SystemDynamics Archives - Page 21 of 22

Four Legs and a Tail

An effective climate policy needs prices, technology, institutional rules, and preferences.

I’m continuously irked by calls for R&D to save us from climate change. Yes, we need it very badly, but it’s no panacea. Without other signals, like a price on carbon, technology isn’t going to do a lot. It’s a one-legged dog. True, we might get lucky with some magic bullet, but I’m not willing to count on that. An effective climate policy needs four legs:

Prices
Technology (the landscape of possibilities on which we make decisions)
Institutional rules and procedures
Preferences, operating within social networks

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My Bathtub is Nonlinear

I’m working on raising my kids as systems thinkers. I’ve been meaning to share some of our adventures here for some time, so here’s a first installment, from quite a while back.

I decided to ignore the great online resources for system dynamics education and reinvent the wheel. But where to start? I wanted an exercise that included stocks and flows, accumulation, graph reading, estimation, and data collection, with as much excitement as could be had indoors. (It was 20 below outside, so fire and explosions weren’t an option).

We grabbed a sheet of graph paper, fat pens, a yardstick, and a stopwatch and headed for the bathtub. Step 1 (to sustain interest) was turn on the tap to fill the tub. While it filled, I drew time and depth axes on the graph paper and explained what we were trying to do. That involved explaining what a graph was for, and what locations on the axes meant (they were perhaps 5 and 6 and probably hadn’t seen a graph of behavior over time before).

When the tub was full, we made a few guesses about how long it might take to empty, then started the clock and opened the drain. Every ten or twenty seconds, we’d stop the timer, take a depth reading, and plot the result on our graph. After a few tries, the kids could place the points. About half way, we took a longer pause to discuss the trajectory so far. I proposed a few forecasts of how the second half of the tub might drain – slowing, speeding up, etc. Each of us took a guess about time-to-empty. Naturally my own guess was roughly consistent with exponential decay. Then we reopened the drain and collected data until the tub was dry.

To my astonishment, the resulting plot showed a perfectly linear decline in water depth, all the way to zero (as best we could measure). In hindsight, it’s not all that strange, because the tub tapers at the bottom, so that a constant linear decline in the outflow rate corresponds with the declining volumetric flow rate you’d expect (from decreasing pressure at the outlet as the water gets shallower). Still, I find it rather amazing that the shape of the tub (and perhaps nonlinearity in the drain’s behavior) results in such a perfectly linear trajectory.

We spent a fair amount of time further exploring bathtub dynamics, with much filling and emptying. When the quantity of water on the floor got too alarming, we moved to the sink to explore equilibrium by trying to balance the tap inflow and drain outflow, which is surprisingly difficult.

We lost track of our original results, so we recently repeated the experiment. This time, we measured the filling as well as the draining, shown below on the same axes. The dotted lines are our data; others are our prior guesses. Again, there’s no sign of exponential draining – it’s a linear rush to the finish line. Filling – which you’d expect to be a perfect ramp if the tub had constant volume per depth – is initially fast, then slows slightly as the tapered bottom area is full. However, that effect doesn’t seem to be big enough to explain the outflow behavior.

Bathtub data

I’ve just realized that I have a straight-sided horse trough lying about, so I think we may need to head outside for another test …

Update: the follow-on to this is rather important.

Policy Resistance in Emerging Markets

A great example of policy undone by feedback, from Paul Krugman’s column, The Widening Gyre:

The really shocking thing, however, is the way the crisis is spreading to emerging markets ’” countries like Russia, Korea and Brazil.

These countries were at the core of the last global financial crisis, in the late 1990s (which seemed like a big deal at the time, but was a day at the beach compared with what we’re going through now). They responded to that experience by building up huge war chests of dollars and euros, which were supposed to protect them in the event of any future emergency. And not long ago everyone was talking about ‘decoupling,’ the supposed ability of emerging market economies to keep growing even if the United States fell into recession. ‘Decoupling is no myth,’ The Economist assured its readers back in March. ‘Indeed, it may yet save the world economy.’

That was then. Now the emerging markets are in big trouble. In fact, says Stephen Jen, the chief currency economist at Morgan Stanley, the ‘hard landing’ in emerging markets may become the ‘second epicenter’ of the global crisis. (U.S. financial markets were the first.)

What happened? In the 1990s, emerging market governments were vulnerable because they had made a habit of borrowing abroad; when the inflow of dollars dried up, they were pushed to the brink. Since then they have been careful to borrow mainly in domestic markets, while building up lots of dollar reserves. But all their caution was undone by the private sector’s obliviousness to risk.

In Russia, for example, banks and corporations rushed to borrow abroad, because dollar interest rates were lower than ruble rates. So while the Russian government was accumulating an impressive hoard of foreign exchange, Russian corporations and banks were running up equally impressive foreign debts. Now their credit lines have been cut off, and they’re in desperate straits.

The unstated closure to the loop is that emerging market governments’ borrowing in domestic markets and hoarding of foreign exchange were likely a cause of higher domestic rate spreads over dollar rates, and thus contributed to the undoing of the policy by driving other borrowing abroad.

Risk Communication on Climate

John Sterman’s new Policy Forum in Science should be required reading. An excerpt:

The strong scientific consensus on the causes and risks of climate change stands in stark contrast to widespread confusion and complacency among the public. Why does this gulf exist, and why does it matter? Policies to manage complex natural and technical systems should be based on the best available scientific knowledge, and the Intergovernmental Panel on Climate Change (IPCC) provides rigorously vetted information to policy-makers. In democracies, however, the beliefs of the public, not only those of experts, affect government policy.

Effective risk communication is grounded in deep understanding of the mental models of policy-makers and citizens. What, then, are the principal mental models shaping people’s beliefs about climate change? Studies show an apparent contradiction: Majorities in the United States and other nations have heard of climate change and say they support action to address it, yet climate change ranks far behind the economy, war, and terrorism among people’s greatest concerns, and large majorities oppose policies that would cut greenhouse gas (GHG) emissions by raising fossil fuel prices.

More telling, a 2007 survey found a majority of U.S. respondents (54%) advocated a “wait-and-see” or “go slow” approach to emissions reductions. Larger majorities favored wait-and-see or go slow in Russia, China, and India. For most people, uncertainty about the risks of climate change means costly actions to reduce emissions should be deferred; if climate change begins to harm the economy, mitigation policies can then be implemented. However, long delays in the climate’s response to anthropogenic forcing mean such reasoning is erroneous.

Wait-and-see works well in simple systems with short lags. We can wait until the teakettle whistles before removing it from the flame because there is little lag between the boil, the whistle, and our response. Similarly, wait-and-see would be a prudent response to climate change if there were short delays in the response of the climate system to intervention. However, there are substantial delays in every link of a long causal chain stretching from the implementation of emissions abatement policies to emissions reductions to changes in atmospheric GHG concentrations to surface warming to changes in ice sheets, sea level, agricultural productivity, extinction rates, and other impacts. Mitigating the risks therefore requires emissions reductions long before additional harm is evident. Wait-and-see policies implicitly presume the climate is roughly a first-order linear system with a short time constant, rather than a complex dynamical system with long delays, multiple positive feedbacks, and nonlinearities that may cause abrupt, costly, and irreversible regime changes.

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Forrester on the Financial Crisis

Jay Forrester writes on the SD email list:

First, the current crisis did not start with the burst housing bubble. It started with the excessive credit that led to the housing bubble. That excess credit resulted from the Federal Reserve holding down interest rates to less than the inflation rate in housing. This negative real interest rate (bank interest minus inflation in the housing assets) produced a powerful incentive for investment and speculation in housing. And the action of the Federal Reserve, with the increase in risk taking by banks, were a result of pressure from Congress and the public who were all enjoying the short-term rise in housing prices.

We see here one of the characteristics of a complex social system in which a policy that is good in the short run is almost always bad in the long run. Feeding the bubble with easy credit was popular in the short run but now we have the consequent day of reckoning with the collapse of the financial system.

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Climate, the Bailout, and the Blame Game

I’ve been watching a variety of explanations of the financial crisis. As a wise friend noticed, the only thing in short supply is acceptance of responsibility. I’ve seen theories that place the seminal event as far back as the Carter administration. Does that make sense, causally?

In a formal sense, it might in some cases. I could have inhaled a carcinogen a decade ago that only leads to cancer a decade from now, without any outside triggers. But I think that sort of system is a rarity. As a practical matter, we have to look elsewhere.

Socioeconomic systems are at a constant slow boil, with many potential threats existing below the threshold of imminent danger at any given time. Occasionally, one grows exponentially and emerges as a real catastrophe. It seems like a surprise, because of the hockey stick behavior of growth (the French riddle of the lily pond again). However, most apparent low-level threats never emerge from the noise. They don’t have enough gain to grow fast, or they get shut down by some unsuspected negative feedback.

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There's always something more pressing …

One reason long-term environmental issues like climate change are so hard to solve is that there’s always something else to do that seems more immediately pressing. War? Energy crisis? Financial meltdown? Those grab headlines, leaving the long-term problems for the slow news days:

Google Trends

In this case, I don’t think slow and steady wins the race. The financial sector gets a trillion dollars in one year, and climate policy gets the Copenhagen Consensus.

Continue reading “There's always something more pressing …”

Jay Forrester honored in POMS

Production and Operations Management 17(4) honors Jay Forrester as an important person in the history of operations management. He joins Kenneth Arrow, Ronald Coase, and William Cooper in the distinction this year. Congratulations Jay!

Hat tip to John Sterman on the SD email list. I don’t have fulltext access to the journal, but if someone sends me a snippet, I’ll post it.

How To Fix A Carbon Tax

Imagine that you and I live in a place that has just implemented a carbon tax. I, being a little greener than you, complain that the tax isn’t high enough, in that it’s not causing emissions to stabilize or fall. As a remedy, I propose the following:

At intervals, a board will set targets for emissions, and announce them in advance for the next three years.
On a daily basis, the board will review current emissions to see if they’re on track to meet the annual target.
The daily review will take account of such things as expectations about growth, the business cycle, weather (as it affects electric power and heating demand), and changing fuel prices.
Based on its review, the board will post a daily value for the carbon tax, to ensure that the target is met.

Sound crazy? Welcome to cap and trade. The only difference is that the board’s daily review is distributed via a market. The presence of a market doesn’t change the fact that emissions trading has its gains backwards: rapid adjustment of prices to achieve an emissions target that can only be modified infrequently (the latter due to the need to set stable quantity expectations). Willingness to set a cap at a level below whatever a tax achieves is equivalent to accepting a higher price of carbon. Why not just raise the tax, and have lower transaction costs, broader sector coverage, and less volatility to boot?

Certainly cap and trade is a viable second-best policy, especially if augmented with a safety valve or a variable-quantity auction providing some supply-side elasticity. However, I find the scenario playing out in BC quite bizarre.

Update: more detailed thoughts on taxes and trading in this article.

Tangible Models

MIT researchers have developed a cool digital drawing board that simulates the physics of simple systems:

You can play with something like this with Crayon Physics or Magic Pen. Digital physics works because the laws involved are fairly simple, though the math behind one of these simulations might appear daunting. More importantly, they are well understood and universally agreed upon (except perhaps among perpetual motion advocates).

I’d like to have the equivalent of the digital drawing board for the public policy and business strategy space: a fluid, intuitive tool that translates assumptions into realistic consequences. The challenge is that there is no general agreement on the rules by which organizations and societies work. Frequently there is not even a clear problem statement and common definition of important variables.

However, in most domains, it is possible to identify and simulate the “physics” of a social system in a useful way. The task is particularly straightforward in cases where the social system is managing an underlying physical system that obeys predictable laws (e.g., if there’s no soup on the shelf, you can’t sell any soup). Jim Hines and MIT Media Lab researchers translated that opportunity into a digital whiteboard for supply chains, using a TUI (tangible user interface). Here’s a demonstration:

There are actually two innovations here. First, the structure of a supply chain has been reduced to a set of abstractions (inventories, connections via shipment and order flows, etc.) that make it possible to assemble one tinker-toy style using simple objects on the board. These abstractions eliminate some of the grunt work of specifying the structure of a system, enabling what Jim calls “modeling at conversation speed”. Second, assumptions, like the target stock or inventory coverage at a node in the supply chain, are tied to controls (wheels) that allow the user to vary them and see the consequences in real time (as with Vensim’s Synthesim). Getting the simulation off a single computer screen and into a tangible work environment opens up great opportunities for collaborative exploration and design of systems. Cool.

Next step: create tangible, shareable, fast tools for uncertain dynamic tasks like managing the social security trust fund or climate policy.