Tom, Author at MetaSD - Page 43 of 90

Fight or flight in resource modeling

A nice reflection on modeling in emotionally charged situations, from Drew Jones, Don Seville & Donella Meadows, Resource Sustainability in Commodity Systems: The Sawmill Industry in the Northern Forest:

Through the workshops and discussions about the forest economy, we also learned that even raising questions of growth and limits can trigger strong defensive routines …, both at the individual level and the organizational level, that make it difficult even to remain engaged in thinking about ecological limits and, therefore, taking any action. Managing these complex process challenges effectively was essential to using systems modeling to help people move towards well-reasoned action or inaction.

… We were presenting our base run to a group of mill executives and landowners from five different companies. During the walk-through of the base-run behavior of mill capacity (which begins to contract severely several decades in the future) we found that a few participants quickly dismissed that possibility, saying, ‘‘Sawmill capacity in this region will never shrink like that,’’ and aggressively pressing us on what factors we had included so that (we presume) they could uncover something missing or incorrect and dismiss the findings. Their body language and tone of voice led us to believe the participants were angry and emotionally charged.

… we came to identify a recurring set of defensive routines, that is, both emotionally laden reflexive responses to seeing the graphs of overshoot in which participants did not connect their critique to an underlying structural theory, or simply disengaged from thinking about the questions at hand. … When we encountered these reactions, we found ourselves torn between avoiding the conflict (the ‘‘flight’’ reaction; modifying our story to fit within their pre-existing assumptions, de-emphasizing the behavior of the model and switching to interview mode, talking about the systems methodology rather than implications of this particular model) or by pushing harder on our own viewpoint (the ‘‘fight’’ reaction; explaining why our assumptions are right, defending the logic behind our model). Neither of these responses was effective.

Back to the presentation to the industry group. During a break, after we had just survived the morning’s tensions and had struggled to avoid ‘‘fight or flight,’’ Dana [Meadows] walked up to us, smiling, and said, ‘‘Isn’t this going great?’’ ‘‘What?!?,’’ we thought.

‘‘The main purpose of our modeling,’’ she said ‘‘is to bring people to this moment—the moment of discomfort, of cognitive dissonance, where they can begin to see how current ways of thinking and their deeply held beliefs are not working anymore, how they are creating a future that they don’t want. The key as a modeler who triggers denial or apathy is to bring the group to this moment, and then just breathe. Hold us there for as long as possible. Don’t fight back. Don’t qualify your conclusions about what structures create what behaviors. State them clearly, and then just hold on.’’

Debt crisis in the European Minifigure Union

A clever visualization from a 9-year-old:

Click through to the original .pdf for the numbered legend.

This is isn’t quite a causal loop diagram; arrows indicate “where each entity would shift the burden of bailout costs,” but the network of relationships implies a lot of interesting dynamics.

Via 4D Pie Charts.

Tipping points

The concept of tipping points is powerful, but sometimes a bit muddled. Things that get described as tipping points often sound to me like mere dramatic events or nonlinear effects, simple thermodynamic irreversibilities, or exponential signals emerging unexpectedly from noise. These may play a role in tipping points, and lead to surprises, but I don’t think they capture the essence of the idea. You can see examples (good and bad) if you sift through the images describing tipping points on google.

I think of tipping points as a feedback phenomenon: positive feedback that amplifies a disturbance, such that change takes off, even if the disturbance is removed. The key outcome is a system that is stable or resistant to disturbances up to a point, beyond which surprising things may happen.

A simple example is sitting in a chair. The system has two stable equilibria: sitting upright, and lying flat on your back (tipped over). There’s also an unstable equilibrium – the precarious moment when you’re balanced on the back legs of the chair, and the force of gravity is neutral. As long as you lean just a little bit, gravity is a restoring force – it will pull you back to the desirable upright equilibrium if you pick up your feet. Lean a bit further, past the unstable tipping point, and gravity begins to pull you over backwards. Gravity gains leverage the further you lean – a positive feedback. Waving your arms and legs won’t help much; you’re going to be flat on your back.

A more generalized explanation is given in catastrophe theory. The interesting twist is that a seemingly-stable system may acquire tipping points unexpectedly as its parameters drift into regimes that create new stable and unstable points, leading to surprises. Even without structural change to the system, its behavior mode can change unexpectedly as the state of the system moves from locally-stable territory to locally-unstable territory, which occurs due to shifting loop dominance from nonlinearities. (Think of the financial crisis and some kinds of aircraft accidents, for example.)

Anyone know some nice, simple tipping point models? I think I’ll have to mine my archives for some concrete examples…

Fortress USA

The Fortress World scenario came up in Bert de Vries’ presentation at the Balaton meeting today. It’s a dystopian global future in which the rich retreat into safe havens (a macro version of gated communities) while the rest of the world degenerates into some combination of feudal subsistence, resource extraction and chaos.

On dark days, looks increasingly to me like this is already playing out in the US with the disappearance of the middle class.

The drivers of rising inequity in the US seem fairly simple. With globalization, capital has become mobile while labor remains tied to geography. So, capital investment flees high wage countries (US) and jobs follow. Asset income goes up, because capital is leveraged by cheaper labor and has good bargaining power among hungry host countries. There’s downward pressure on rich world wages, because with less capital per capita employed, the marginal productivity of labor is lower.

It’s not all bad for the rich world working class, because cheaper goods (WalMart) offset wage losses to some degree. If asset and wage income were uniformly distributed, there might even be a net benefit.

However, asset income and wages aren’t uniformly distributed, so income disparity goes up. Pre-globalization, this wasn’t so noticeable, because there was an implicit deal, in which wage earners knew that, even if they didn’t own all the capital in their country, at least they’d be the beneficiaries of it in some sense through employment and trickle down. Free trade and mobile capital turns the deal into a divorce, which puts a sharp point on questions of property rights allocations that were never quite fair, and sows the seeds of future discontent among the losers.

So far, everyone appears to be committed to pursuing this thread to its logical conclusion. Probably most are unwitting participants; workers are as enthusiastic about offshoring of capital in their pension funds as are the captains of industry.

However, it seems to me that there are several corrosive effects. The asset-owning rich appear convinced that their windfall has arrived because they’re smart, that the misfortunes of the masses are due to laziness. Their incentive to invest in services like education for labor they don’t need is no longer palpable. Uneducated masses are easier to manipulate anyway. Meanwhile the masses are desperate (if misguided) to lower tax burdens in order to compete with offshore labor.

The ultimate effect seems likely to hollow out the human capital of the rich world, leaving only tycoons and serfs, with perhaps a few protected sectors of the economy (pilots for tycoons’ jets). But is that a plausible end-state for this game?

If I were an American tycoon endowed with a little enlightened self interest, I’d be worried about several ways things could go wrong:

Increasing income disparity and loss of human capital cause a loss of civility at home, requiring wealthy enclaves to become desperate armed camps.
Political turbulence abroad leads to loss of control of all that capital that went overseas.
The global economy reaches such a vast physical scale that no amount of personal wealth provides adequate insulation against its side effects.

These outcomes could be triggered or amplified by financial or ecological stress. Even if you don’t care about equity or social justice per se, these possibilities seem like a great reason to invest in human and social capital at home and abroad.

The Insidious Dynamics of Driving to School

When I passed by my old high school a few years ago, I was astonished to see that they’d paved over a nice grass field to make room for a vast parking lot, which must be for students. There’s really no excuse for driving to school in Palo Alto, CA – the weather is great, it’s flat, and no one lives more than a couple miles away.
Most of the responsibility falls to this nest of positive feedback loops:

I’ll start with a perception: parents worried about the safety of their kids start driving them to school (or, in Palo Alto, buy them a BMW so they can drive themselves). All that extra driving adds to traffic density, reinforcing the perceived danger on the roads. Over the long haul, all that traffic demands more lane space, so bike lanes and sidewalks get crowded out. And who wants to bike next to a bunch of hot, smelly tailpipes?

The more students drive, the less fit they get, which diminishes the fun of riding. They also become less tolerant of weather – in spite of Gore Tex, a lot of people react to a little water falling from the sky like the Wicked Witch of the West.

The result of all this is a kind of phase transition – at some point, conditions are right for all these positive loops to kick in, and everyone shifts from bike-dominated transport to driving.

This transition should not be irreversible, if one is patient. One can move the point at which the phase transition occurs, to encourage bicycling. I think there are two leverage points. First, a society that can afford cars for kids can afford to provide Dutch- or Danish-style traffic separation, breaking the safety loop and decreasing the attractiveness of driving by removing traffic lanes, which causes congestion until people go back to bikes. Second, make the cars pay for the infrastructure they use and the environmental and safety externalities they cause. Once people are back on bikes, they’ll get fitter and healthier, and the positive loops will help lock in a more sustainable mode.

Inspired by a comment in Bert de Vries’ talk this morning at the 30th Balaton Group meeting.

The GDP Song

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:

Sharing Systems

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:

Model building
- Group model building (in the spirit of SUNY Albany work)
- Collaborative modeling (e.g., a distributed team working on federated modules of a model, but not necessarily involving the client and group conceptualization processes)
- Collaborative causal loop diagramming
- Model code sharing and reuse
Model consumption
- Online games (playing through a simulation in real time) – possibly multiplayer
- Online simulations (interactive experimentation with a model) – possibly with a social aspect as at Climate Colab

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://ecotippingpoints.org/

http://www.cotelco.net/

http://www.bfi.org/

http://www.seedsystems.net/

http://www.clexchange.org/

http://www.watersfoundation.org/

http://climateinteractive.org

http://www.systemdynamics.org/MITCollectionRoadMaps.htm

http://www.systemswiki.org/index.php?title=Main_Page

http://insightmaker.com/

http://forio.com/

http://dt.asu.edu/

A natural driver of increasing CO2 concentration?

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

Exploring stimulus policy

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.