Election Reflection

Jay Forrester’s 1971 Counter Intuitive Behavior of Social Systems sums up this election pretty well for me.

… social systems are inherently insensitive to most policy changes that people choose in an effort to alter the behavior of systems. In fact, social systems draw attention to the very points at which an attempt to intervene will fail. Human intuition develops from exposure to simple systems. In simple systems, the cause of a trouble is close in both time and space to symptoms of the trouble. If one touches a hot stove, the burn occurs here and now; the cause is obvious. However, in complex dynamic systems, causes are often far removed in both time and space from the symptoms. True causes may lie far back in time and arise from an entirely different part of the system from when and where the symptoms occur. However, the complex system can mislead in devious ways by presenting an apparent cause that meets the expectations derived from simple systems. A person will observe what appear to be causes that lie close to the symptoms in both time and space—shortly before in time and close to the symptoms. However, the apparent causes are usually coincident occurrences that, like the trouble symptom itself, are being produced by the feedback-loop dynamics of a larger system.

Translation: economy collapses under a Republican administration. Democrats fail to fix it, partly for lack of knowledge of correct action but primarily because it’s unfixable on a two-year time scale. Voters who elected the Dems by a large margin forget the origins of the problem, become dissatisfied and throw the bums out, but replace them with more clueless bums.

… social systems seem to have a few sensitive influence points through which behavior can be changed. These high-influence points are not where most people expect. Furthermore, when a high-influence policy is identified, the chances are great that a person guided by intuition and judgment will alter the system in the wrong direction.

Translation: everyone suddenly becomes a deficit hawk at the worst possible time, even though they don’t know whether Obama is a Keynesian.

The root of the problem:

Mental models are fuzzy, incomplete, and imprecisely stated. Furthermore, within a single individual, mental models change with time, even during the flow of a single conversation. The human mind assembles a few relationships to fit the context of a discussion. As debate shifts, so do the mental models. Even when only a single topic is being discussed, each participant in a conversation employs a different mental model to interpret the subject. Fundamental assumptions differ but are never brought into the open. Goals are different but left unstated.

It is little wonder that compromise takes so long. And even when consensus is reached, the underlying assumptions may be fallacies that lead to laws and programs that fail.

Still,

… there is hope. It is now possible to gain a better understanding of dynamic behavior in social systems. Progress will be slow. There are many cross-currents in the social sciences which will cause confusion and delay. … If we proceed expeditiously but thoughtfully, there is a basis for optimism.

Now cap & trade is REALLY dead

From the WaPo:

[Obama] also virtually abandoned his legislation – hopelessly stalled in the Senate – featuring economic incentives to reduce carbon emissions from power plants, vehicles and other sources.

“I’m going to be looking for other means of addressing this problem,” he said. “Cap and trade was just one way of skinning the cat,” he said, strongly implying there will be others.

In the campaign, Republicans slammed the bill as a “national energy tax” and jobs killer, and numerous Democrats sought to emphasize their opposition to the measure during their own re-election races.

Brookings reflects, Toles nails it.

Modelers: you're not competing

Well, maybe a little, but it doesn’t help.

From time to time we at Ventana encounter consulting engagements where the problem space is already occupied by other models. Typically, these are big, detailed models from academic or national lab teams who’ve been working on them for a long time. For example, in an aerospace project we ran into detailed point-to-point trip generation models and airspace management simulations with every known airport and aircraft in them. They were good, but cumbersome and expensive to run. Our job was to take a top-down look at the big picture, integrating the knowledge from the big but narrow models. At first there was a lot of resistance to our intrusion, because we consumed some of the budget, until it became evident that the existence of the top-down model added value to the bottom-up models by placing them in context, making their results more relevant. The benefit was mutual, because the bottom-up models provided grounding for our model that otherwise would have been very difficult to establish. I can’t quite say that we became one big happy family, but we certainly developed a productive working relationship.

I think situations involving complementary models are more common than head-to-head competition among models that serve the same purpose. Even where head-to-head competition does exist, it’s healthy to have multiple models, especially if they embody different methods. (The trouble with global climate policy is that we have many models that mostly embody the same general equilibrium assumptions, and thus differ only in detail.) Rather than getting into methodological pissing matches, modelers should be seeking the synergy among their efforts and making it known to decision makers. That helps to grow the pie for all modeling efforts, and produces better decisions.

Certainly there are exceptions. I once ran across a competing vendor doing marketing science for a big consumer products company. We were baffled by the high R^2 values they were reporting (.92 to .98), so we reverse engineered their model from the data and some slides (easy, because it was a linear regression). It turned out that the great fits were due to the use of 52 independent parameters to capture seasonal variation on a weekly basis. Since there were only 3 years of data (i.e. 3 points per parameter), we dubbed that the “variance eraser.” Replacing the 52 parameters with a few targeted at holidays and broad variations resulted in more realistic fits, and also revealed problems with inverted signs (presumably due to collinearity) and other typical pathologies. That model deserved to be displaced. Still, we learned something from it: when we looked cross-sectionally at several variants for different products, we discovered that coefficients describing the sales response to advertising were dependent on the scale of the product line, consistent with our prior assertion that effects of marketing and other activities were multiplicative, not additive.

The reality is that the need for models is almost unlimited.  The physical sciences are fairly well formalized, but models span a discouragingly small fraction of the scope of human behavior and institutions. We need to get the cost of providing insight down, not restrict the supply through infighting. The real enemy is seldom other models, but rather superstition, guesswork and propaganda.

There must be a model here somewhere

I ran across a nice interpretation of Paul Krugman’s comments on China’s monetary policy. It’s also a great example of the limitations of verbal descriptions of complex feedbacks:

In order to invest in China you need state permission and the state limits how much money comes in. It essentially has an import quota on Yuan.

This means that while Yuan are loose in the international market and therefore cheap, they are actually tight at home and therefore expensive. Because China is controlling the flow on money across the border it can have a loose international monetary policy but a tight domestic monetary policy.

Indeed, it goes deeper than that. A loose international Yuan bids up foreign demand for Chinese goods. This in turn both increase the quantity of goods China produces and their domestic price. Essentially, foreign consumers are given a price advantage relative to domestic consumers.

However, China doesn’t want domestic consumers to face higher prices. So, it has to tighten the domestic Yuan even tighter. It has too push down domestic demand so that the sum of international demand plus domestic demand are not so high that they produce domestic inflation.

The tight domestic Yuan, therefore, is driving down Chinese consumption at precisely the time in which the world could use more consumption. The loose international Yuan also gives foreigners a price advantage when buying Chinese goods and so it is driving down inflation in the US at precisely the time the Fed is trying to dive it up.

However, the story still gets worse from there – I am really riffing here, half of this is just occurring to me as I type. The loose international Yuan can only be used to produce manufactured goods. Manufacturing requires commodities both as the feed stock for the actual goods and to be used in the construction of new manufacturing facilities.

What does that mean. It should mean that when the Fed loosens policy, that China responds by loosening the International Yuan which in turn gets shunted towards commodities. Thus rather than boosting the consumer price level as we hope, Fed easing actually winds up boosting commodities.

This is because China is offsetting the total increase in worldwide consumer demand by tightening the Yuan at home, and boosting the total increase in commodity demand by loosening the Yuan abroad.

If this is a bit baffling, it helps to get the context from the originals. Still, it begs for a model or at least a diagram. At least the punch line is simple:

Thus this Yuan policy does all the wrong things.

Meanwhile, in a bizarre parallel universe where climate policy exists in a vacuum, China calls the US a preening pig. Couldn’t they at least wait for Palin to be elected? Seriously, US climate policy is a joke, but Chinese monetary-industrial policy is just as destructive.

Ben Franklin, systems thinker

I find that many great thinkers are systems thinkers, even if they don’t use the lingo of feedback. Here’s a great example, in which Ben Franklin anticipates the American revolution, describing forces that could bring it about:

TO THE COMMITTEE OF CORRESPONDENCE IN MASSACHUSETTS

London, May 15, 1771.

GENTLEMEN,

I have received your favour of the 27th of February, with the journal of the House of Representatives, and copies of the late oppressive prosecutions in the Admiralty Court, which I shall, as you direct, communicate to Mr. Bollan, and consult with him on the most advantageous use to be made of them for the interest of the province.

I think one may clearly see, in the system of customs [import taxes] to be exacted in America by act of Parliament, the seeds sown of a total disunion of the two countries, though, as yet, that event may be at a considerable distance. The course and natural progress seems to be, first, the appointment of needy men as officers, for others do not care to leave England; then, their necessities make them rapacious, their office makes them proud and insolent, their insolence and rapacity make them odious, and, being conscious that they are hated, they become malicious; their malice urges them to a continual abuse of the inhabitants in their letters to administration, representing them as disaffected and rebellious, and (to encourage the use of severity) as weak, divided, timid, and cowardly. Government believes all; thinks it necessary to support and countenance its officers; their quarrelling with the people is deemed a mark and consequence of their fidelity; they are therefore more highly rewarded, and this makes their conduct still more insolent and provoking.

The resentment of the people will, at times and on particular incidents, burst into outrages and violence upon such officers, and this naturally draws down severity and acts of further oppression from hence. The more the people are dissatisfied, the more rigor will be thought necessary; severe punishments will be inflicted to terrify; rights and privileges will be abolished; greater force will then be required to secure execution and submission; the expense will become enormous; it will then be thought proper, by fresh exactions, to make the people defray it; thence, the British nation and government will become odious, the subjection to it will be deemed no longer tolerable; war ensues, and the bloody struggle will end in absolute slavery to America, or ruin to Britain by the loss of her colonies; the latter most probable, from America’s growing strength and magnitude.

….

I do not pretend to the gift of prophecy. History shows, that, by these steps, great empires have crumbled heretofore; and the late transactions we have so much cause to complain of show, that we are in the same train, and that, without a greater share of prudence and wisdom, than we have seen both sides to be possessed of, we shall probably come to the same conclusion….

With great esteem and respect, I have the honour to be, &c.

B. FRANKLIN.

This translates readily into a rich causal loop diagram (click the image to enlarge):

Franklin anticipates the revolution

My CLD here is basically a direct translation of the letter. That makes it sound a little more like a cycle of events, and less like interaction of quantities that can vary, than I would like. I think it could be refined somewhat by aggregating related concepts and rearranging a few links. For example, war is really just an escalation of violence, so one could simplify by treating the level of violence more generically.

The interesting thing about this diagram is that it’s all positive loops. Presumably the “prudence and wisdom” that Franklin noted would have created negative loops that would have stabilized the situation. What were they?

I bet a lot of the same dynamics are in the DOD Afghanistan counterinsurgency diagram.

Thanks to Dan Proctor for the original letter & idea.

The Vensim CLD is here if you want to play: franklin.mdl

Climate CoLab Contest

The Climate CoLab is an interesting experiment that combines three features,

  • Collaborative simulation modeling (including several integrated assessment models and C-LEARN)
  • On-line debates
  • Collective decision-making

Together these create an infrastructure for collective intelligence that gets beyond the unreal rhetoric that pervades many policy debates.

The CoLab is launching its 2010 round of policy proposal contests:

To members of the Climate CoLab community,

We are pleased to announce the launch of a new Climate CoLab contest, as well as a major upgrade of our software platform.

The contest will address the question: What international climate agreements should the world community make?

The first round runs through October 31 and the final round through November 26.

In early December, the United Nations and U.S. Congress will be briefed on the winning entries.

We are raising funds in the hope of being able to pay travel expenses for one representative from each winning team to attend one or both of these briefings.

We invite you to form teams and enter the contest–learn more at http://climatecolab.org.

We also encourage you to fill out your profiles and add a picture, so that members of the community can get to know each other.

And please inform anyone you believe might be interested about the contest.

Best,

Rob Laubacher

The contest leads to real briefings on the hill, and there are prizes for winners. See details.

Technology first?

The idea of a technology-led solution to climate is gaining ground, most recently with a joint AEI-Brookings proposal. Kristen Sheeran has a nice commentary at RCE on the prospects. Go read it.

I’m definitely bearish on the technology-first idea. I agree that technology investment is a winner, with or without environmental externalities. But for high tech to solve the climate problem by itself, absent any emissions pricing, may require technical discontinuities that are less than likely. That makes technology-first the Hail-Mary pass of climate policy: something you do when you’re out of options.

The world isn’t out of options in a physical sense; it’s just that the public has convinced itself otherwise. That’s a pity.

Out with the bad, in with the good

A while back Obama jumped on the fire-bad-teachers bandwagon:

“You’ve got to have radical change, and radical change is something that’s in the interest of students,” he said. “We’ve got to be able to identify teachers who are doing well. … And, ultimately, if some teachers aren’t doing a good job, they’ve got to go.”

Politico

This is all well and good, but some of what I’ve read about this idea seems naively linear. Bad teachers gone >> students learn more? Just contemplating the stocks and flows gives me pause. If we accelerate the outflow of bad teachers, what happens to the stock of teachers? Does it go down, causing class sizes to go up, inadvertently making things tougher on the remaining good teachers, who might then also leave? If not, where do we get the inflow of replacements, and what makes them any better? Is there an infinite source of potential good teachers out there, waiting to be exploited, or do we have to do something to create it?

Certainly there are some good reasons to think that getting rid of bad teachers is part of the solution. Anecdotal evidence of exceedingly low turnover rates in some districts suggests an opportunity. More importantly, there are positive feedbacks around quality. Good teachers make good colleagues, so a dolt-free school should be able to attract more good teachers. Good teaching reduces inspires, reducing behavior issues, so schools can focus resources on teaching, not discipline.

But at the end of the day, retention of good teachers has to be part of the picture as well. That means caring for them appropriately: giving them the flexibility to develop their own teaching style, not making evaluation obtrusive, providing slack time for development and continuing education, and – god forbid – paying them well. Many other education initiatives run counter to this purpose. For example:

Obama also said “nothing’s more important” than education, and he said if students stayed in class for one more summer month every year, they would retain more information. “I think we should have longer school years,” he said.

This is classic “get a bigger hammer” thinking. Is one more month of school that isn’t working going to help? Are underpaid teachers going to provide 10% more hours on a volunteer basis, or do we cut their effective pay to implement this? Could the resources instead be used to reduce class sizes 10%, or raise salaries 10% to attract better teachers? Again, there may be a kernel of wisdom here, but it’s hard to separate it from its systemic context.

My half-baked view is that it’s unreasonable to expect a revolution in education without providing more resources. That money isn’t going to come from poor school districts. The physics of the distribution of wealth suggests that it would have to come from the rich. At times, the rich have been willing to ante up for education, in recognition that wealth is unsustainable without civil society. But currently we seem to be in a social Darwinian phase, in which wealth is exclusively personal (in stark contrast to the view of achievement in science). So, perhaps the first step would be to make the problem salient: internalize the costs of uncivil society. Let’s pay for policing and the prison system with a luxury tax on McMansions, sports cars, yachts, first class air travel, space tourism, fine art, vintage wine and Viagra. Then we can tackle the really hard stuff, like anti-intellectual culture (since lotteries, our tax on ignorance, don’t seem to be depressing the supply).

The invisible hand works

… but not always with the intended outcome. This collapsed condo in China, built without significant rebar connecting building to footing, is a nice demonstration of the fact that markets are lousy at providing unobserved goods, like safety and quality.

ChinaCondoCollapse

Markets are great at decentralizing decisions where there’s rapid outcome feedback, but lousy at dealing with delayed or temporally remote feedback: pollution, emergent disease resistance, risky lending. As long as markets are incomplete, the invisible hand can’t solve such problems on its own any more than the thermostat in a room can prevent it from getting too hot due to a building fire. In this case, the profit motive probably led to a cascade of bad decisions that actively contributed to the toppling of the building.

There are three possible solutions: 1. regulate the market (government building inspections), 2. create a market (label buildings for rebar content?), or 3.  let a solution emerge (financiers and buyers learn their lesson). 3 is the preferred solution of small-government enthusiasts, but I don’t see any evidence that it actually works for nonlocal problems like pollution or low-probability/high-consequence events. That leaves society holding the bag for the fallout of individual decisions. Possibly that was a good deal for all concerned in the 19th century, but it seems like a dubious approach to the 21st.