Complex Systems Made Simple

February 19, 2013

Just as the name implies, com­plex sys­tems are dif­fi­cult to tease apart. An organism’s genome, a bio­chem­ical reac­tion, or even a social net­work all con­tain many inter­de­pen­dent components—and changing any one of them can have per­va­sive effects on all the others. In the case of a very large system, like the human genome, which con­tains 20,000 inter­con­nected genes, it’s impos­sible to mon­itor the whole system at once.

But that may not matter any­more. In a paper pub­lished in the pres­ti­gious mul­ti­dis­ci­pli­nary journal Pro­ceed­ings of the National Academy of Sci­ence, North­eastern net­work sci­en­tists have devel­oped an algo­rithm capable of iden­ti­fying the subset of components—or nodes—that are nec­es­sary to reveal a com­plex system’s overall nature.

The approach takes advan­tage of the inter­de­pen­dent nature of com­plexity to devise a method for observing sys­tems that are oth­er­wise beyond quan­ti­ta­tive scrutiny.

“Con­nect­ed­ness is the essence of com­plex sys­tems,” said Albert-​​László Barabási, one of the paper’s authors and a Dis­tin­guished Pro­fessor of Physics with joint appoint­ments in biology and the Col­lege of Com­puter and Infor­ma­tion Sci­ence. “Thanks to the links between com­po­nents, infor­ma­tion is dis­trib­uted throughout a net­work. Hence I do not need to mon­itor everyone to have a full sense of what the system does.”

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