The Law of Attraction

No, not that silly one.

Controlling Growth by Controlling Attractiveness

In Woodstock, Vermont, everyone’s mad about a highway. In other places the issue is a sewer system or a school. The real issue, of course, is growth. According to Jay Forrester’s Attractiveness Principle (Forrester is a professor of systems analysis at MIT) there’s only one way to control growth — control attractiveness.

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In a free society if any place is unusually attractive, folks will — no surprise — be attracted there. The most mobile people (the young, the rich, the educated) will get there first. The place will grow until its attractiveness has been reduced by crowded highways, or unemployment, or scarce housing, or pollution, or just plain visual blight. (The most mobile people have moved on by then). When the place is no more attractive than anywhere else, then and only then will it stop growing. What else can stop it?

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The attractiveness of a place is a complex combination of climate, economy, amenities, scenery. No one can define attractiveness exactly, but people make up their minds about it every day by deciding to move from Hartford or Boston or Westchester County to Vermont (that’s the direction they’re moving at the moment). Millions of human judgements weigh Vermont’s clean air against Boston’s job market and Manhattan’s cost of living. The very different mixes of attractiveness and unattractiveness in those places may seem incommensurable, but people make their comparisons, and eventually attractiveness evens out everywhere.

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The normal instinct of public officials, including those of Woodstock, is to fix problems and make their community perfect. The more perfect they make it, the more new people show up. What Woodstock needs to do, Forrester would say, is decide what kinds of imperfection it’s willing to live with.

A crowded, unsafe highway? If that’s unacceptable, then choose something else. Super-restrictive zoning, perhaps, or an absolute limit on new curb cuts, or higher property taxes (I know, they’re already too high, but not high enough to stop people from moving in). Bad schools. Bad air. No jobs. Developments so ugly you might as well live in New Jersey. Some sort of whopping surcharge on those developers. Either Woodstock chooses its form of unattractiveness, or the growth process itself chooses.

It takes awhile to absorb the implications of the Attractiveness Principle, because it turns conventional thinking upside down (Forrester is good at doing that). Its implications are not good news for the sort of people who live in Woodstock. The Principle says you can’t live in a privileged bubble of attractiveness, unless you are perpetually young, rich, educated, and on the move at the head of the attractiveness wave. It says that growth is your problem wherever it occurs. It says the only way to be sure of living in an attractive place is to be committed to the attractiveness of every place.

From the Donella Meadows Archive

Enabling an R&D addiction

I actually mean that in a good way. A society addicted to learning and innovation would be pretty cool.

However, it’s not all about money. Quoting the OSTP Science of Science Policy Roadmap,

Investment in science and technology, however, is only one of the policy instruments available to science policy makers; others include fostering the role of competiton and openness in the promotion of discovery, the construction of intellectual property systems, tax policy, and investment in a STEM workforce. However, the probable impact of these various policies and interventions is largely unknown. This lack of knowledge can lead to serious and unintended consequences.

In other words, to spend $16 billion/year wisely, you have to get a number of moving parts coordinated, including:

  1. Prices & tax policy. If prices of natural resources, national security, clean air, health, etc. don’t reflect their true values to society, innovation policy will be pushing against the tide. Innovations will be DOA in the marketplace. The need for markets for products is matched by the need for markets for innovators:
  2. Workforce management. Just throwing money at a problem can create big dislocations in researcher demographics. Put it all into academic research, and you create a big glut of graduates who have no viable career path in science. Put it all into higher education, and your pipeline of talent will be starved by poor science preparation at lower levels. Put it all into labs and industry, and it’ll turn into pay raises for a finite pool of workers. Balance is needed.
  3. Intellectual property law. This needs to reflect the right mix of incentives for private investment and recognition that creations are only possible to the extent that we stand on the shoulders of giants and live in a society with rule of law. Currently I suspect that law has swung too far toward eternal protection that actually hinders innovation.

At the end of the day, #1 is most important. Regardless of the productivity of the science enterprise, someone will probably figure out how to make graphene cables or an aspen tree that bears tomatoes. The key question, then, is how society puts those things to use, to solve its problems and improve welfare. That requires a delicate balancing act, between preserving diversity and individual freedom to explore new ways of doing things, and preventing externalities from harming everyone else.

R&D – crack for techno-optimists

I like R&D. Heck, I basically do R&D. But the common argument, that people won’t do anything hard to mitigate emissions or reduce energy use, so we need lots of R&D to find solutions, strikes me as delusional.

The latest example to cross my desk (via the NYT) is the new American Energy Innovation Council’s recommendations,

Create an independent national energy strategy board.
Invest $16 billion per year in clean energy innovation.
Create Centers of Excellence with strong domain expertise.
Fund ARPA-E at $1 billion per year.
Establish and fund a New Energy Challenge Program to build large-scale pilot projects.

Let’s look at the meat of this – $16 billion per year in energy innovation funding. Historic funding looks like this:

R&D funding

Total public energy R&D, compiled from Gallagher, K.S., Sagar, A, Segal, D, de Sa, P, and John P. Holdren, “DOE Budget Authority for Energy Research, Development, and Demonstration Database,” Energy Technology Innovation Project, John F. Kennedy School of Government, Harvard University, 2007. I have a longer series somewhere, but no time to dig it up. Basically, spending was negligible (or not separately accounted for) before WWII, and ramped up rapidly after 1973.

The data above reflects public R&D; when you consider private spending, the jump to $16 billion represents maybe a factor of 3 or 4 increase. What does that do for you?

Consider a typical model of technical progress, the two-factor learning curve:

cost = (cumulative R&D)^A*(cumulative experience)^B

The A factor represents improvement from deliberate R&D, while the B factor reflects improvement from production experience like construction and installation of wind turbines. A and B are often expressed as learning rates, the multiple on cost that occurs per doubling of the relevant cumulative input. In other words, A,B = ln(learning rate)/ln(2). Typical learning rates reported are .6 to .95, or cost reductions of 40% to 5% per doubling, corresponding with A/B values of -.7 to -.15, respectively. Most learning rate estimates are on the high end (smaller reductions per doubling), particularly when the two-factor function is used (as opposed to just one component).

Let’s simplify so that

cost = (cumulative R&D)^A

and use an aggressive R&D learning rate (.7), for A=-0.5. In steady state, with R&D growing at the growth rate of the economy (call it g), cost falls at the rate A*g (because the integral of exponentially growing spending grows at the same rate, and exp(g*t)^A = exp(A*g*t)).

That’s insight number one: a change in R&D allocation has no effect on the steady-state rate of progress in cost. Obviously one could formulate alternative models of technology where that is not true, but compelling argument for this sort of relationship is that the per capita growth rate of GDP has been steady for over 250 years. A technology model with a stronger steady-state spending->cost relationship would grow super-exponentially.

Insight number two is what the multiple in spending (call it M) does get you: a shift in the steady-state growth trajectory to a new, lower-cost path, by M^A. So, for our aggressive parameter, a multiple of 4 as proposed reduces steady-state costs by a factor of about 2. That’s good, but not good enough to make solar compatible with baseload coal electric power soon.

Given historic cumulative public R&D, 3%/year baseline growth in spending, a 0.8 learning rate (a little less aggressive), a quadrupling of R&D spending today produces cost improvements like this:

R&D future 4x

Those are helpful, but not radical. In addition, even if R&D produces something more miraculous than it has historically, there are still big nontechnical lock-in humps to overcome (infrastructure, habits, …). Overcoming those humps is a matter of deployment more than research. The Energy Innovation Council is definitely enthusiastic about deployment, but without internalizing the externalities associated with energy production and use, how is that going to work? You’d either need someone to pick winners and implement them with a mishmash of credits and subsidies, or you’d have to hope for/wait for cleantech solutions to exceed the performance of conventional alternatives.

The latter approach is the “stone age didn’t end because we ran out of stones” argument. It says that cleantech (iron) will only beat conventional (stone) when it’s unequivocally better, not just for the environment, but also convenience, cost, etc. What does that say about the prospects for CCS, which is inherently (thermodynamically) inferior to combustion without capture? The reality is that cleantech is already better, if you account for the social costs associated with energy. If people aren’t willing to internalize those social costs, so be it, but let’s not pretend we’re sure that there’s a magic technical bullet that will yield a good outcome in spite of the resulting perverse incentives.

Gallagher, K.S., Sagar, A, Segal, D, de Sa, P, and John P. Holdren, “DOE Budget Authority for Energy Research, Development, and Demonstration Database,” Energy Technology Innovation Project, John F. Kennedy School of Government, Harvard University, 2007.

A modest proposal for the IPCC

Make it shorter. The Fifth Assessment, that is.

There’s a fairly endless list of suggestions for ways to amend IPCC processes, plus an endless debate over mostly-miniscule improprieties and errors buried in the depths of the report, fueled by the climategate emails.

I find the depth of the report useful personally, but I’m an outlier – how much is really needed? Do any policy makers really read 3000 pages of stuff, every 5 years?

Maybe the better part of valor would be to agree on a page limit – perhaps 350 per working group (the size of the 1990 report), and relegate all the more granular material to a wiki-like lit review and live summary, that could evolve more fluidly.

A shorter report would be easier to edit and read, and less likely to devote ink to details that are fundamentally very uncertain.

Bifurcating Salmon

A nifty paper on nonlinear dynamics of salmon populations caught my eye on ArXiv.org today. The math is straightforward and elegant, so I replicated the model in Vensim.

A three-species model explaining cyclic dominance of pacific salmon

Authors: Christian Guill, Barbara Drossel, Wolfram Just, Eddy Carmack

Abstract: The four-year oscillations of the number of spawning sockeye salmon (Oncorhynchus nerka) that return to their native stream within the Fraser River basin in Canada are a striking example of population oscillations. The period of the oscillation corresponds to the dominant generation time of these fish. Various – not fully convincing – explanations for these oscillations have been proposed, including stochastic influences, depensatory fishing, or genetic effects. Here, we show that the oscillations can be explained as a stable dynamical attractor of the population dynamics, resulting from a strong resonance near a Neimark Sacker bifurcation. This explains not only the long-term persistence of these oscillations, but also reproduces correctly the empirical sequence of salmon abundance within one period of the oscillations. Furthermore, it explains the observation that these oscillations occur only in sockeye stocks originating from large oligotrophic lakes, and that they are usually not observed in salmon species that have a longer generation time.

The paper does a nice job of connecting behavior to structure, and of relating the emergence of oscillations to eigenvalues in the linearized system.

Units balance, though I had to add a couple implicit scale factors to do so.

The general results are qualitatitively replicable. I haven’t tried to precisely reproduce the authors’ bifurcation diagram and other experiments, in part because I couldn’t find a precise specification of numerical methods used (time step, integration method), so I wouldn’t expect to succeed.

Unlike most SD models, this is a hybrid discrete-continuous system. Salmon, predator and zooplankton populations evolve continuously during a growing season, but with discrete transitions between seasons.

The model uses SAMPLE IF TRUE, so you need an advanced version of Vensim to run it, or the free Model Reader. (It should be possible to replace the SAMPLE IF TRUE if an enterprising person wanted a PLE version). It would also be a good candidate for an application of SHIFT IF TRUE if someone wanted to experiment with the cohort age structure.

sockeye.vmf

For a more policy-oriented take on salmon, check out Andy Ford’s work on smolt migration.

Urban Dynamics

This is an updated version of Urban Dynamics, the classic by Forrester et al.

John Richardson upgraded the diagrams and cleaned up a few variable names that had typos.

I added some units equivalents and fixed a few variables in order to resolve existing errors. The model is now free of units errors, except for 7 warnings about use of dimensioned inputs to lookups (not uncommon practice, but it would be good to normalize these to suppress the warnings and make the model parameterization more flexible). There are also some runtime warnings about lookup bounds that I have not investigated (take a look – there could be a good paper lurking here).

Behavior is identical to that of the original from the standard Vensim distribution.

Urban Dynamics 2010-06-14.vpm

Urban Dynamics 2010-06-14.mdl

Urban Dynamics 2010-06-14.vmf

DICE

This is a replication of William Nordhaus’ original DICE model, as described in Managing the Global Commons and a 1992 Science article and Cowles Foundation working paper that preceded it.

There are many good things about this model, but also some bad. If you are thinking of using it as a platform for expansion, read my dissertation first.

Units balance.

I provide several versions:

  1. Model with simple heuristics replacing the time-vector decisions in the original; runs in Vensim PLE
  2. Full model, with decisions implemented as vectors of points over time; requires Vensim Pro or DSS
  3. Same as #2, but with VECTOR LOOKUP replaced with VECTOR ELM MAP; supports earlier versions of Pro or DSS
    • DICE-vec-6-elm.mdl (you’ll also want a copy of DICE-vec-6.vpm above, so that you can extract the supporting optimization control files)

Note that there may be minor variances from the published versions, e.g. that transversality coefficients for the state variables (i.e. terminal values of the states for optimization) are not included. The optimizations use fewer time decision points than the original GAMS equivalents. These do not have any significant effect on the outcome.

Workshop on Modularity and Integration of Climate Models

The MIT Center for Collective Intelligence is organizing a workshop at this year’s Conference on Computational Sustainability entitled “Modularity and Integration of Climate Models.” Check out the  Agenda.

Traditionally, computational models designed to simulate climate change and its associated impacts (climate science models, integrated assessment models, and climate economics models) have been developed as standalone entities. This limits possibilities for collaboration between independent researchers focused on sub-­?problems, and is a barrier to more rapid advances in climate modeling science because work is not distributed effectively across the community. The architecture of these models also precludes running a model with modular sub -­? components located on different physical hardware across a network.

In this workshop, we hope to examine the possibility for widespread development of climate model components that may be developed independently and coupled together at runtime in a “plug and play” fashion. Work on climate models and modeling frameworks that are more modular has begun, (e.g. Kim, et al., 2006) and substantial progress has been made in creating open data standards for climate science models, but many challenges remain.

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A goal of this workshop is to characterize issues like these more precisely, and to brainstorm about approaches to addressing them. Another desirable outcome of this workshop is the creation of an informal working group that is interested in promoting more modular climate model development.