Tag: Networks

Breakpoint: When Bigger is Not Better

Jeff Stibel's book Breakpoint: Why the Web will Implode, Search will be Obsolete, and Everything Else you Need to Know about Technology is in Your Brain is an interesting read. The book is about “understanding what happens after a breakpoint. Breakpoints can't and shouldn't be avoided, but they can be identified.”

What is missing—what everyone is missing—is that the unit of measure for progress isn’t size, it’s time.

In any system continuous growth is impossible. Everything reaches a breakpoint. The real question is how the system responds to this breakpoint. “A successful network has only a small collapse, out of which a stronger network emerges wherein it reaches equilibrium, oscillating around an ideal size.”

The book opens with an interesting example.

In 1944, the United States Coast Guard brought 29 reindeer to St. Matthew Island, located in the Bering Sea just off the coast of Alaska. Reindeer love eating lichen, and the island was covered with it, so the reindeer gorged, grew large, and reproduced exponentially. By 1963, there were over 6,000 reindeer on the island, most of them fatter than those living in natural reindeer habitats.

There were no human inhabitants on St. Matthew Island, but in May 1965 the United States Navy sent an airplane over the island, hoping to photograph the reindeer. There were no reindeer to be found, and the flight crew attributed this to the fact that the pilot didn’t want to fly very low because of the mountainous landscape. What they didn’t realize was that all of the reindeer, save 42 of them, had died. Instead of lichen, the ground was covered with reindeer skeletons.

The network of St. Matthew Island reindeer had collapsed: the result of a population that grew too large and consumed too much. The reindeer crossed a pivotal point , a breakpoint, when they began consuming more lichen than nature could replenish. Lacking any awareness of what was happening to them, they continued to reproduce and consume. The reindeer destroyed their environment and, with it, their ability to survive. Within a few short years, the remaining 42 reindeer were dead. Their collapse was so extreme that for these reindeer there was no recovery.

Jeff Stibel

In the wild, of course, reindeer can move if they run out of lichen, which allows lichen in the area to be replenished before they return.

Nature rarely allows the environment to be pushed so far that it collapses. Ecosystems generally keep life balanced. Plants create enough oxygen for animals to survive, and the animals, in turn, produce carbon dioxide for the plants. In biological terms, ecosystems create homeostasis.

We evolved to reproduce and consume whatever food is available.

Back when our ancestors started climbing down from the trees, this was a good thing: food was scarce so if we found some, the right thing to do was gorge. As we ate more, our brains were able to grow, becoming larger than those of any other primates. This was a very good thing. But brains consume disproportionately large amounts of energy and, as a result, can only grow so big relative to body size. After that point, increased calories are actually harmful. This presents a problem for humanity, sitting at the top of the food pyramid. How do we know when to stop eating? The answer, of course, is that we don’t. People in developed nations are growing alarmingly obese, morbidly so. Yet we continue to create better food sources, better ways to consume more calories with less bite.

Mother Nature won’t help us because this is not an evolutionary issue: most of the problems that result from eating too much happen after we reproduce, at which point we are no longer evolutionarily important. We are on our own with this problem. But that is where our big brains come in. Unlike reindeer, we have enough brainpower to understand the problem, identify the breakpoint, and prevent a collapse.

We all know that physical things have limits. But so do the things we can't see or feel. Knowledge is an example. “Our minds can only digest so much. Sure, knowledge is a good thing. But there is a point at which even knowledge is bad.” This is information overload.

We have been conditioned to believe that bigger is better and this is true across virtually every domain. When we try to build artificial intelligence, we start by shoveling as much information into a computer as possible. Then we stare dumbfounded when the machine can't figure out how to tie its own shoes. When we don't get the results we want, we just add more data. Who doesn't believe that the smartest person is the one with the biggest memory and the most degrees, that the strongest person has the largest muscles, that the most creative person has the most ideas?

Growth is great until it goes too far.

[W]e often destroy our greatest innovations by the constant pursuit of growth. An idea emerges, takes hold, crosses the chasm, hits a tipping point, and then starts a meteoric rise with seemingly limitless potential. But more often than not, it implodes, destroying itself in the process.

Growth isn't bad. It's just not as good as we think.

Nature has a lesson for us if we care to listen: the fittest species are typically the smallest. The tinest insects often outlive the largest lumbering animals. Ants, bees, and cockroaches all outlived the dinosaurs and will likely outlive our race. … The deadliest creature is the mosquito, not the lion. Bigger is rarely better in the long run. What is missing—what everyone is missing—is that the unit of measure for progress isn't size, it's time.

Of course, “The world is a competitive place, and the best way to stomp out potential rivals is to consume all the available resources necessary for survival.”

Otherwise, the risk is that someone else will come along and use those resources to grow and eventually encroach on the ones we need to survive.

Networks rarely approach limits slowly “… they often don't know the carrying capacity of their environments until they've exceeded it. This is a characteristic of limits in general: the only way to recognize a limit is to exceed it. ” This is what happened with MySpace. It grew too quickly. Pages became cluttered and confusing. There was too much information. It “grew too far beyond its breakpoint.”

There is an interesting paradox here though: unless you want to keep small social networks, the best way to keep the site clean is actually to use a filter that prevents you from seeing a lot of information, which creates a filter bubble.

Stibel offers three phases to any successful network.

first, the network grows and grows and grows exponentially; second, the network hits a breakpoint, where it overshoots itself and overgrows to a point where it must decline, either slightly or substantially; finally, the network hits equilibrium and grows only in the cerebral sense, in quality rather than in quantity.

He offers some advice:

Rather than endless growth, the goal should be to grow as quickly as possible—what technologists call hypergrowth—until the breakpoint is reached. Then stop and reap the benefits of scale alongside stability.

Breakpoint goes on to predict the fall of Facebook.

When Networks Network

A great article in Science News by Elizabeth Quill on the connectivity and complexity of networks. A great example of this is the 2008 power station shutdown in Italy that led to the failure of a communications network, causing another power station to go down and “triggering an electrical blackout affecting much of Italy.”

“When we think about a single network in isolation, we are missing so much of the context,” says Raissa D’Souza, a physicist and engineer at the University of California, Davis. “We are going to make predictions that don’t match real systems.”

Like their single-network counterparts, networks of networks show up everywhere. By waking up in the morning, going to work and using your brain, you are connecting networks. Same when you introduce a family member to a friend or send a message on Facebook that you also broadcast via Twitter. In fact, anytime you access the Internet, which is supported by the power grid, which gets its instructions via communications networks, you are relying on interdependent systems. And if your 401(k) lost value during the recent recession, you’re feeling the effects of such systems gone awry.

Findings so far suggest that networks of networks pose risks of catastrophic danger that can exceed the risks in isolated systems. A seemingly benign disruption can generate rippling negative effects. Those effects can cost millions of dollars, or even billions, when stock markets crash, half of India loses power or an Icelandic volcano spews ash into the sky, shutting down air travel and overwhelming hotels and rental car companies. In other cases, failure within a network of networks can mean the difference between a minor disease outbreak or a pandemic, a foiled terrorist attack or one that kills thousands of people.

Understanding these life-and-death scenarios means abandoning some well-established ideas developed from single-network studies. Scientists now know that networks of networks don’t always behave the way single networks do.