Don't just do something, stand there! Reflections on the counterintuitive behavior of complex systems, seen through the eyes of System Dynamics, Systems Thinking and simulation.
In SD, we often talk about the pitfalls of managing systems with delays and feedback while paying attention to the wrong indicators. The classic example is navigating a car at high speed in the fog on an icy road by looking in the rearview mirror.
A related problem is managing your system to maximize the wrong goals, e.g. running the economy by a problematic metric like GDP. Here’s Alan AtKisson’s musical take on that:
I’m at the 30th Balaton Group meeting this week. A group of us just put our heads together to think about online approaches to teaching and sharing systems thinking and systems modeling. The basic question was, if you needed thousands of systems thinkers in a hurry, how could you scale up systems education quickly?
My list of interesting things people might want to do online:
Much can already be done through online model services like Forio and other means. However, I think there’s a lot more to be done. In particular, we’re weak on providing shared model transparency and quality control for any but the simplest models.
Some interesting systems & sustainability online learning links that came up in the conversation:
http://www.unep.org/ieacp/iea/
http://www.google.com/tools/dlpage/res/talkvideo/hangouts/
http://www.watersfoundation.org/
http://www.systemdynamics.org/MITCollectionRoadMaps.htm
You wouldn’t normally look at a sink with the tap running and conclude that the water level must be rising because the drain is backing up. Nevertheless, a physically similar idea has been popular in climate skeptic circles lately.
You actually don’t need much more than a mass balance to conclude that anthropogenic emissions are the cause of rising atmospheric CO2, but with a model and some data you can really pound a lot of nails into the coffin of the idea that temperature is somehow responsible.
This notion has been adequately debunked already, but here goes:
This is another experimental video. As before, there’s a lot of fine detail, so you may want to head over to Vimeo to view in full screen HD. I find it somewhat astonishing that it takes 45 minutes to explore a first-order model.
Here’s the model: co2corr2.vpm (runs in Vensim PLE; requires DSS or Pro for calibration optimization)
Update: a new copy, replacing a GET DATA FIRST TIME call to permit running with simpler versions of Vensim. co2corr3.vpm
To celebrate the debt ceiling deal, I updated my copy of Nathan Forrester’s model, A Dynamic Synthesis of Basic Macroeconomic Theory.
Now, to celebrate the bad economic news and increasing speculation of a double-dip depression replay, here are some reflections on policy, using that model.
The model combines a number of macro standards: the multiplier-accelerator, inventory adjustment, capital accumulation, the IS-LM model, aggregate supply/aggregate demand dynamics, the permanent income hypothesis and the Phillips curve.
Forrester experimented with the model to identify the effects of five policies intended to stabilize fluctuations: countercyclical government transfers and spending, graduated income taxes, and money supply growth or targets. He used simulations experiments and linear system analysis (frequency response and eigenvalue elasticity) to identify the contribution of policies to stability.
Interestingly, the countercyclical policies tend to destabilize the business cycle. However, they prove to be stabilizing for a long-term cycle associated with the multiplier-accelerator and involving capital stock and long-term expectations.
I got curious about the effect of these policies through a simulated recession like the one we’re now in. So, I started from equilibrium and created a recession by imposing a negative shock to final sales, which passes immediately into aggregate demand. Here’s what happens:
There’s a lot of fine detail, so you may want to head over to Vimeo to view in full screen HD.
This is part of a couple of experiments I’ve tried with screencasting models, as practice for creating some online Vensim training materials. My preliminary observation is that even a perfunctory exploration of a simple model is time consuming to create and places high demands on audience attention. It’s no wonder you never see any real data or math on the Discovery Channel. I’d be interested to hear of examples of this sort of thing done well.
I’ve been browsing the ALEC model legislation on ALECexposed, some of which infiltrated the Montana legislature. It’s discouragingly predictable stuff, but not without a bit of amusement. Take the ALEC Energy Principles:
Mission: To define a comprehensive strategy for energy security, production, and distribution in the states consistent with the Jeffersonian principles of free markets and federalism.
Except when authoritarian government is needed to stuff big infrastructure projects down the throats of unwilling private property owners:
Reliable electricity supply depends upon significant improvement of the transmission grid. Interstate and intrastate transmission siting authority and procedures must be addressed to facilitate the construction of needed new infrastructure.
Like free markets, federalism apparently has its limits:
Such plan shall only be approved by the commission if the expense of implementing such a plan is borne by the federal government.
Companies self-regulate just fine, without any rule of law, like they do in Nigeria:
Some of the results are “horrifying” and “unprecedented,” Brown says. The wells serving at least 10 Ogoni communities, for instance, have unsafe levels of hydrocarbons; one well had levels of benzene, a known carcinogen, that were 900 times greater than those deemed safe by the World Health Organization. In some areas, the researchers measured 8 centimeters of oil floating on top of groundwater and oil-soaked soils 5 meters deep. “Areas which appear unaffected at the surface are in reality severely contaminated underground,” the report concluded. In one bit of good news, the researchers concluded that spilled oil had not tainted local fish, a major source of protein, although it had ruined numerous fish farms.
A while back I decided to never buy another garden plant unless I’d first dug the hole for it. In a single stroke, this simple rule eliminated impulse shopping at the nursery, improved the survival rate of new plants, and increased overall garden productivity.
This got me thinking about the insidious dynamics of stuff, by which tools come to rule their masters. I’ve distilled most of my thinking into this picture:
This is mainly a visual post, but here’s a quick guide to some of the loops:
Black: stuff is the accumulation of shopping, less outflows from discarding and liquidation.
Red: Shopping adjusts the stock of stuff to a goal. The goal is set by income (a positive feedback, to the extent that stuff makes you more productive, so you can afford more stuff) and by the utility of stuff at the margin, which falls as you have less and less time to use each item of stuff, or acquire increasingly useless items.
So far, Economics 101 would tell a nice story of smooth adjustment of the shopping process to an equilibrium at the optimal stuff level. That’s defeated by the complexity of all of the other dynamics, which create a variety of possible vicious cycles and misperceptions of feedback that result in suboptimal stuffing.
Orange: You need stuff to go with the stuff. The iPad needs a dock, etc. Even if the stuff is truly simple, you need somewhere to put it.
Green: Society reinforces the need for stuff, via keep-up-with-the-Joneses and neglect of shared stuff. When you have too much stuff, C.H.A.O.S. ensues – “can’t have anyone over syndrome” – which reinforces the desire for stuff to hide the chaos or facilitate fun without social contact.
Blue: Stuff takes time, in a variety of ways. The more stuff you have, the less time you actually have for using stuff for fun. This can actually increase your desire for stuff, due to the desire to have fun more efficiently in the limited time available.
Brown: Pressure for time and more stuff triggers a bunch of loops involving quality of stuff. One response is to buy low-quality stuff, which soon increases the stock of broken stuff lying about, worsening time pressure. One response is the descent into disposability, which saves the time, at the expense of a high throughput (shopping->discarding) relative to the stock of stuff. Once you’re fully stocked with low-quality stuff, why bother fixing it when it breaks? Fixing one thing often results in collateral damage to another (computers are notorious for this).
I’m far from a successful minimalist yet, but here’s what’s working for me to various degrees:
There are also some policies that address the social dimensions of stuff:
This system has a lot of positive feedback, so once you get the loops running the right way, improvement really takes off.
A sort of causal loop diagram made the cover of the WSJ today:
Source: WSJ h/t Drew Jone
Is it useful, or chartjunk? When I started to look at it from the perspective of good SD diagramming practice, I realized that it’s the latter.
First off, this isn’t really a structural diagram at all. It depicts a sequence of events mixed up with icons depicting some entities involved in those events. From the chain of events, one might infer that there is causality, but that would be hazardous, particularly in this case where there is no operational description of what’s happening. Did money rush to havens because stocks fell, or did stocks fall because money was rushing to havens? How could we tell, without articulating the mechanics of stocks and flows of money in price formation?
A good diagram ought to include quantifiable elements that can vary, with clear directionality. Traders and bars of gold are clearly not helpful variables. Nor are events particularly useful; mental accounting for a “decrease in Stocks Fell” is difficult, for example.
A good diagram should also distinguish key states that describe a system, and distinguish actual states from desired states. Presumably the magnitude and direction of the “money rush to havens” is a function of desired and actual positions in various securities, but we won’t learn much about that from this picture.
Finally, a good diagram ought to give some indication of the polarity of relationships. But what exactly is happening at the top of this diagram, where blue seems to pass to red through the treasury building? Is the diagram arguing that rising treasuries caused falling stocks, so that this is a runaway positive feedback loop? (stock value down, flight to havens, treasuries up, stocks down…). Or are we to be reassured that rising treasuries lower yields, reversing the fall in stocks?
Personally, I preferred the old black & white all-text WSJ.
So far, the comedy coverage of the downgrade is more illuminating than some serious efforts:
Now that I’ve dumped on emerging behavioral feedback technologies, perhaps I should share a personal success story, in which measurement technology played a key role.
Ten years ago, a routine test revealed that my cholesterol was 280 mg/dl, and even higher in a confirmation test. That’s not instant death, but it’s bad. NIH calls <200 desirable, and many argue for even lower levels.
This was a surprise, because I was getting a fair amount of exercise and eating healthier than the typical American diet. I suspect that their must be some genetic component.
Without any discussion, my doctor handed me a prescription for Lipitor. Now, I liked that doctor, and I know he was smart because we’d just had an interesting conversation about wavelet analysis of time series data in biomedical research. But I think he was operating under the assumption that there was no potential for improvement from behavior change. This idea seems to grip much of the medical profession, and creates nasty self-fulfilling prophecy and eroding goals dynamics.
I decided that I didn’t want to take statins for the rest of my (hopefully long) life, so with the aid of spousal prodding and planning, I eliminated all cholesterol and saturated fats (essentially all animal products) from my diet. I was quickly below 200, and then made more gradual progress to a range of about 160 to 180.
Interestingly, since then I’ve also cut out a lot of carbohydrates, because the rest of my family is gluten intolerant, which takes the fun out of bread and pasta. My cholesterol is now lower than ever, 149 at last check, in spite of adding eggs, a big dietary cholesterol source, back into my diet.
While my wife deserves most of the credit for my success, I think technology played a key role as well. Early on, I bought a home cholesterol test meter (a Bioscanner 2000, predecessor to the CardioChek that I now have). The meter allowed me to close the loop between behavior and outcome without the long delay and expense involved with a trip to the doctor. That obviously had a practical benefit, but it was also very motivating.
In a recent conversation about data requirements for future Vensim, a colleague observed that the availability of ready access to ‘big data’ in corporations has had curious side effects. One might have hoped for a flowering of model-driven conversations about the firm. Instead, ubiquitous access to data has led managers to spend less time contemplating what data might actually be important. Crucial data for model calibration are often harder to get than they were in the bad old days, because:
Perhaps this is a consequence of the fact that data collection has become incredibly cheap. In the short run, business is about execution of essentially fixed strategies, and raw data is pretty darn useful for that. The problem is that the long run challenge of formulating strategies requires an investment of time to turn data into models (mental or formal), but modeling hasn’t experienced the same productivity revolution. This could leave companies more strategically blind than ever, and therefore accelerate the process of inadvertently walking off a cliff.
Around the same time, I ran into this Wired article about the power of feedback to change behavior. It details a variety of interesting innovations, from radar speed signs to brainwave headbands. I’ve experimented with similar stuff, like Daytum (found here, clever, but soon abandoned) and the Kill-a-watt (still used occasionally).
In the past two or three years, the plunging price of sensors has begun to foster a feedback-loop revolution. …
And today, their promise couldn’t be greater. The intransigence of human behavior has emerged as the root of most of the world’s biggest challenges. Witness the rise in obesity, the persistence of smoking, the soaring number of people who have one or more chronic diseases. Consider our problems with carbon emissions, where managing personal energy consumption could be the difference between a climate under control and one beyond help. And feedback loops aren’t just about solving problems. They could create opportunities. Feedback loops can improve how companies motivate and empower their employees, allowing workers to monitor their own productivity and set their own schedules. They could lead to lower consumption of precious resources and more productive use of what we do consume. They could allow people to set and achieve better-defined, more ambitious goals and curb destructive behaviors, replacing them with positive actions. Used in organizations or communities, they can help groups work together to take on more daunting challenges. In short, the feedback loop is an age-old strategy revitalized by state-of-the-art technology. As such, it is perhaps the most promising tool for behavioral change to have come along in decades.
But the applications don’t quite live up to these big ambitions:
… The GreenGoose concept starts with a sheet of stickers, each containing an accelerometer labeled with a cartoon icon of a familiar household object—a refrigerator handle, a water bottle, a toothbrush, a yard rake. But the secret to GreenGoose isn’t the accelerometer; that’s a less-than-a-dollar commodity. The key is the algorithm that Krejcarek’s team has coded into the chip next to the accelerometer that recognizes a particular pattern of movement. For a toothbrush, it’s a rapid back-and-forth that indicates somebody is brushing their teeth. … In essence, GreenGoose uses sensors to spray feedback loops like atomized perfume throughout our daily life—in our homes, our vehicles, our backyards. “Sensors are these little eyes and ears on whatever we do and how we do it,” Krejcarek says. “If a behavior has a pattern, if we can calculate a desired duration and intensity, we can create a system that rewards that behavior and encourages more of it.” Thus the first component of a feedback loop: data gathering.
Then comes the second step: relevance. GreenGoose converts the data into points, with a certain amount of action translating into a certain number of points, say 30 seconds of teeth brushing for two points. And here Krejcarek gets noticeably excited. “The points can be used in games on our website,” he says. “Think FarmVille but with live data.” Krejcarek plans to open the platform to game developers, who he hopes will create games that are simple, easy, and sticky. A few hours of raking leaves might build up points that can be used in a gardening game. And the games induce people to earn more points, which means repeating good behaviors. The idea, Krejcarek says, is to “create a bridge between the real world and the virtual world. This has all got to be fun.”
This strikes me as a rehash of the corporate experience: use cheap data to solve execution problems, but leave the big strategic questions unaddressed. The torrent of the measurable might even push the crucial intangibles – love, justice, happiness, wisdom – further toward the unmanaged margins of our existence.
My guess is that these technologies can help us solve our universal personal problems, particularly in areas like health and fitness where rewards are proximate in time and space. There might even be beneficial spillovers from healthier, happier personal lifestyles to reduced resource demand and
But I don’t see them doing much to solve global environmental problems, or even large-scale universal problems like urban decay and poverty. Those problems exist, not for lack of data, but for lack of feedback that is compelling to the same degree as the pressures of markets and other financial and social systems, which aren’t all about fun. In the US, we’re not even willing to entertain the idea of creating climate feedback loops. I suspect that the solutions to our biggest problems awaits some other technology that makes us much more productive at devising good strategies based on shared mental models.
