Analyzing information sharing among the parts of a system can help explain its behaviors on different scales
Science News, 22 September 2014
It’s hard to find simple scientific principles from which to deduce all the multifaceted things that such complex systems do. But there is, for sure, one thing that they do all have in common. They all have a structure. And it’s by quantifying structure, three scientists suggest in an intriguing new paper, that complexity can be tamed.
. . . . . . .
Supposedly, information theory provides the math for quantifying relationships among the parts of a system. “An information measure indicates how many questions one needs answered to remove uncertainty about the system components under consideration,” Allen (of Harvard), Stacey (Brandeis University) and Bar-Yam (New England Complex Systems Institute) point out. But traditionally, information measures have ignored the scale at which a system’s behavior is operating. Systems exhibit different behaviors on different scales. Only by incorporating the importance of scale can structure and complexity be properly accounted for, Allen, Stacey and Bar-Yam aver. An effective approach “requires an understanding of information theory in a multiscale context, a context that has not been developed in information theory nor in the statistical physics of phase transitions.”
Scale considerations are also often absent in the network approach, which emphasizes pairwise relationships (links) between two parts of a system. Network math can be tweaked to accommodate how pairwise links influence behaviors at higher scales, but it misses relationships that are intrinsically large-scale to begin with.
Working out the math to take scale more fully into account is at the core of Allen, Stacey and Bar-Yam’s new approach to coping with structure.