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.
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.
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 pondagain). 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.
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:
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.
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.
The NYT reports that a switch to efficient cars is underway, as evidenced by, among other things, an increase in market share for small cars from an eighth of the market at the height of SUV-mania to a fifth today, together with a sharp drop in large truck and SUV sales.
Ezzati said, “The finding that 4% of the male population and 19% of the female population experienced either decline or stagnation in mortality is a major public health concern.” Christopher Murray, Director of the Institute for Health Metrics and Evaluation at the University of Washington and co-author of the study, added that “life expectancy decline is something that has traditionally been considered a sign that the health and social systems have failed, as has been the case in parts of Africa and Eastern Europe. The fact that is happening to a large number of Americans should be a sign that the U.S. health system needs serious rethinking.”
I question whether it’s just the health system that requires rethinking. Health is part of a complex system of income and wealth, education, and lifestyle choices:
As an experiment, I’ve created a wiki for dynamic models. I’m gradually migrating my existing model library into the wiki, in the hope that it will be easier to maintain and more useful for visitors. I’ve also created a public model library section so that users can submit new material. Check it out!