap in the discourse, allowing policy makers to do all those things I just said they couldn’t using the IPCC climate models. Furthermore, there’s an online version of the model called C-LEARN at

Having that model to play with makes it all a lot more real. Here’s a dirty secret. If you model all of the publicly available proposals for reducing emissions, by 2100, the amount of carbon in the atmosphere is 600 parts per million, and still rising. That’s a lot more than the 350 parts per million target that’s been often mentioned. Though, of course, we’re already exceeded that, so the target’s been moved to 400.

600 parts per million, when you take into account a mild amount of glacial melting and thermal expansion, means a world sea level rise along a shallow exponential curve to hit about 4 metres by 2100. Take into account the melting of the various ice sheets, non-carbon dioxide greenhouse gases, and the more hypothetical feedbacks such as the clathrate gun, and it gets much worse. Unfortunately, he didn’t talk much about these, nor are they available in the C-LEARN model.

To stabilize atmospheric carbon any time before 2100, you basically need everyone to agree to something like 80% cuts in emissions from 2004 levels by 2050. Interestingly, Europe’s already done that, Canada’s close, and if you can believe the Obama rhetoric, the US government at least wants to do that. Unfortunately, everyone needs to do this, even eventually including the African nations, who aren’t emitting much at all right now. Oh, and by the way, the New Zealand National government’s proposals are clearly inadequate, particularly when one takes into account our agricultural methane production..

Of course, even if we can manage cuts like that, we’ve already committed ourselves to about a 2 degree rise in temperature, which means at least a metre rise in sea level.


On the other hand, tools like this makes it a lot harder to obfuscate and mislead oneself about the measures need to address the problem, and from what I hear, it’s been used in training sessions with politicians in a bunch of countries, with remarkable results. So, there’s room for a little optimism, I guess.

[1] It’s not enough to stop emissions growth. If it’s not clear why that’s true, think about a bathtub. If the tap is pouring in more water than is draining, the tub will gradually fill. Same applies to carbon dioxide in the atmosphere. Basically, as described earlier, people suck at understanding dynamics.

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I’m going to be making design changes to my blog (the one at Meme Hazard) over the next few weeks. Apologies in advance for the mess and inevitable breakdowns.

For those of you reading this syndicated on Facebook, LJ or elsewhere, this is probably irrelevant. Please go about your normal business :)

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Notes from one of the closing plenaries at the 27th System Dynamics Conference:

Are advice adhered to? “Populist” versus “activist” or “systems analyst” advice by Erling Moxnes

Hypothetically, imagine you’re a reindeer herder. Weird, I know, but this research is from Norway.

You’ve got, say, 1850 reindeer on your island. Reindeer eat lichen, which grows back every year at a rate determined by its current height; slowly if there’s not much left, slowly if it’s nearing maximum growth, and swiftly about half way in between. For the amount of lichen to remain constant, the annual consumption has to equal the annual growth. Then, the maximum sustainable herd size is the amount of reindeer who consume lichen at the maximum growth rate. Given your existing reindeer herd, cultivate your herd and the lichen on your island to attain the maximum sustainable herd size.

Past research has shown that people are generally unable to determine near optimal strategies in situations like this. Interestingly, this is the case for experts as well as novices. This paper built on those results, looking further to see what would happen if people were given advice. The author considered three types of advice: ‘populist’ advice based on normal behaviour, ‘activist’ advice that basically accused reindeer herders of being irresponsible, and ‘systems’ advice using reasoned language similar to that in my previous paragraph. The strategies advocated by the activist and systems advice were optimal and identical, differing only in the justifications presented.

There were three groups of participants; one, the control, just heard the ‘populist’ advice, while the two experimental groups also heard either the activist or systems advice.

The results were pretty interesting. None of the groups followed the optimal strategy advocated by the systems or activist advice. That said, those who got the extra advice generally began to adapt their suboptimal strategy more swiftly than those who did not. Furthermore, those who got the emotive advice adapted more swiftly than those who got the well-reasoned advice.

Key point: People tend to build defective mental models of dynamic systems. Furthermore, people also tend not to follow advice that runs contrary to those models. Incidentally, it’s not the case that the suboptimal strategies were only a little worse – if one followed the explicit advice given, one would achieve the sustainable maximum herd within about 3 cycles; as it were, none of the groups  got close, even after 15 cycles. Taken with other research in this vein, it’s pretty clear that computational models give much better results than people. Furthermore, people are just as vulnerable, if not more so, to all of the failings that models are accused of (say, by climate skeptics).

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This will be my last journal style post on the conference; I’ve got two detailed write-ups of the “Closing Challenges” presentations to post separately, and at some point, I’ve got some reflections on the conference as a whole, and in general on what one can get out of a conference, but they’ll wait till later.

On Wednesday night, there was a panel titled “A Conversation with Peter Senge“. He’s the author of “The Fifth Discipline” and is considered one of the field’s luminaries. I’ve not read his books, but I understand he’s particularly interested in System Dynamics as a whole way of thinking rather than just as a modelling technique, if that makes any sense. Sounds like something I should read, anyway.

The discussion touched on a bunch of topics, but the two main themes were education and theories of change. In terms of education, he advocated what is to me, now, a fairly standard position involving less formal classrooms, mentoring instead of authoritarian teaching, and the use of models and games to scaffold learning. I didn’t get a lot out of the discussion of theories of change, mostly because it was really abstract, in terms of both content and structure. One nice quote came out of it, though: “People don’t resist change, they resist being changed”.

There was also a little bit of discussion about climate change, following on from John Sterman’s presentation (I’ll post notes on this in the next day or so) earlier in the day. Peter offered an interestingly optimistic perspective on the social implications of the problem. It’s not a problem that can just be solved by engineers or scientists, nor is it a problem that any one group of us can solve alone. Rather, it can be recast as a unique opportunity that forces the whole world to cooperate lest we all drown together, so to speak. This might seem hopelessly optimistic, but there’s a kernel of truth to this – strength can indeed come from adversity, and who knows what institutions might evolve to deal with this threat.

Thursday was workshop day. In the morning, I participated in one led by John Sterman in which he demonstrated a model based game his team had devised for teaching about commodity pricing. It was actually quite fun; we each played salt producers competing for shares in a US-wide market for salt to be spread on roads in winter, based purely on pricing. The optimal strategies had less to do with pricing out one’s competitors to capture market share than with silently colluding with competition in order to maintain a profitable market split evenly. This was counterintuitive, as the instinct was compete and grow by capturing market share. This sort of game could easily fit into classroom lessons, and was a great example of model-based education. More on this some other time when I’m better focused on education again.

In the afternoon, I sat in on part of a workshop called ‘Masterful Classes K-12′. I’d hoped it was about  employing system dynamics to teach other topics, as this has a lot in common with my interest in games and education; a model is just like a toy, which is just like a game without goals and structured activity. Unfortunately, it was more about masterful teaching of system dynamics, which was of less interest to me.

That evening, I went downtown to look around. I ended up having sushi for dinner. I wasn’t really expecting it to be great, as Albuquerque doesn’t strike me as an international city nor is it famed for its seafood.  Nonetheless, it was great and the specialty with green chilli I tried was really quite delightful, particularly in the contrast between the chilli and wasabi.

Friday was the sixth and final day, making this the longest academic conference I’ve been to. It was a ‘bonus’ day, with special events and a few SIG meetings.

In the morning, I participated in the ‘Copenhagen Climate Exercise’. This was basically a simplistic LARP based around the climate treaty negotiation process, using the C-ROADS model I’ll be discussing in a post tomorrow or the next day. We were divided into three teams representing the developed nations, the rapidly devoloping nations, and the least developed nations, together negotiating agreements on emissions reduction. These proposals were then presented to the moderators to run through the model.

I was on the team representing the least developed nations, along with about thirty others. To reflect our economic and political clout, we had to sit on the floor as we were not allowed chairs. Unfortunately, with a group as large as ours, group discussion and decision making was difficult to manage. As a result, our first proposal came from a small group of vocal players, leaving many of the others feeling left out. This made the activity a lot more interesting for me, however, as it opened up opportunities for coalition building and setting up some sort of deliberative structure. I love political games.

As a teaching tool, the format seemed effective. While making the activity more game-like would have satisfied me further, the additional complexity would probably have obscured the exercise’s educational purpose. Nonetheless, it’s got me thinking about how LARPs could be adapted for educational purposes. This is done to some extent already by some teachers, I think; Mr Hoskins, a teacher I once had when I was about six, certainly did something like a LARP involving pirates once. This is a tangent I might explore more some other time.

In the afternoon, I sat in on a strategy meeting held by the business SIG. They were discussing ways to make system dynamics more visible, as despite its many success stories, it’s largely unknown or misunderstood in the business world. I may have said I’ll advise on creating a blog and wiki to help market themselves. Oops. On the other hand, it’s good and worthwhile stuff to promote, and it’s not like I’ve committed to any sort of workload.

And that was that. I went back to my hostel, had a nap, then spent the evening reading, before flying back this afternoon. I was thinking about going out and doing some more touristing, but frankly, my brain had melted.

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