Another Look at Limits to Growth

I was just trying to decide whether I believed what I said recently, that the current economic crisis is difficult to attribute to environmental unsustainability. While I was pondering, I ran across this article by Graham Turner on the LtG wiki entry, which formally compares the original Limits runs to history over the last 30+ years. A sample:

Industrial output in Limits to Growth runs vs. history

The report basically finds what I’ve argued before: that history does not discredit Limits.

Setting Up Vensim

I’m trying to adapt to the new tabbed interface in Office 2007. So far, all those pretty buttons seem like a hindrance. Vensim, on the other hand, is a bit too austere. I’ve just installed version 5.9 (check it out, and while you’re at it see the new Ventana site); my setup follows. Note that this only applies to advanced versions of Vensim.

First, I allow the equation editor to “accept enter” – I like to be able to add line breaks to equations (and hate accidentally dismissing the editor with an <enter>). You can do this anyway with <ctl><enter>, but I prefer it this way.

vensim1.png

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Writing an SD Conference Paper

It’s review time for SD conference papers again. As usual, there’s a lot of variance in quality: really good stuff, stuff that isn’t SD, and good ideas imprisoned in a bad presentation. A few thoughts on how to write a good conference paper, in no particular order:

  • Read a bunch of good SD papers, by browsing the SD Review, Dynamica, Desert Island Dynamics, or past conference plenary papers. You could do a lot worse than picking one as a model for your paper.
  • Start with: What’s the question? Why do we care? Who’s the audience? How will they be influenced? What is their prevailing mental model, and how must it change for things to improve? (If your paper is a methods paper, not a model paper, perhaps the relevant questions are different, but it’s still nice to know why I’m reading something up front.)
  • If you have a model,
    • Make sure units balance, stocks and flows are conserved, structure is robust in extreme conditions, and other good practices are followed. When in doubt, refer to Industrial Dynamics or Business Dynamics.
    • Provide a high-level diagram.
    • Describe what’s endogenous, what’s exogenous, and what’s excluded.
    • Provide some basic stats – What’s the time horizon? How many state variables are there?
    • Provide some data on the phenomena in question, or at least reference modes and a dynamic hypothesis.
    • Discuss validation – how do we know your model is any good?
    • Discuss “Which Policy Run is Best, and Who Says So?” (See DID for the reference).
    • Provide the model in supplementary material, if at all possible.
    • Use intelligible and directional variable names.
    • Clearly identify the parameter changes used to generate each run.
    • Change only one thing at a time in your simulation experiments (or more generally, use scientific method).
    • Explore uncertainty.
    • If your output shows interesting dynamics (or weird discontinuities and other artifacts), please explain.
    • Most importantly, clearly explain why things are happening by relating behavior to structure. Black-box output is boring. Causal loop diagrams or simplified stock-flow schematics may be helpful for explaining the structure of interest.
  • If you use CLDs, Read Problems with Causal Loop Diagrams and Guidelines for Drawing Causal Loop Diagrams and Chapter 5 of Business Dynamics.
  • Archetypes are a compact way to communicate a story, but don’t assume that everyone knows them all. Don’t shoehorn your problem into an archetype; if it doesn’t fit, describe the structure/behavior in its own right.
  • If you present graphs, label axes with units, clearly identify each series, etc. Follow general good practice for statistical graphics. I like lots of graphs because they’re information-rich, but each one should have a clear purpose and association with the text. Screenshots straight out of some modeling packages are not presentation-quality in my opinion.
  • I don’t think it’s always necessary to follow the standard scientific journal article format, it could even be boring, but when in doubt it’s not a bad start.
  • If your English is not the best (perhaps even if it is), at least seek help editing your abstract, so that it’s clear and succinct.
  • Ask yourself whether your paper is really about system dynamics. If you have a model, is it dynamic? Is it behavioral? Does it employ an operational description of the system under consideration? If you’re describing a method, is it applicable to (possibly nonlinear) dynamic systems? If you’re describing a process (group modeling, for example), does it involve decision making or inquiry into a dynamic system? I welcome cross-disciplinary papers, but I think pure OR papers (say, optimizing a shop-floor layout) belong at OR conferences.
  • Do a literature search, especially of the SD Review and SD bibliography, but also of literature outside the field, so that you can explain how the model/method relates to past work in SD and to different perspectives elsewhere. Usually it’s not necessary to report all the gory details of other papers though.
  • Can’t think of a topic? Replicate a classic SD model or a model from another field and critique it. See Christian Erik Kampmann, “Replication and revision of a classic system dynamics model: Critique of ‘Population Control Mechanisms’ System Dynamics Review 7(2), 1991. Or try this.
  • Rejected anyway? Don’t feel bad. Try again next year!

If your kids are boring, you're doing it wrong

The other day I ran across a blog post (undeserving of a link, though there is a certain voyeuristic fascination to be had in reading it) that described children as boring little wretches, unsuited to inhabit the cerebral stratosphere of their elders. The mental model seemed to be something like the following:

Bad parenting mental model

The policy response to the misfortune of having children implied by the above is to foist them off on TV and day care until they grow up enough that you can tolerate their presence. That leaves you plenty of time for more intellectual pursuits, like tweeting, or speculating about the romance of the person in the next cubicle.

This reminded me of an earlier perspective on children, now thankfully less prevalent:

Their Hearts naturally, are a meer nest, root, fountain of Sin, and wickedness; an evil Treasure from whence proceed evil things viz. Evil Thoughts. Murders, Adulteries &c. Indeed, as sharers in the guilt of Adam’s first Sin, they’re Children of Wrath by Nature, liable to Eternal Vengeance, the Unquencheable Flames of Hell. – Benjamin Wadsworth

Untitled, Ansel Fiddaman, Pastel

Continue reading “If your kids are boring, you're doing it wrong”

SD on Long Waves, Boom & Bust

Two relevant conversations from the SD email list archive:

Where are we in the long wave?

Bill Harris asks, in 2003,

… should a reasonable person think we are now in the down side of a long wave? That the tough economic times we’ve seen for the past few years will need years to work through, as levels adjust? That simple, short-term economic fixes wont work as they may have in the past? That the concerns we’ve heard about deflation should be seen in a longer context of an entire cycle, not as an isolated event to be overcome? Is there a commonly accepted date for the start of this decline?

Was Bill 5 years ahead of schedule?

Preventing the next boom and bust

Kim Warren asks, in 2001,

This is a puzzle – we take a large fraction of the very brightest and best educated people in the world, put them through 2 years of further intensive education in how business, finance and economics are supposed to work, set them to work in big consulting firms, VCs, and investment banks, pay them highly and supervise them with very experienced and equally bright managers. Yet still we manage to invent quite implausible business ideas, project unsustainable earnings and market performance, and divert huge sums of money and talented people from useful activity into a collective fantasy. Some important questions remain unanswered, like who they are, what they did, how they got away with it, and why the rest of us meekly went along with them? So the challenge to SDers in business is … where is the next bubble coming from, what will it look like, and how can we stop it?

Clearly this is one nut we haven’t cracked.

The Blood-Hungry Spleen

OK, I’ve stolen another title, this time from a favorite kids’ book. This post is really about the thyroid, which is a little less catchy than the spleen.

Your hormones are exciting!
They stir your body up.
They’re made by glands (called endocrine)
and give your body pluck.

Allan Wolf & Greg Clarke, The Blood-Hungry Spleen

A friend has been diagnosed with hypothyroidism, so I did some digging on the workings of the thyroid. A few hours searching citations on PubMed, Medline and google gave me enough material to create this diagram:

Thyroid function and some associated feedbacks

(This is a LARGE image, so click through and zoom in to do it justice.)

The bottom half is the thyroid control system, as it is typically described. The top half strays into the insulin regulation system (borrowed from a classic SD model), body fat regulation, and other areas that seem related. A lot of the causal links above are speculative, and I have little hope of turning the diagram into a running model. Unfortunately, I can’t find anything in the literature that really digs into the dynamics of the system. In fact, I can’t even find the basics – how much stuff is in each stock, and how long does it stay there? There is a core of the system that I hope to get running at some point though:

Thyroid - core regulation and dose titration

(another largish image)

This is the part of the system that’s typically involved in the treatment of hypothyroidism with synthetic hormone replacements. Normally, the body runs a negative feedback loop in which thyroid hormone levels (T3/T4) govern production of TSH, which in turn controls the production of T3 and T4. The problem begins when something (perhaps an autoimmune disease, i.e. Hashimoto’s) diminishes the thyroid’s ability to produce T3 and T4 (reducing the two inflows in the big yellow box at center). Then therapy seeks to replace the natural control loop, by adjusting a dose of synthetic T4 (levothyroxine) until the measured level of TSH (left stock structure) reaches a desired target.

This is a negative feedback loop with fairly long delays, so dosage adjustments are made only at infrequent intervals, in order to allow the system to settle between changes. Otherwise, you’d have the aggressive shower taker problem: water’s to cold, crank up the hot water … ouch, too hot, turn it way down … eek, too cold …. Measurements of T3 and T4 are made, but seldom paid much heed – the TSH level is regarded as the “gold standard.”

This black box approach to control is probably effective for many patients, but it leaves me feeling uneasy about several things. The “normal” range for TSH varies by an order of magnitude; what basis is there for choosing one or the other end of the range as a target? Wouldn’t we expect variation among patients in the appropriate target level? How do we know that TSH levels are a reliable indicator, if they don’t correlate well with T3/T4 levels or symptoms? Are extremely sparse measurements of TSH really robust to variability on various time scales, or is dose titration vulnerable to noise?

One could imagine alternative approaches to control, using direct measurements of T3 and T4, or indirect measurements (symptoms). Those might have the advantage of less delay (fewer confounding states between the goal state and the measured state). But T3/T4 measurements seem to be regarded as unreliable, which might have something to do with the fact that it’s hard to find any information on the scale or dynamics of their reservoirs. Symptoms also take a back seat; one paper even demonstrates fairly convincingly that dosage changes +/- 25% have no effect on symptoms (so why are we doing this again?).

I’d like to have a more systemic understanding of both the internal dynamics of the thyroid regulation system, and its interaction with symptoms, behaviors, and other regulatory systems. Here’s hoping that one of you lurkers (I know you’re out there) can comment with some thoughts or references.


So the spleen doesn’t feel shortchanged, I’ll leave you with another favorite:

Lovely
I think that I ain’t never seen
A poem ugly as a spleen.
A poem that could make you shiver
Like 3.5 … pounds of liver.
A poem to make you lose your lunch,
Tie your intestines in a bunch.
A poem all gray, wet, and swollen,
Like a stomach or a colon.
Something like your kidney, lung,
Pancreas, bladder, even tongue.
Why you turning green, good buddy?
It’s just human body study.

John Scieszka & Lane Smith, Science Verse

Random Excellence – Bailouts, Biases, Boxplots

(A good title, stolen from TOP, and repurposed a bit).

1. A nice graphical depiction of the stimulus package, at the Washington post

2. An interesting JDM article on the independence of cognitive ability and biases, via Marginal Revolution. Abstract:

In 7 different studies, the authors observed that a large number of thinking biases are uncorrelated with cognitive ability. These thinking biases include some of the most classic and well-studied biases in the heuristics and biases literature, including the conjunction effect, framing effects, anchoring effects, outcome bias, base-rate neglect, ‘less is more’ effects, affect biases, omission bias, myside bias, sunk-cost effect, and certainty effects that violate the axioms of expected utility theory. In a further experiment, the authors nonetheless showed that cognitive ability does correlate with the tendency to avoid some rational thinking biases, specifically the tendency to display denominator neglect, probability matching rather than maximizing, belief bias, and matching bias on the 4-card selection task. The authors present a framework for predicting when cognitive ability will and will not correlate with a rational thinking tendency.

The framework alluded to in that last sentence is worth a look. Basically, the explanation hinges on whether subjects have “mindware” available, time resources, and reflexes to trigger an (unbiased) analytical solution when a (biased) heuristic response is unwarranted. This seems to be applicable to dynamic decision making tasks as well: people use heuristics (like pattern matching), because they don’t have the requisite mindware (understanding of dynamics) or triggers (recognition that dynamics matter).

3. A nice monograph on the construction of statistical graphics, via Statisitical Modeling, Causal Inference, and Social Science Update: Bill Harris likes this one too.

Bathtub Still Filling, Despite Slower Inflow

Found this bit, under the headline Carbon Dioxide Levels Rising Despite Economic Downturn:

A leading scientist said on Thursday that atmospheric levels of carbon dioxide are hitting new highs, providing no indication that the world economic downturn is curbing industrial emissions, Reuters reported.

Joe Romm does a good job explaining why conflating emissions with concentrations is a mistake. I’ll just add the visual:

CO2 stock flow structure

And the data to go with it:

CO2 data

It would indeed take quite a downturn to bring the blue (emissions) below the red (uptake), which is what would have to happen to see a dip in the CO2 atmospheric content (green). In fact, the problem is tougher than it looks, because a fall in emissions would be accompanied by a fall in net uptake, due to the behavior of short-term sinks. Notice that atmospheric CO2 kept going up after the 1929 crash. (Interestingly, it levels off from about 1940-1945, but it’s hard to attribute that because it appears to be within natural variability).

At the moment, it’s kind of odd to look for the downturn in the atmosphere when you can observe fossil fuel consumption directly. The official stats do involve some lag, but less than waiting for natural variability to shake out of sparse atmospheric measurements. Things might change soon, though, with the advent of satellite measurements.

Sea Level Rise – VI – The Bottom Line (Almost)

The pretty pictures look rather compelling, but we’re not quite done. A little QC is needed on the results. It turns out that there’s trouble in paradise:

  1. the residuals (modeled vs. measured sea level) are noticeably autocorrelated. That means that the model’s assumed error structure (a white disturbance integrated into sea level, plus white measurement error) doesn’t capture what’s really going on. Either disturbances to sea level are correlated, or sea level measurements are subject to correlated errors, or both.
  2. attempts to estimate the driving noise on sea level (as opposed to specifying it a priori) yield near-zero values.

#1 is not really a surprise; G discusses the sea level error structure at length and explicitly address it through a correlation matrix. (It’s not clear to me how they handle the flip side of the problem, state estimation with correlated driving noise – I think they ignore that.)

#2 might be a consequence of #1, but I haven’t wrapped my head around the result yet. A little experimentation shows the following:

driving noise SD equilibrium sensitivity (a, mm/C) time constant (tau, years) sensitivity (a/tau, mm/yr/C)
~ 0 (1e-12) 94,000 30,000 3.2
1 14,000 4400 3.2
10 1600 420 3.8

Intermediate values yield values consistent with the above. Shorter time constants are consistent with expectations given higher driving noise (in effect, the model is getting estimated over shorter intervals), but the real point is that they’re all long, and all yield about the same sensitivity.

The obvious solution is to augment the model structure to include states representing persistent errors. At the moment, I’m out of time, so I’ll have to just speculate what that might show. Generally, autocorrelation of the errors is going to reduce the power of these results. That is, because there’s less information in the data than meets the eye (because the measurements aren’t fully independent), one will be less able to discriminate among parameters. In this model, I seriously doubt that the fundamental banana-ridge of the payoff surface is going to change. Its sides will be less steep, reflecting the diminished power, but that’s about it.

Assuming I’m right, where does that leave us? Basically, my hypotheses in Part IV were right. The likelihood surface for this model and data doesn’t permit much discrimination among time constants, other than ruling out short ones. R’s very-long-term paleo constraint for a (about 19,500 mm/C) and corresponding long tau is perfectly plausible. If anything, it’s more plausible than the short time constant for G’s Moberg experiment (in spite of a priori reasons to like G’s argument for dominance of short time constants in the transient response). The large variance among G’s experiment (estimated time constants of 208 to 1193 years) is not really surprising, given that large movements along the a/tau axis are possible without degrading fit to data. The one thing I really can’t replicate is G’s high sensitivities (6.3 and 8.2 mm/yr/C for the Moberg and Jones/Mann experiments, respectively). These seem to me to lie well off the a/tau ridgeline.

The conclusion that IPCC WG1 sea level rise is an underestimate is robust. I converted Part V’s random search experiment (using the optimizer) into sensitivity files, permitting Monte Carlo simulations forward to 2100, using the joint a-tau-T0 distribution as input. (See the setup in k-grid-sensi.vsc and k-grid-sensi-4x.vsc for details). I tried it two ways: the 21 points with a deviation of less than 2 in the payoff (corresponding with a 95% confidence interval), and the 94 points corresponding with a deviation of less than 8 (i.e., assuming that fixing the error structure would make things 4x less selective). Sea level in 2100 is distributed as follows:

Sea level distribution in 2100

The sample would have to be bigger to reveal the true distribution (particularly for the “overconfident” version in blue), but the qualitative result is unlikely to change. All runs lie above the IPCC range (.26-.59), which excludes ice dynamics.

Continue reading “Sea Level Rise – VI – The Bottom Line (Almost)”