James Gleick: What Defines A Meme?

Smithsonian Magazine has posted a fantastic excerpt from James Gleick's new book, The Information: A History, a Theory, a Flood. In this piece, he presents a popular yet fresh introduction to the concept of the "meme," crediting Richard Dawkins, of course, but also going back years before The Selfish Gene, to French biologist and Nobel laureate Jacques Monod, who said that ideas have "spreading power." "Ideas cause ideas and help evolve new ideas. They interact with each other and with other mental forces in the same brain, in neighboring brains, and thanks to global communication, in far distant, foreign brains. And they also interact with the external surroundings to produce in toto a burstwise advance in evolution that is far beyond anything to hit the evolutionary scene yet." He continues with several fascinating examples of memes (see video above) and lands on Twitter as a powerful meme incubator. From Smithsonian:

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Inspired by a chance conversation on a hike in the Hong Kong mountains, information scientists Charles H. Bennett from IBM in New York and Ming Li and Bin Ma from Ontario, Canada, began an analysis of a set of chain letters collected during the photocopier era. They had 33, all variants of a single letter, with mutations in the form of misspellings, omissions and transposed words and phrases. “These letters have passed from host to host, mutating and evolving,” they reported in 2003.

Like a gene, their average length is about 2,000 characters. Like a potent virus, the letter threatens to kill you and induces you to pass it on to your “friends and associates”–some variation of this letter has probably reached millions of people. Like an inheritable trait, it promises benefits for you and the people you pass it on to. Like genomes, chain letters undergo natural selection and sometimes parts even get transferred between coexisting “species.”

Reaching beyond these appealing metaphors, the three researchers set out to use the letters as a “test bed” for algorithms used in evolutionary biology. The algorithms were designed to take the genomes of various modern creatures and work backward, by inference and deduction, to reconstruct their phylogeny–their evolutionary trees. If these mathematical methods worked with genes, the scientists suggested, they should work with chain letters, too. In both cases the researchers were able to verify mutation rates and relatedness measures.

Still, most of the elements of culture change and blur too easily to qualify as stable replicators. They are rarely as neatly fixed as a sequence of DNA. Dawkins himself emphasized that he had never imagined founding anything like a new science of memetics. A peer-reviewed Journal of Memetics came to life in 1997–published online, naturally–and then faded away after eight years partly spent in self-conscious debate over status, mission and terminology. Even compared with genes, memes are hard to mathematize or even to define rigorously. So the gene-meme analogy causes uneasiness and the genetics-memetics analogy even more.

"What Defines a Meme?" (Smithsonian)

"The Information: A History, a Theory, a Flood" by James Gleick (Amazon)