Tag Archives: system dynamics

benefits of cycling infrastructure

From New Zealand, here’s a cost-benefit analysis of cycling infrastructure based on a participatory system dynamics model.

Methods: We used system dynamics modeling (SDM) to compare realistic policies, incorporating feedback effects, nonlinear relationships, and time delays between variables. We developed a system dynamics model of commuter bicycling through interviews and workshops with policy, community, and academic stakeholders. We incorporated best available evidence to simulate five policy scenarios over the next 40 years in Auckland, New Zealand. Injury, physical activity, fuel costs, air pollution, and carbon emissions outcomes were simulated.

Results: Using the simulation model, we demonstrated the kinds of policies that would likely be needed to change a historical pattern of decline in cycling into a pattern of growth that would meet policy goals. Our model projections suggest that transforming urban roads over the next 40 years, using best practice physical separation on main roads and bicycle-friendly speed reduction on local streets, would yield benefits 10–25 times greater than costs.

causal loop diagram

Donella Meadows

Here is Donella Meadows explaining how your bathtub is like your bank account.

If you’re about to take a bath, you have a desired water level in mind. You plug the drain, turn on the faucet and watch until the water rises to your chosen level (until the discrepancy between the desired and the actual state of the system is zero). Then you turn the water off.

If you start to get in the bath and discover that you’ve underestimated your volume and are about to produce an overflow, you can open the drain for awhile, until the water goes down to your desired level.

Those are two negative feedback loops, or correcting loops, one controlling the inflow, one controlling the outflow, either or both of which you can use to bring the water level to your goal. Notice that the goal and the feedback connections are not visible in the system. If you were an extraterrestrial trying to figure out why the tub fills and empties, it would take awhile to figure out that there’s an invisible goal and a discrepancy-measuring process going on in the head of the creature manipulating the faucets. But if you watched long enough, you could figure that out.

Very simple so far. Now let’s take into account that you have two taps, a hot and a cold, and that you’re also adjusting for another system state — temperature. Suppose the hot inflow is connected to a boiler way down in the basement, four floors below, so it doesn’t respond quickly. And you’re making faces at yourself in the mirror and not paying close attention to the water level. And, of course, the inflow pipe is connected to a reservoir somewhere, which is connected to the whole planetary hydrological cycle. The system begins to get complex, and realistic, and interesting.

Mentally change the bathtub into your checking account. Write checks, make deposits, add a faucet that keeps dribbling in a little interest and a special drain that sucks your balance even drier if it ever goes dry. Attach your account to a thousand others and let the bank create loans as a function of your combined and fluctuating deposits, link a thousand of those banks into a federal reserve system — and you begin to see how simple stocks and flows, plumbed together, make up systems way too complex to figure out.

“management flight simulators”

MIT has posted some free “management flight simulators” (aka games) online. It didn’t sound that interesting to me until I noticed that one of them is the “fish banks” game originally developed by Dennis Meadows who, I now recall, was from MIT. Other games simulate a clean energy startup and climate change negotiations.

R and NetLogo

I had never heard of Netlogo, which is a programming language for simulating and teaching agent based models. Agent-based modeling is important because it might be the key to real quantitative simulation in economics and the social sciences. You can keep drilling down into the links in this post from R-bloggers until you either run out of time or find out everything you want to know about it.