Category Archives: natural-world

Bedroom Cities

My town of about 40K people is a bedroom community; it’s population shrinks by about a third each day as people head off to work in other parts of the Boston metro area. When the railroad into this town the commuting ran the other way – farm workers who lived in the urban center and came out on the train to work the fields.

Urban cores typically draw workers into them during the day. Boston’s population rises by 41% each day, more than most. A very few larges cities in the US shrink, anti-cities? Not a hubs of economic activity but instead of hubs of housing. In Virginia Beach the population shrinks by 12%, in Mesa City, AZ it shrinks by 10%. In San Jose, CA it shrinks by 6%, and in Long Beach, CA it drops 4%. Detroit manages to just break even. Washington DC grows by 72%; and Boston swells 41%.

I’m amused by the idea that San Jose and the surrounding area, silicon valley, might become the first inside out metro-donut. A dense vibrant urban residential core built in the vertical with a surrounded the flat 1-2 story industrial landscape, so popular in the valley.

Data from US Census by way of Infectious Greed.

Your Child Recliner Has Grape Jam on It!. Seller Financing.

Jealous of all the press bird flu’s been getting the blog-o-sphere ecology has evolved splogs. They are causing congestion of my pubsub queries. Today’s title: Your Child Recliner Has Grape Jam on It!. Seller Financing.. Many of my pubsub queries are coughing up a steady phlegm of splogs, bu it could easly get worse. Just remember the great spam plague of 1998; millions of email addresses died!

Lets toss two terms into the pot. “Dominant design” and “convergent evolution” into the pot. In part because I want to drop them into ye olde blog. But mostly because the provide an entry point to I want to get off my chest about bird flu.

Dominant design is a term of art among the folks who think about innovation. Dominant designs emerge in design spaces as innovators progressively “mine-out” the options in the design space. Once the dominant designs emerge it becomes possible for complementary activities to gather around them – i.e. they create new design spaces. I like the term because it avoids calling these the best designs. The emergence of dominant designs is extremely contextual and path dependent. Owning the dominant design, being the early into that part of the design space, and encouraging the emergance of the compilmentary stuff is all part and parcel of the gold rush in and around one of these design spaces. Careful though. It is rare tha a single dominant design emerges from a design space; more typically a bloom of designs emerges. How skewed the user’s adoption of these designs turns out varies. There are, for example, a handful of dominant designs for operating systems. Typical power-law stuff.

Covergent evolution is the name given a pattern observed in nature where two very similar species (or organs) evolve in widely seperate environments. The design of the two species converges, presumably, because the two niches place analagous pressures on their evolution. Examples of convergently evolved species can be quite disconcerting. When traveling in Ireland many years ago I found it bizzare how some of the birds would behave almost identically along the forest edge to those in New England. If they had behaved exactly as the birds at home that wouldn’t have bothered me; but in Ireland they would dart in slightly different patterns. Which would trigger for me a startle reaction. These days I often experiance something analagous when talking to people from differing developer communities. For example both Apache and Wikipedia appear to have covergently evolved some community norms the might be filed under “convival.” Or, for example most commercial developer networks begin with a focus on developers (aka hackers) but then there emerges a parrallel network that nurtures the relationship with the folks who market the products built on the platform.

Dominant design is a way to think about the shape of systems over time. You can expect to find dominant designs in any reasonably system (market, institution, ecology, whatever). You can expect to see conflict about what designs will be dominant in systems that haven’t stablized. Users attempt to time their choices about when to adopt depending on these signals about how stable things are; and the appearance of dominant designs is just such a signal.

Convergent evolution is a way to think about where to go to get ideas. Similar ecologies can be expected to be rich sources for patterns that are likely to work in your home community. While gene splicing design patterns is easier than teaching Irish birds to fly right it’s still real work. It often fails because it’s easly to over simplify and presume that a the other system is like your own when on closer examination that presumption would fall appart.

Ok, so about that bird flu. The standard story template in the media about bird flu is to tell the story of the 1918 flu pandemic. The fear that nature’s random number generator will mutate the current flu into a form that is virilent in humans. This fear plays on our intuitions about both dominant design and convergent evolution. Do either of these make sense?

We know that systems tend to settle down with one or more dominant designs settling out. But I don’t see how that is a reasonable expectation in this case. Two forces drive things toward a dominant design: fitness and complements. While an operating system has complements a flu doesn’t. So is a more virilent flu more fit?

Of course a flu virus doesn’t engage in longterm planning, but the 1918 flu was obviously too virilent for it’s own good. That’s why it went extinct; it burned bright, fast and out.

Our intuition that this flu will converge to the same design pattern as the 1918 flu is all well and good, but it only leads naturally to the next question. What niche did the 1918 flue arise in. What niche encouraged so virilent a beast. There is a good discussion of just that question in Paul Ewald book.

He argues there that it is entirely possible that the 1918 flu evolved in a very very unusual niche; i.e. the one created by the allied army in support of the trench warfare of the first world war. It wasn’t that the trenchs were a squalid venue; those are common. What was unique about that niche was the tremendously vigorous mixing of flu victums.

To survive a flu virus needs to strike a balance; it must be mild enough to assure it gets passed onto lots of other victums. So the good news is that this tends to temper how virilent a virus of this kind (air infection) becomes. Ewald’s guess about the 1918 virus is that because when a young soldier fell ill with a virus in the trenchs they would toss him in a truck and then bucket brigade him to a series of hospitals. This bucket brigade assured that the virus got to infect a lot of other people. This environment encouraged the virus to evolve toward a more and more virilent strain. First off because it didn’t suffer the negative consequences of high virilence, i.e. lossing the opportunity to infect a large follow on population. Second because high virilence increased the chance the victum would be pulled out of the trenches; giving the virus an impoved chance of infecting a large follow on population.

It’s not hard to see how the hyper dense bird farms of modern agriculture offer an analagous niche for evolving virilent strains of bird flu. Sidebar: the word selfish doesn’t quite seem to be the optimal label for the practice of feeding generic anti-viral drugs to farm animals.

The important question about bird flu in humans is two fold. How far in the flu design space does the existing bird flu need to travel to be virilent in humans; and are we providing a niche that rewards traveling in that direction? We are not running a 19th century European trench war, so that’s good news. Not that I really know anything about the evolutionn of infectious diseases but it appears to me that both the intuitions that drive the fear about bird flu don’t stand up to casual closer examination. Of course, casual isn’t a very good strategy for risk management.

Peak Oil

I’ve been reading The Old Drum.com for a while now. Recently it’s mostly been about the consequences for the hurricanes on the US oil, gasoline, and natural gas supplies. More generally it’s about the hypothesis that the planet’s oil supplies are on the verge of switching from more available every year to less available every year. This will happen. When it happens the current economic system is going to have to change faster than it’s probably able to. Something to throw on the pile of things to worry about along with global warming, bird flu, the US deficit, terrorism, etc. etc.

Today it was pointed out that the site, which is vibrant little community of smart people, is entirely invisible via Google. I’ve never seen that before. It really is invisible. Weird.

In the world of big-things-to-worry-about conspiracy theories are very popular. The Oil Drum used to be at blogspot, maybe Google’s carrying a grudge?

Sixteen Thousand Alligators


The last 10% of this interview on NPR is mighty thought provoking. It’s an interview with the director of a key oil port in Louisiana. As the interviewer is wrapping up the interview she casually asks if he is at home right now. He says yes, but that his house isn’t behind the levee system. His farm has it’s own private levee. Which is over topping, “right now.” He and his brother have 16 thousand alligators, on their farm. Notice the nervous laugh.

Demographic Young

Anything that happened in the first 60% of your life happened when you were young. Your youth expands as you age.

Society labels you, young or old depending, on those around you. If you live with lots of elderly people, say in Florida, you can remain young longer. If you work in the schools you become old faster. More generally the baby boom enables an extended youth for the following generation.

If you wish to feel mature hang with the young. If you wish to feel young hang out with the old. A similar trickworks for wealth, and status.

Rita

update: Note this is no longer current, for current info go to the NHC; note you may need to scroll down to find the tropical storm that your interested in since these days there are often plenty.

Futures

These charts show the price of gasoline, and natural gas respectively.

These show the price to get delivery in October. There are lots of delivery schedules you can purchase commodities on. For example there are contracts called “strips” that will get you a delivery every month for some period of time. The natural gas strip for this winter is currently selling for prices like that shown above.

Looks like it’s going to be expensive to heat the house this winter. Meanwhile, in New England a large chunk of our electricity is generated from natural gas.

Site v.s. Situation

Most excellent article in Slate about New Orleans.

…The river system’s inexorable downstream current swept cotton, grain, sugar, and an array of other commodities to New Orleans’ door. Because of the region’s geography and topography, many 19th-century observers believed that God-working through nature, His favorite medium-would see to it that anyone shrewd enough to build and live in New Orleans would be made rich.

… But it also brought water, wind, and pathogens, elements of a fickle environment that in the past as now turned cruelly chaotic.

Geographers refer to this as the difference between a city’s “situation”-the advantages its location offers relative to other cities-and its “site”-the actual real estate it occupies. New Orleans has a near-perfect situation and an almost unimaginably bad site.

Curiously that is directly analogous to my model of Microsoft Windows.

Reverse Flash Flood


In the southwestern united states they warn you about flash floods in the canyons. Storms dump a few inches of water at someplace upstream and this water is then aggregated into a giant pulse of water that sweeps down thru the canyon your standing in, killing you. The sky is clear and there is very little warning, maybe just a slight increase in the flow before the flood passes thru.

I’ve always been a fan of this realtime stream flow data network the government runs. It allows them to make accurate forecasts for the downstream flooding. The black dots on this chart show the gages pinned to their maxiumum. A number of gages aren’t reporting.

The tree like networks that draw the water out of river basins and down to the sea are made up of billions of links. Each segement of the stream another link. These networks are powerlaw distributed, the mighty rivers at their roots the hubs of thier distribution systems.

On Monday morning I filled the cars with gas, topping them up to capture the last of the gas at last weeks prices. I was actually surprised that none of the gas stations had raised their prices. Gas on the wholesale market in New York was already up and I assumed that station owners would reprice that huge expensive asset each morning. One guy I asked said “Later, we do it around midday.” Another guy said “The boss hasn’t come in yet.”

This morning we were awoken by a sound you don’t hear in the summer. The oil truck was delivering oil across the street. A few minutes ago another oil truck filled the tank of another neighbor. I don’t know if that my neighbor’s topping up, or if it’s their oil guys pushing oil out to their customers so they can, in turn, top up their tanks.

This is an interesting example of the long tail at work. The moment that supply shifts from abundant and dependable to scarce and volitile everybody along the entire distribution system changes their behavior. They address the volitility risk by adding reserves to their storage capacity, but they also shift capital into oil and gas because of the perception that their price will be higher in the future. I.e. it’s a good investment to top up my car’s gas tank or for my neighbors and their oil guy to top up their storage tanks.

This is a facinating example of the long tail at work. If the entire periphery of the distribution system tops up it’s as if the river basin suddenly starts running up hill. The calculations about risk and future values changes for each and every link in the entire distribution chain.