Don't just do something, stand there! Reflections on the counterintuitive behavior of complex systems, seen through the eyes of System Dynamics, Systems Thinking and simulation.
Change management is one of the great challenges in modeling projects. I don’t mean this in the usual sense of getting people to change on the basis of model results. That’s always a challenge, but there’s another.
Over the course of a project, the numerical results and maybe even the policy conclusions given by a model are going to change. This is how we learn from models. If the results don’t change, either we knew the answer from the outset (a perception that should raise lots of red flags), or the model isn’t improving.
The problem is that model consumers are likely to get anchored to the preliminary results of the work, and resist change when it arrives later in the form of graphs that look different or insights that contradict early, tentative conclusions.
Fortunately, there are remedies:
Start with the assumption that the model and the data are wrong, and to some extent will always remain so.
Recognize that the modeler is not the font of all wisdom.
Emphasize extreme conditions tests and reality checks throughout the modeling process, not just at the end, so bugs don’t get baked in while insights remain hidden.
Do lots of sensitivity analysis to determine the circumstances under which insights are valid.
Keep the model simpler than you think it needs to be, so that you have some hope of understanding it, and time for reflecting on behavior and communicating results.
Involve a broad team of model consumers, and set appropriate expectations about what the model will be and do from the start.
All of us, even if we have no knack for science, look at the weather, at our children, at our markets, at the sky, and we see rhythms and patterns that seem to repeat, that give us the ability to predict. …
Do any of us live beyond pattern? …
I don’t think so. Artists may be, oddly, the most pattern-aware. Case in point: The totally unpredictable, one-of-a-kind novelist Kurt Vonnegut … once gave a lecture in which he presented — in graphic form — the basic plots of all the world’s great stories. Every story you’ve ever heard, he said, are reflections of a few, classic story shapes. They are so elementary, he said, he could draw them on an X/Y axis.
“With some help from wedges, the world decided that dealing with global warming wasn’t impossible, so it must be easy,” Socolow says. “There was a whole lot of simplification, that this is no big deal.”
Socolow’s strong rebuke of the misuse of his work is a welcome contribution and, perhaps optimistically, marks a positive step forward in the climate debate.
I spoke to Socolow today at length, and he stands behind every word of that — including the carefully-worded title. Indeed, if Socolow were king, he told me, he’d start deploying some 8 wedges immediately. A wedge is a strategy and/or technology that over a period of a few decades ultimately reduces projected global carbon emissions by one billion metric tons per year (see Princeton website here). Socolow told me we “need a rising CO2 price” that gets to a serious level in 10 years. What is serious? “$50 to $100 a ton of CO2.”
Revkin weighs in with a broader view, but the tone is a bit Pielkeish,
From the get-go, I worried about the gushy nature of the word “solving,” particularly given that there was then, and remains, no way to solve the climate problem by 2050.
1. Look closely at what is in quotes, which generally comes from my slides, and what is not in quotes. What is not in quotes is just enough “off” in several places to result in my messages being misconstrued. I have given a similar talk about ten times, starting in December 2010, and this is the first time that I am aware of that anyone in the audience so misunderstood me. I see three places where what is being attributed to me is “off.”
a. “It was a mistake, he now says.” Steve Pacala’s and my wedges paper was not a mistake. It made a useful contribution to the conversation of the day. Recall that we wrote it at a time when the dominant message from the Bush Administration was that there were no available tools to deal adequately with climate change. I have repeated maybe a thousand times what I heard Spencer Abraham, Secretary of Energy, say to a large audience in Alexandria. Virginia, early in 2004. Paraphrasing, “it will take a discovery akin to the discovery of electricity” to deal with climate change. Our paper said we had the tools to get started, indeed the tools to “solve the climate problem for the next 50 years,” which our paper defined as achieving emissions 50 years from now no greater than today. I felt then and feel now that this is the right target for a world effort. I don’t disown any aspect of the wedges paper.
b. “The wedges paper made people relax.” I do not recognize this thought. My point is that the wedges people made some people conclude, not surprisingly, that if we could achieve X, we could surely achieve more than X. Specifically, in language developed after our paper, the path we laid out (constant emissions for 50 years, emissions at stabilization levels after a second 50 years) was associated with “3 degrees,” and there was broad commitment to “2 degrees,” which was identified with an emissions rate of only half the current one in 50 years. In language that may be excessively colorful, I called this being “outflanked.” But no one that I know of became relaxed when they absorbed the wedges message.
c. “Well-?intentioned groups misused the wedges theory.” I don’t recognize this thought. I myself contributed the Figure that accompanied Bill McKibben’s article in National Geographic that showed 12 wedges (seven wedges had grown to eight to keep emissions level, because of emissions growth post-?2006 and the final four wedges drove emissions to half their current levels), to enlist the wedges image on behalf of a discussion of a two-?degree future. I am not aware of anyone misusing the theory.
2. I did say “The job went from impossible to easy.” I said (on the same slide) that “psychologists are not surprised,” invoking cognitive dissonance. All of us are more comfortable with believing that any given job is impossible or easy than hard. I then go on to say that the job is hard. I think almost everyone knows that. Every wedge was and is a monumental undertaking. The political discourse tends not to go there.
3. I did say that there was and still is a widely held belief that the entire job of dealing with climate change over the next 50 years can be accomplished with energy efficiency and renewables. I don’t share this belief. The fossil fuel industries are formidable competitors. One of the points of Steve’s and my wedges paper was that we would need contributions from many of the available option. Our paper was a call for dialog among antagonists. We specifically identified CO2 capture and storage as a central element in climate strategy, in large part because it represents a way of aligning the interests of the fossil fuel industries with the objective of climate change.
…
It is distressing to see so much animus among people who have common goals. The message of Steve’s and my wedges paper was, above all, ecumenical.
My take? It’s rather pointless to argue the merits of 7 or 14 or 25 wedges. We don’t really know the answer in any detail. Do a little, learn, do some more. Socolow’s $50 to $100 a ton would be a good start.
this
three
a. “It
It
time
available
thousand
audience
akin
the
tools
to
get
started,
indeed
the
tools
to
“solve
the
climate
problem
for
the
next
50
years,”
than
disown
any
aspect
of
the
wedges
paper.
b. “The
wedges
paper
made
people
relax.”
I
do
not
recognize
this
thought.
My
point
is
that
the
wedges
people
made
some
people
conclude,
not
surprisingly,
that
if
we
could
achieve
after
our
paper,
the
path
we
laid
out
(constant
emissions
for
50
years,
emissions
at
stabilization
was
only
half
the
current
one
in
50
years.
In
language
that
may
be
excessively
colorful,
I
called
this
being
“outflanked.”
But
no
one
that
I
know
of
became
relaxed
when
they
absorbed
the
wedges
message.
c.
“Well-?intentioned
myself
contributed
the
Figure
that
accompanied
Bill
McKibben’s
article
in
National
I just rediscovered the Carnegie Mellon EIO-LCA tool, an online model for input-output lifecycle analysis. I ran it for the “Electronic computer manufacturing” sector to see how the results compare with Apple’s lifecycle analysis of my new MacBook.
The result: 284 tons CO2eq per million dollars of output. That translates to 340 kg for a $1200 computer. This is almost the same as Apple’s number, except that the Apple figure includes lifecycle emissions from use, for about a third of the total, so Apple’s manufacturing emissions are about a third lower than the generic computer sector in the EIO-LCA tool.
Directionally, it’s interesting that Apple’s estimate (presumably a process-based accounting) is lower, given that manufacturing happens in China, where electricity and GDP are both carbon-intensive on average. I wouldn’t read too much into the differences without digging much deeper though.
We are pleased to announce the launch of the 2011 Climate CoLab Contest. This year, the question that the CoLab poses is:
How should the 21st century economy evolve bearing in mind the reality of climate change?
This year’s contest will feature two competition pools:
Global, whose proposals outline how a feature of the world economy should evolve,
Regional/national, whose proposals outline how a feature of a regional or national economy should evolve.
The contest will run for six months from May 16 to November 15. Winners will be selected based on voting by community members and review by the judges.
The winning teams will present their proposals at briefings at the United Nations in New York City and U.S. Congress in Washington, D.C. The Climate CoLab will sponsor one representative from each of the winning teams.
We encourage you to form teams with other CoLab members who share your regional or global interests. Fill out your profile and start debating and brainstorming. If you would like to join a team, please send me a message.
I just discovered the Harvard Natural Sciences Lecture Demonstrations – a catalog of ways to learn and play with science. It’s all fun, but a few of the videos provide nice demonstrations of dynamic phenomena.
Here’s a pretty array of pendulums of different lengths and therefore different natural frequencies:
This is a nice demonstration of how structure (length) causes behavior (period of oscillation). You can also see a variety of interesting behavior patterns, like beats, as the oscillations move in and out of phase with one another.
Synchronized metronomes:
These metronomes move in and out of sync as they’re coupled and uncoupled. This is interesting because it’s a fundamentally nonlinear process. Syncprovides a nice account of such things, and there’s a nifty interactive coupled pendulum demo here.
Mousetrap fission:
This is a physical analog of an infection model or the Bass diffusion model. It illustrates shifting loop dominance – initially, positive feedback dominates due to the chain reaction of balls tripping new traps, ejecting more balls. After a while, negative feedback takes over as the number of live traps is depleted, and the reaction slows.
There were three surprised when I recently ordered an Apple Macbook Pro. The first was how good the industrial design is compared to any PC laptop I’ve had. The second was getting a FedEx tracking number – straight from Shanghai. The third was how big the carbon footprint of this svelte machine is.
Here it is, perched on a massive granite stair that took prybars, Egyptian pyramid-building techniques, and considerable sweat to place (not to mention the negative contribution to my kids’ vocabulary). The two bigger blocks: about 370kg (over 800 pounds). The Mac’s lifecycle carbon footprint: 350kg (2/3 manufacturing & transport, 1/3 use).
Grist covers a detailed report on the rebound effect, which recently appeared at ElectricityPolicy.com (pdf from NRDC). The report discusses a wide range of rebound arguments, basically concluding that rebounds are not a big deal.
Some of the reasons derive from the microeconomic effects of efficiency improvements. For example, improving the efficiency of light bulbs makes light services cheaper. But user’s don’t immediately increase lighting in proportion to the cost reduction, because their demand for lighting is saturated: there are only so many fixtures in a house, hours in the day requiring light, etc. Similarly, the elasticity of dirty dish production with respect to the energy cost of running a dishwasher is pretty darn low. This is reminiscent of the dynamics of process improvement at Analog Devices, where TQM improved productivity, but the company had a hard time translating that to expansion of its market niche in the short term.
I think the report underweights the long term effects of efficiency though. Efficiency increases contribute to aggregate productivity growth in the economy (more than you’d expect, if you believe that agency problems and other market failures create a bias toward overuse of energy). With wealth comes an expansion of energy use, hence the boom in such energy hogs as undercounter freezers and wine chillers, countering Energy Star improvement in refrigeration. However, this is not really an efficiency problem; it’s a progress problem, and it brings welfare benefits along with the added energy (at least until you get to the absurd margin).
The report cites an Energy Policy survey of empirical estimates:
Improvements in energy efficiency make energy services cheaper, and therefore encourage increased consumption of those services. This so-called direct rebound effect offsets the energy savings that may otherwise be achieved. This paper provides an overview of the theoretical and methodological issues relevant to estimating the direct rebound effect and summarises the empirical estimates that are currently available. The paper focuses entirely on household energy services, since this is where most of the evidence lies and points to a number of potential sources of bias that may lead the effect to be overestimated. For household energy services in the OECD, the paper concludes that the direct rebound effect should generally be less than 30%. doi:10.1016/j.enpol.2008.11.026
Sadly, a press release for related studies from the same research group spins this as a catastrophe:
‘Rebound Effects’ Threaten Success of UK Climate Policy
This is really only a catastrophe for a politician foolish enough to try to set and hit a hard emissions target, with efficiency mandates as the only measure for achieving it. As soon as you have any course correction (i.e. negative feedback) built into your policies, like an adaptive carbon tax or cap & trade system (the latter being the less stable option), the catastrophe goes away. The real catastrophe is failing to price GHG emissions and other externalities due to misperceptions about efficiency.
The real bottom line for rebound effects should be, “who cares?” If rebound effects are large, efficiency programs have small energy effects, but potentially large welfare improvements (if you accept that there are energy market failures tending towards overconsumption), and emissions pricing has large energy effects, because high rebound implies high price elasticity. If rebound effects are small, efficiency programs work and emissions pricing is a good way to collect taxes. Neither condition is a reason to avoid efficiency or emissions pricing, though emissions pricing is the preferable way to proceed.
Eli Pariser argues that “filter bubbles” are bad for us and bad for democracy:
As web companies strive to tailor their services (including news and search results) to our personal tastes, there’s a dangerous unintended consequence: We get trapped in a “filter bubble” and don’t get exposed to information that could challenge or broaden our worldview.
Filter bubbles are close cousins of confirmation bias, groupthink, polarization and other cognitive and social pathologies.
As confidence in an idea grows, the delay in recognition (or frequency of outright rejection) of anomalous information grows larger. As a result, confidence in the idea – flat earth, 100mpg carburetor – can grow far beyond the level that would be considered reasonable, if contradictory information were recognized.
The dynamics resulting from this and other positive feedbacks play out in many spheres. Wittenberg & Sterman give an example:
The dynamics generated by the model resemble the life cycle of intellectual fads. Often a promising new idea rapidly becomes fashionable through excessive optimism, aggressive marketing, media hype, and popularization by gurus. Many times the rapid influx of poorly trained practitioners, or the lack of established protocols and methods, causes expectations to outrun achievements, leading to a backlash and disaffection. Such fads are commonplace, especially in (quack) medicine and most particularly in the world of business, where “new paradigms” are routinely touted in the pages of popular journals of management, only to be displaced in the next issue by what many business people have cynically come to call the next “flavor of the month.”
Typically, a guru proposes a new theory, tool, or process promising to address persistent problems facing businesses (that is, a new paradigm claiming to solve the anomalies that have undermined the old paradigm.) The early adopters of the guru’s method spread the word and initiate some projects. Even in cases where the ideas of the guru have little merit, the energy and enthusiasm a team can bring to bear on a problem, coupled with Hawthorne and placebo effects and the existence of “low hanging fruit” will often lead to some successes, both real and apparent. Proponents rapidly attribute these successes to the use of the guru’s ideas. Positive word of mouth then leads to additional adoption of the guru’s ideas. (Of course, failures are covered up and explained away; as in science there is the occasional fraud as well.) Media attention further spreads the word about the apparent successes, further boosting the credibility and prestige of the guru and stimulating additional adoption.
As people become increasingly convinced that the guru’s ideas work, they are less and less likely to seek or attend to disconfirming evidence. Management gurus and their followers, like many scientists, develop strong personal, professional, and financial stakes in the success of their theories, and are tempted to selectively present favorable and suppress unfavorable data, just as scientists grow increasingly unable to recognize anomalies as their familiarity with and confidence in their paradigm grows. Positive feedback processes dominate the dynamics, leading to rapid adoption of those new ideas lucky enough to gain a sufficient initial following. …
The wide range of positive feedbacks identified above can lead to the swift and broad diffusion of an idea with little intrinsic merit because the negative feedbacks that might reveal that the tools don’t work operate with very long delays compared to the positive loops generating the growth. …
For filter bubbles, I think the key positive loops are as follows:
Loops R1 are the user’s well-worn path. We preferentially visit sites presenting information (theory x or y) in which we have confidence. In doing so, we consider only a subset of all information, building our confidence in the visited theory. This is a built-in part of our psychology, and to some extent a necessary part of the process of winnowing the world’s information fire hose down to a usable stream.
Loops R2involve the information providers. When we visit a site, advertisers and other observers (Nielsen) notice, and this provides the resources (ad revenue) and motivation to create more content supporting theory x or y. This has also been a part of the information marketplace for a long time.
R1 and R2 are stabilized by some balancing loops (not shown). Users get bored with an all-theory-y diet, and seek variety. Providers seek out controversy (real or imagined) and sensationalize x-vs-y battles. As Pariser points out, there’s less scope for the positive loops to play out in an environment with a few broad media outlets, like city newspapers. The front page of the Bozeman Daily Chronicle has to work for a wide variety of readers. If the paper let the positive loops run rampant, it would quickly lose half its readership. In the online world, with information customized at the individual level, there’s no such constraint.
Individual filtering introduces R3. As the filter observes site visit patterns, and preferentially serves up information consistent with past preferences. This introduces a third set of reinforcing feedback processes, as users begin to see what they prefer, they also learn to prefer what they see. In addition, on Facebook and other social networking sites every person is essentially a site, and people include one another in networks preferentially. This is another mechanism implementing loop R1 – birds of a feather flock together and share information consistent with their mutual preferences, and potentially following one another down conceptual rabbit holes.
The result of the social web and algorithmic filtering is to upset the existing balance of positive and negative feedback. The question is, were things better before, or are they better now?
I’m not exactly sure how to tell. Presumably one could observe trends in political polarization and duration of fads for an indication of the direction of change, but that still leaves open the question of whether we have more or less than the “optimal” quantity of pet rocks, anti-vaccine campaigns and climate skepticism.
My suspicion is that we now have too much positive feedback. This is consistent with Wittenberg & Sterman’s insight from the modeling exercise, that the positive loops are fast, while the negative loops are weak or delayed. They offer a prescription for that,
The results of our model suggest that the long-term success of new theories can be enhanced by slowing the positive feedback processes, such as word of mouth, marketing, media hype, and extravagant claims of efficacy by which new theories can grow, and strengthening the processes of theory articulation and testing, which can enhance learning and puzzle-solving capability.
In the video, Pariser implores the content aggregators to carefully ponder the consequences of filtering. I think that also implies more negative feedback in algorithms. It’s not clear that providers have an incentive to do that though. The positive loops tend to reward individuals for successful filtering, while the risks (e.g., catastrophic groupthink) accrue partly to society. At the same time, it’s hard to imagine a regulatory that does not flirt with censorship.
Absent a global fix, I think it’s incumbent on individuals to practice good mental hygiene, by seeking diverse information that stands some chance of refuting their preconceptions once in a while. If enough individuals demand transparency in filtering, as Pariser suggests, it may even be possible to gain some local control over the positive loops we participate in.
I’m not sure that goes far enough though. We need tools that serve the social equivalent of “strengthening the processes of theory articulation and testing” to improve our ability to think and talk about complex systems. One such attempt is the “collective intelligence” behind Climate Colab. It’s not quite Facebook-scale yet, but it’s a start. Semantic web initiatives are starting to help by organizing detailed data, but we’re a long way from having a “behavioral dynamic web” that translates structure into predictions of behavior in a shareable way.
