Complexity_ A Guided Tour - Melanie Mitchell [122]
What do I mean by “spreading information in a network”? Here I’m using the term information to capture any kind of communication among nodes. Some examples of information spreading are the spread of rumors, gossip, fads, opinions, epidemics (in which the communication between people is via germs), electrical currents, Internet packets, neurotransmitters, calories (in the case of food webs), vote counts, and a more general network-spreading phenomenon called “cascading failure.”
The phenomenon of cascading failure emphasizes the need to understand information spreading and how it is affected by network structure. Cascading failure in a network happens as follows: Suppose each node in the network is responsible for performing some task (e.g., transmitting electrical power). If a node fails, its task gets passed on to other nodes. This can result in the other nodes getting overloaded and failing, passing on their task to still other nodes, and so forth. The result is an accelerating domino effect of failures that can bring down the entire network.
Examples of cascading failure are all too common in our networked world. Here are two fairly recent examples that made the national news:
August 2003: A massive power outage hit the Midwestern and Northeastern United States, caused by cascading failure due to a shutdown at one generating plant in Ohio. The reported cause of the shutdown was that electrical lines, overloaded by high demand on a very hot day, sagged too far down and came into contact with overgrown trees, triggering an automatic shutdown of the lines, whose load had to be shifted to other parts of the electrical network, which themselves became overloaded and shut down. This pattern of overloading and subsequent shutdown spread rapidly, eventually resulting in about 50 million customers in the Eastern United States and Canada losing electricity, some for more than three days.
August 2007: The computer system of the U.S. Customs and Border Protection Agency went down for nearly ten hours, resulting in more than 17,000 passengers being stuck in planes sitting on the tarmac at Los Angeles International Airport. The cause turned out to be a malfunction in a single network card on a desktop computer. Its failure quickly caused a cascading failure of other network cards, and within about an hour of the original failure, the entire system shut down. The Customs agency could not process arriving international passengers, some of whom had to wait on airplanes for more than five hours.
A third example shows that cascading failures can also happen when network nodes are not electronic devices but rather corporations.
August–September 1998: Long-Term Capital Management (LTCM), a private financial hedge fund with credit from several large financial firms, lost nearly all of its equity value due to risky investments. The U.S. Federal Reserve feared that this loss would trigger a cascading failure in worldwide financial markets because, in order to cover its debts, LTCM would have to sell off much of its investments, causing prices of stocks and other securities to drop, which would force other companies to sell off their investments, causing a further drop in prices, et cetera. At the end of September 1998, the Federal Reserve acted to prevent such a cascading failure by brokering a bailout of LTCM by its major creditors.
The network resilience I talked about earlier—the ability of networks to maintain short average path lengths in spite of the failure of random nodes—doesn’t take into account the cascading failure scenario in which the failure of one node causes the failure of other nodes. Cascading failures provide another example of “tipping points,” in which small events can trigger accelerating feedback, causing a minor problem to balloon into a major disruption. Although many people worry about malicious threats to our world’s networked infrastructure from hackers or “cyber-terrorists,” it may be that cascading failures pose a much