Category Archives: visualization

Another tangible user interface: the sandtable

This looks fun to play with: it’s a sandbox combined with digital sensing and projection tools. You shape your sand, and it maps the surface:

Digital Sandtable by Redfish Group @ Santa Fe Complex from stephen guerin on Vimeo.

Once your sandscape is constructed, you can simulate a forest fire on it, using a cigarette lighter as the ignition source, just like a real arsonist:

Lighting a fire on the Digital Sandtable from stephen guerin on Vimeo.

This isn’t quite as exciting to me as Jim Hines’ tangible user interface, because you can essentially change the initial conditions of your sandsystem, but not the structure of the model. However, it sure would be fun to play with, and could be pretty good at giving people insights about physical systems. It’s gone commercial as simtable.

I predict that this will soon go meta, with an ipad app that simulates the sandtable, allowing the user to push simsand around on the surface, flicking a lighter with a finger tap, creating the first virtual virtual forest fire environment.

Return of the Afghan spaghetti

The Afghanistan counterinsurgency causal loop diagram makes another appearance in this TED talk, in which Eric Berlow shows the hypnotized chickens the light:

I’m of two minds about this talk. I love that it embraces complexity rather than reacting with the knee-jerk “eeewww … gross” espoused by so many NYT commenters. The network view of the system highlights some interesting relationships, particularly when colored by the flavor of each sphere (military, ethnic, religious … ). Also, the generic categorization of variables that are actionable (unlike terrain) is useful. The insights from ecosystem simplification are potentially quite interesting, though we really only get a tantalizing hint at what might lie beneath.

However, I think the fundamental analogy between the system CLD and a food web or other network may only partially hold. That means that the insight, that influence typically lies within a few degrees of connectivity of the concept of interest, may not be generalizable. Generically, a dynamic model is a network of gains among state variables, and there are perhaps some reasons to think that, due to signal attenuation and so forth, that most influences are local. However, there are some important differences between the Afghan CLD and typical network diagrams.

In a food web, the nodes are all similar agents (species) which have a few generic relationships (eat or be eaten) with associated flows of information or resources. In a CLD, the nodes are a varied mix of agents, concepts, and resources. As a result, their interactions may differ wildly: the interaction between “relative popularity of insurgents” and “funding for insurgents” (from the diagram) is qualitatively different from that between “targeted strikes” and “perceived damages.” I suspect that in many models, the important behavior modes are driven by dynamics that span most of the diagram or model. That may be deliberate, because we’d like to construct models that describe a dynamic hypothesis, without a lot of extraneous material.

Probably the best way to confirm or deny my hypothesis would be to look at eigenvalue analysis of existing models. I don’t have time to dig into this, but Kampmann & Oliva’s analysis of Mass’ economic model is an interesting case study. In that model, the dominant structures responsible for oscillatory modes in the economy are a real mixed bag, with important contributions from both short and longish loops.

This bears further thought … please share yours, especially if you have a chance to look at Berlow’s PNAS article on food webs.

Interactive diagrams – obesity dynamics

Food-nutrition-health-exercise-energy interactions are an amazing nest of positive feedbacks, with many win-win opportunities, but more on that another time.

Instead, I’m hoisting an interesting influence diagram about obesity from the comments. At first glance, it’s just another plate of spaghetti.

ForesightObesity

But when you follow the link (do it now), there’s an interesting innovation: the diagram is interactive. You can zoom, scroll, and highlight particular sectors and dynamics. There’s some narrative here and here.

It took me a while to decide whether I’d call this a causal loop diagram or not. I think the primary distinction between a CLD and other kinds of mindmaps or process diagrams is the use of variables. On a CLD, each label represents a quantity that can vary, with a definite direction – TV Watching, Stress, Use of Medicines. Items on other kinds of diagrams might represent events or fuzzier constellations of concepts. This diagram doesn’t have link polarities (too bad) or loop polarities (which would be pretty incomprehensible anyway), but many other CLDs also avoid such labels for simplicity.

I think there’s a lot of potential for further exploration of this idea. There’s a lot you could do to relate structure to behavior, or at least to explain the rationale for structure (both shortcomings of the diagram). Each link, for example, could have its tale revealed when clicked, and key loops could be animated individually, with stories told. Drill-down could be extended to provide links between top-level subsystem relationships and more microscopic views.

I think huge diagrams like the one above are always going to be overwhelming to a layperson. Also, it’s hard to make even a small CLD good, so making a big one really accurate is tough. Therefore, I’d rather see advanced CLD presentations used to improve the communication of simpler stories, with a few loops. However, big or small, there might be many common technological benefits from dedicated diagramming software.

When sea level chartjunk attacks

SeaLevelAttack

This informationisbeautiful graphic is pretty, but I don’t find it informative. The y scale is nonlinear, and I don’t know if the x scale conveys anything. It’s hard to work out the timing of inundation, which is really the key. The focus on the low points of big cities in developed countries is misleading, because those will be defended for a long time. Ho Chi Minh city should be on there, as well as the US gulf coast. USA Today would love this.

Dynamics on the iPhone

Scott Johnson asks about C-LITE, an ultra-simple version of C-ROADS, built in Processing – a cool visually-oriented language.

C-LITE

(Click the image to try it).

With this experiment, I was striving for a couple things:

  • A reduced-form version of the climate model, with “good enough” accuracy and interactive speed, as in Vensim’s Synthesim mode (no client-server latency).
  • Tufte-like simplicity of the UI (no grids or axis labels to waste electrons). Moving the mouse around changes the emissions trajectory, and sweeps an indicator line that gives the scale of input and outputs.
  • Pervasive representation of uncertainty (indicated by shading on temperature as a start).

This is just a prototype, but it’s already more fun than models with traditional interfaces.

I wanted to run it on the iPhone, but was stymied by problems translating the model to Processing.js (javascript) and had to set it aside. Recently Travis Franck stepped in and did a manual translation, proving the concept, so I took another look at the problem. In the meantime, a neat export tool has made it easy. It turns out that my code problem was as simple as replacing “float []” with “float[]” so now I have a javascript version here. It runs well in Firefox, but there are a few glitches on Safari and iPhones – text doesn’t render properly, and I don’t quite understand the event model. Still, it’s cool that modest dynamic models can run realtime on the iPhone. [Update: forgot to mention that I sued compute rates & auxiliaries

compute levels

The only hassle is that equations have to be ordered manually. I built a Vensim prototype of the model halfway through, in order to stay clear on the structure as I flew seat-of-the pants.

With the latest Processing.js tools, it’s very easy to port to javascript, which runs on nearly everything. Getting it running on the iPhone (almost) was just a matter of discovering viewport meta tags and a line of CSS to set zero margins. The total codebase for my most complicated version so far is only 500 lines. I think there’s a lot of potential for sharing model insights through simple, appealing browser tools and handheld platforms.

As an aside, I always wondered why javascript didn’t seem to have much to do with Java. The answer is in this funny programming timeline. It’s basically false advertising.

Diagrams vs. Models

Following Bill Harris’ comment on Are causal loop diagrams useful? I went looking for Coyle’s hybrid influence diagrams. I didn’t find them, but instead ran across this interesting conversation in the SDR:

The tradition, one might call it the orthodoxy, in system dynamics is that a problem can only be analysed, and policy guidance given, through the aegis of a fully quantified model. In the last 15 years, however, a number of purely qualitative models have been described, and have been criticised, in the literature. This article briefly reviews that debate and then discusses some of the problems and risks sometimes involved in quantification. Those problems are exemplified by an analysis of a particular model, which turns out to bear little relation to the real problem it purported to analyse. Some qualitative models are then reviewed to show that they can, indeed, lead to policy insights and five roles for qualitative models are identified. Finally, a research agenda is proposed to determine the wise balance between qualitative and quantitative models.

… In none of this work was it stated or implied that dynamic behaviour can reliably be inferred from a complex diagram; it has simply been argued that describing a system is, in itself, a useful thing to do and may lead to better understanding of the problem in question. It has, on the other hand, been implied that, in some cases, quantification might be fraught with so many uncertainties that the model’s outputs could be so misleading that the policy inferences drawn from them might be illusory. The research issue is whether or not there are circumstances in which the uncertainties of simulation may be so large that the results are seriously misleading to the analyst and the client. … This stream of work has attracted some adverse comment. Lane has gone so far as to assert that system dynamics without quantified simulation is an oxymoron and has called it ‘system dynamics lite (sic)’. …

Coyle (2000) Qualitative and quantitative modelling in system dynamics: some research questions

Jack Homer and Rogelio Oliva aren’t buying it:

Geoff Coyle has recently posed the question as to whether or not there may be situations in which computer simulation adds no value beyond that gained from qualitative causal-loop mapping. We argue that simulation nearly always adds value, even in the face of significant uncertainties about data and the formulation of soft variables. This value derives from the fact that simulation models are formally testable, making it possible to draw behavioral and policy inferences reliably through simulation in a way that is rarely possible with maps alone. Even in those cases in which the uncertainties are too great to reach firm conclusions from a model, simulation can provide value by indicating which pieces of information would be required in order to make firm conclusions possible. Though qualitative mapping is useful for describing a problem situation and its possible causes and solutions, the added value of simulation modeling suggests that it should be used for dynamic analysis whenever the stakes are significant and time and budget permit.

Homer & Oliva (2001) Maps and models in system dynamics: a response to Coyle

Coyle rejoins:

This rejoinder clarifies that there is significant agreement between my position and that of Homer and Oliva as elaborated in their response. Where we differ is largely to the extent that quantification offers worthwhile benefit over and above analysis from qualitative analysis (diagrams and discourse) alone. Quantification may indeed offer potential value in many cases, though even here it may not actually represent ‘‘value for money’’. However, even more concerning is that in other cases the risks associated with attempting to quantify multiple and poorly understood soft relationships are likely to outweigh whatever potential benefit there might be. To support these propositions I add further citations to published work that recount effective qualitative-only based studies, and I offer a further real-world example where any attempts to quantify ‘‘multiple softness’’ could have lead to confusion rather than enlightenment. My proposition remains that this is an issue that deserves real research to test the positions of Homer and Oliva, myself, and no doubt others, which are at this stage largely based on personal experiences and anecdotal evidence.

Coyle (2001) Rejoinder to Homer and Oliva

My take: I agree with Coyle that qualitative models can often lead to insight. However, I don’t buy the argument that the risks of quantification of poorly understood soft variables exceeds the benefits. First, if the variables in question are really too squishy to get a grip on, that part of the modeling effort will fail. Even so, the modeler will have some other working pieces that are more physical or certain, providing insight into the context in which the soft variables operate. Second, as long as the modeler is doing things right, which means spending ample effort on validation and sensitivity analysis, the danger of dodgy quantification will reveal itself as large uncertainties in behavior subject to the assumptions in question. Third, the mere attempt  to quantify the qualitative is likely to yield some insight into the uncertain variables, which exceeds that derived from the purely qualitative approach. In fact, I would argue that the greater danger lies in the qualitative approach, because it is quite likely that plausible-looking constructs on a diagram will go unchallenged, yet harbor deep conceptual problems that would be revealed by modeling.

I see this as a cost-benefit question. With infinite resources, a model always beats a diagram. The trouble is that in many cases time, money and the will of participants are in short supply, or can’t be justified given the small scale of a problem. Often in those cases a qualitative approach is justified, and diagramming or other elicitation of structure is likely to yield a better outcome than pure talk. Also, where resources are limited, an overzealous modeling attempt could lead to narrow focus, overemphasis on easily quantifiable concepts, and implementation failure due to too much model and not enough process. If there’s a risk to modeling, that’s it – but that’s a risk of bad modeling, and there are many of those.

Are causal loop diagrams useful?

Reflecting on the Afghanistan counterinsurgency diagram in the NYTimes, Scott Johnson asked me whether I found causal loop diagrams (CLDs) to be useful. Some system dynamics hardliners don’t like them, and others use them routinely.

Here’s a CLD:

Chicken CLD

And here’s it’s stock-flow sibling:

Chicken Stock Flow

My bottom line is:

  • CLDs are very useful, if developed and presented with a little care.
  • It’s often clearer to use a hybrid diagram that includes stock-flow “main chains”. However, that also involves a higher burden of explanation of the visual language.
  • You can get into a lot of trouble if you try to mentally simulate the dynamics of a complex CLD, because they’re so underspecified (but you might be better off than talking, or making lists).
  • You’re more likely to know what you’re talking about if you go through the process of building a model.
  • A big, messy picture of a whole problem space can be a nice complement to a focused, high quality model.

Here’s why:

Continue reading

Visualizing biological time

A new paper on arXiv shows an interesting approach to visualizing time in systems with circadian or other rhythms. I haven’t figured out if it’s useful for oscillatory dynamic systems more generally, but it makes some neat visuals:

scheme

The method makes it possible to see changes in behavior in time series with waaay to many oscillations to explore on a normal 2D time-value plot:

cardiac

Read more on arXiv.