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Today's a holiday in Ontario, the mid-point of our brief summer.
So here are some Complexity posts for your enjoyment
FRSC INSNA Founder University of Toronto
http://www.chass.utoronto.ca/~wellman twitter: @barrywellman
NETWORKED:The New Social Operating System. Lee Rainie & Barry Wellman
MIT Press http://amzn.to/zXZg39 Print $14 Kindle $9
In this paper, we propose, discuss, and illustrate a computationally feasible definition of chaos which can be applied very generally to situations that are commonly encountered, including attractors, repellers, and non-periodically forced systems. This definition is based on an entropy-like quantity, which we call ˙˙expansion entropy,˙˙ and we define chaos as occurring when this quantity is positive. We relate and compare expansion entropy to the well-known concept of topological entropy to which it is equivalent under appropriate conditions. We also present example illustrations, discuss computational implementations, and point out issues arising from attempts at giving definitions of chaos that are not entropy-based.
Brian R. Hunt and Edward Ott
Chaos 25, 097618 (2015); http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=3793fc8124&e=55e25a0e3e
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What Can Interaction Webs Tell Us About Species Roles?
Ecological interactions are highly diverse even when considering a single species: the species might feed on a first, disperse the seeds of a second, and pollinate a third. Here we extend the group model, a method for identifying broad patterns of interaction across a food web, to networks which contain multiple types of interactions. Using this new method, we ask whether the traditional approach of building a network for each type of interaction (food webs for consumption, pollination webs, seed-dispersal webs, host-parasite webs) can be improved by merging all interaction types in a single network. In particular, we test whether combining different interaction types leads to a better definition of the roles species play in ecological communities. We find that, although having more information necessarily leads to better results, the improvement is only incremental if the linked species remain unchanged. However, including a new interaction type that attaches new species to
the network substantially improves performance. This method provides insight into possible implications of merging different types of interactions and allows for the study of coarse-grained structure in any signed network, including ecological interaction webs, gene regulation networks, and social networks.
Sander EL, Wootton JT, Allesina S (2015) What Can Interaction Webs Tell Us About Species Roles? PLoS Comput Biol 11(7): e1004330. http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=e6275eb9a7&e=55e25a0e3e
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Urban Transfer Entropy across Scales
The morphology of urban agglomeration is studied here in the context of information exchange between different spatio-temporal scales. Urban migration to and from cities is characterised as non-random and following non-random pathways. Cities are multidimensional non-linear phenomena, so understanding the relationships and connectivity between scales is important in determining how the interplay of local/regional urban policies may affect the distribution of urban settlements. In order to quantify these relationships, we follow an information theoretic approach using the concept of Transfer Entropy. Our analysis is based on a stochastic urban fractal model, which mimics urban growing settlements and migration waves. The results indicate how different policies could affect urban morphology in terms of the information generated across geographical scales.
Murcio R, Morphet R, Gershenson C, Batty M (2015) Urban Transfer Entropy across Scales. PLoS ONE 10(7): e0133780. http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=9bce14ea15&e=55e25a0e3e
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Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.
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