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Open AccessArticle

Multiscale Information Theory and the Marginal Utility of Information

1
Department of Mathematics, Emmanuel College, Boston, MA 02115, USA
2
Program for Evolutionary Dynamics, Harvard University, Cambridge, MA 02138, USA
3
Department of Physics, University of Massachusetts-Boston, Boston, MA 02125, USA
4
New England Complex Systems Institute, Cambridge, MA 02139, USA
*
Author to whom correspondence should be addressed.
Entropy 2017, 19(6), 273; https://doi.org/10.3390/e19060273
Received: 28 February 2017 / Revised: 26 May 2017 / Accepted: 9 June 2017 / Published: 13 June 2017
(This article belongs to the Special Issue Complexity, Criticality and Computation (C³))
Complex systems display behavior at a range of scales. Large-scale behaviors can emerge from the correlated or dependent behavior of individual small-scale components. To capture this observation in a rigorous and general way, we introduce a formalism for multiscale information theory. Dependent behavior among system components results in overlapping or shared information. A system’s structure is revealed in the sharing of information across the system’s dependencies, each of which has an associated scale. Counting information according to its scale yields the quantity of scale-weighted information, which is conserved when a system is reorganized. In the interest of flexibility we allow information to be quantified using any function that satisfies two basic axioms. Shannon information and vector space dimension are examples. We discuss two quantitative indices that summarize system structure: an existing index, the complexity profile, and a new index, the marginal utility of information. Using simple examples, we show how these indices capture the multiscale structure of complex systems in a quantitative way. View Full-Text
Keywords: complexity; complex systems; entropy; information; scale complexity; complex systems; entropy; information; scale
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Allen, B.; Stacey, B.C.; Bar-Yam, Y. Multiscale Information Theory and the Marginal Utility of Information. Entropy 2017, 19, 273.

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