The static reserve life index rears its ugly head in the State of the Union

Published on January 25, 2012 in energy. 0 Comments

The President said, in the State of the Union Address,

We have a supply of natural gas that can last America nearly one hundred years, and my Administration will take every possible action to safely develop this energy.

The 100 year figure presumably comes straight from EIA:

According to the EIA Annual Energy Outlook 2011, the United States possesses 2,543 trillion cubic feet (Tcf) of potential natural gas resources. Natural gas from shale resources, considered uneconomical just a few years ago, accounts for 862 Tcf of this resource estimate. At the 2010 rate of U.S. consumption (about 24.1 Tcf per year), 2,543 Tcf of natural gas is enough to supply over 100 years of use.

100 years is the static reserve life index (SRLI). It’s well known that the SRLI is a misleading metric (this figured prominently in Limits to Growth, for example). Exponential growth in consumption violates the basic assumption of the SRLI, which is that consumption remains constant. Even a small amount of growth greatly erodes the actual lifetime of a resource:

Growth at 3% per year reduces the SRLI of gas from 100 years to a realized lifetime of 45 years, which is not nearly so comfortable. This ought to be intuitive even if you can’t integrate exponentials in your head, because gas consumption would have to roughly double to replace coal (and that doubling would have to happen quickly to meet job claims), so clearly “100 years at current rates” isn’t going to happen.

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Disinfographics

Published on January 25, 2012 in energy and visualization. 0 Comments

I cringed when I saw the awful infographics in a recent GreenBiz report, highlighted in a Climate Progress post. A site that (rightly) criticizes the scientific illiteracy of the GOP field shouldn’t be gushing over chartjunk that would make USA Today blush. Climate Progress dumped my mildly critical comment into eternal moderation queue purgatory, so I have to rant about this a bit.

Here’s one of the graphics, with my overlay of the data plotted correctly (in green):

“What We Found: The energy consumed per dollar of gross domestic product grew slightly in 2010, the first increase after steady declines for more than half a century.”

Notice that:

  • No, there really wasn’t a great cosmic coincidence that caused energy intensity to progress at a uniform rate from 1950-1970 and 1980-2009, despite the impression given by the arrangements of points on the wire.
  • The baseline of the original was apparently some arbitrary nonzero value.
  • The original graphic vastly overstates the importance of the last two data points by using a nonuniform time axis.

The issues are not merely aesthetic; the bad graphics contribute to distorted interpretations of reality, as the caption above indicates. From another graphic (note the short horizon and nonzero baseline), CP extracts the headline, “US carbon intensity is flat lining.”

From any reasonably long sample of the data it should be clear that the 2009-2011 “flat lining” is just a blip, having little to do with the long term emission trends we need to modify to achieve deep emissions reductions.

The other graphics in the article are each equally horrific in their own special way.

My advice to analysts is simple. If you want to communicate information, find someone numerate who’s read Tufte to make your plots. If you must have a pretty picture for eye candy, use it as a light background to an accurate plot. If you want pretty pictures to persuade people without informing them, skip the data and use a picture of a puppy. Here, you can even use my puppy:

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Why learn calculus?

Published on January 19, 2012 in Education and SystemDynamics. 2 Comments

A young friend asked, why bother learning calculus, other than to get into college?

The answer is that calculus holds the keys to the secrets of the universe. If you don’t at least have an intuition for calculus, you’ll have a harder time building things that work (be they machines or organizations), and you’ll be prey to all kinds of crank theories. Of course, there are lots of other ways to go wrong in life too. Be grumpy. Don’t brush your teeth. Hang out in casinos. Wear white shoes after Labor Day. So, all is not lost if you don’t learn calculus. However, the world is less mystifying if you do.

The amazing thing is, calculus works. A couple of years ago, I found my kids busily engaged in a challenge, using a sheet of tinfoil of some fixed size to make a boat that would float as many marbles as possible. They’d managed to get 20 or 30 afloat so far. I surreptitiously went off and wrote down the equation for the volume of a rectangular prism, subject to the constraint that its area not exceed the size of the foil, and used calculus to maximize. They were flabbergasted when I managed to float over a hundred marbles on my first try.

The secrets of the universe come in two flavors. Mathematically, those are integration and differentiation, which are inverses of one another.

Continue reading ‘Why learn calculus?’

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Sandpiles & Systems

Published on January 16, 2012 in Feedback Everywhere. 4 Comments

Sand piles are sometimes used as a counterpoint to systems, where a system is a bunch of interconnected components that interact in some interesting way, while a sand pile is just a bunch of boring stuff. Ironically, sand piles are actually pretty interesting – they self organize. Avalanches regulate the angle of repose of the pile. In aggregate, one can think of this as a negative feedback process – when the pile is too steep, it avalanches, building up the base and lowering the top. There’s more to the picture when you look at it from a disaggregate perspective; the resulting state is an example of self-organized criticality, and if you keep adding to the pile, you get avalanches at all scales (i.e. a power law distribution).

Overnight, nature left me a nice example of a snow pile system on our front stair railing. At some point, the accumulated snow on the handrail partially avalanched, leaving bare wood on its lower half. Evidently the railing is at just the right angle for the ongoing snowfall, fine grains due to the cold, to make a kind of cellular automaton, resulting in this surprisingly regular pattern, reminiscent of a Sierpinski triangle or one of Wolfram’s elementary systems.

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Is social networking making us dumber?

Published on December 19, 2011 in behavior. 0 Comments

Another great conversation at the Edge weaves together a number of themes I’ve been thinking about lately, like scientific revolutions, big data, learning from models, filter bubbles and the balance between content creation and consumption. I can’t embed, or do it full justice, so go watch the video or read the transcript (the latter is a nice rarity these days).

Pagel’s fundamental hypothesis is humans as social animals are wired for imitation more than innovation, for the very good reason that imitation is easy, while innovation is hard, error-prone and sometimes dangerous. Better communication intensifies the advantage to imitators, as it has become incredibly cheap to observe our fellows in large networks like Facebook. There are a variety of implications of this, including the possibility that, more than ever, large companies have strong incentives to imitate through acquisition of small innovators rather than to risk innovating themselves. This resonates very much with Ventana colleague David Peterson’s work on evolutionary simulation of the origins of economic growth and creativity.

Continue reading ‘Is social networking making us dumber?’

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Self-generated Seasonal Cycles

Published on November 25, 2011 in Feedback Everywhere and SystemDynamics. Closed

Why is Black Friday the biggest shopping day of the year? Back in 1961, Jay Forrester identified an endogenous cause in Appendix N of Industrial Dynamics, Self-generated Seasonal Cycles:

Industrial policies adopted in recognition of seasonal sales patterns may often accentuate the very seasonality from which they arise. A seasonal forecast can lead to action that may cause fulfillment of the forecast. In closed-loop systems this is a likely possibility. … any effort toward statistical isolation of a seasonal sales component will find some seasonality in the random disturbances. Should the seasonality so located lead to decisions that create actual seasonality, the process can become self-regenerative.

I think there are actually quite a few reinforcing feedback mechanisms, some of which cross consumer-business stovepipes and therefore are difficult to address.

Before heading to the mall, it’s a good day to think about stuff.

Update: another interesting take.

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Brinicle

Published on November 25, 2011 in Aside. Closed

This “icy finger of death” is an interesting dynamic system, but I’m just posting it because it’s cool:

Background story here. h/t Stoat. Too bad we Americans won’t see the last episode of this series.

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Visualizing food relationships

Published on November 22, 2011 in visualization. Closed

A recent paper by Chun-Yuen Teng, Yu-Ru Lin & Lada A. Adamic on arXiv explores the network of relationships among ingredients in recipes. That leads to this web of coincident ingredients:

Unfortunately this doesn’t shed any light on whether pizza is really a vegetable.

Chun-Yuen Teng, Yu-Ru Lin, Lada A. Adamic

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Et tu, Groupon?

Published on November 21, 2011 in Models and SystemDynamics. Closed Tags: .

Is Groupon overvalued too? Modeling Groupon actually proved a bit more challenging than my last post on Facebook.

Again, I followed in the footsteps of Cauwels & Sornette, starting with the SEC filing data they used, with an update via google. C&S fit a logistic to Groupon’s cumulative repeat sales. That’s actually the end of a cascade of participation metrics, all of which show logistic growth:

The variable of greatest interest with respect to revenue is Groupons sold. But the others also play a role in determining costs – it takes money to acquire and retain customers. Also, there are actually two populations growing logistically – users and merchants. Growth is presumably a function of the interaction between these two populations. The attractiveness of Groupon to customers depends on having good deals on offer, and the attractiveness to merchants depends on having a large customer pool.

I decided to start with the customer side. The customer supply chain looks something like this:

Subscribers data includes all three stocks, cumulative customers is the right two, and cumulative repeat customers is just the rightmost.

Continue reading ‘Et tu, Groupon?’

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Time to short some social network stocks?

Published on November 16, 2011 in Models and SystemDynamics. 2 Comments

I don’t want to wallow too long in metaphors, so here’s something with a few equations.

A recent arXiv paper by Peter Cauwels and Didier Sornette examines market projections for Facebook and Groupon, and concludes that they’re wildly overvalued.

We present a novel methodology to determine the fundamental value of firms in the social-networking sector based on two ingredients: (i) revenues and profits are inherently linked to its user basis through a direct channel that has no equivalent in other sectors; (ii) the growth of the number of users can be calibrated with standard logistic growth models and allows for reliable extrapolations of the size of the business at long time horizons. We illustrate the methodology with a detailed analysis of facebook, one of the biggest of the social-media giants. There is a clear signature of a change of regime that occurred in 2010 on the growth of the number of users, from a pure exponential behavior (a paradigm for unlimited growth) to a logistic function with asymptotic plateau (a paradigm for growth in competition). [...] According to our methodology, this would imply that facebook would need to increase its profit per user before the IPO by a factor of 3 to 6 in the base case scenario, 2.5 to 5 in the high growth scenario and 1.5 to 3 in the extreme growth scenario in order to meet the current, widespread, high expectations. [...]

I’d argue that the basic approach, fitting a logistic to the customer base growth trajectory and multiplying by expected revenue per customer, is actually pretty ancient by modeling standards. (Most system dynamicists will be familiar with corporate growth models based on the mathematically-equivalent Bass diffusion model, for example.) So the surprise for me here is not the method, but that forecasters aren’t using it.

Looking around at some forecasts, it’s hard to say what forecasters are actually doing. There’s lots of handwaving and blather about multipliers, and little revelation of actual assumptions (unlike the paper). It appears to me that a lot of forecasters are counting on big growth in revenue per user, and not really thinking deeply about the user population at all.

To satisfy my curiosity, I grabbed the data out of Cauwels & Sornette, updated it with the latest user count and revenue projection, and repeated the logistic model analysis. A few observations:

I used a generalized logistic, which has one more parameter, capturing possible nonlinearity in the decline of the growth rate of users with increasing saturation of the market. Here’s the core model:

Continue reading ‘Time to short some social network stocks?’

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