Complexity & Systems
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Complexity & Systems
Complex systems present problems both in mathematical modelling and philosophical foundations. The study of complex systems represents a new approach to science that investigates how relationships between parts give rise to the collective behaviors of a system and how the system interacts and forms relationships with its environment. The equations from which models of complex systems are developed generally derive from statistical physics, information theory and non-linear dynamics, and represent organized but unpredictable behaviors of natural systems that are considered fundamentally complex.  wikipedia (en)
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Rescooped by Bernard Ryefield from Papers!

Fast and slow thinking -- of networks: The complementary 'elite' and 'wisdom of crowds' of amino acid, neuronal and social networks

Fast and slow thinking -- of networks: The complementary 'elite' and 'wisdom of crowds' of amino acid, neuronal and social networks | Complexity & Systems |

Complex systems may have billion components making consensus formation slow and difficult. Recently several overlapping stories emerged from various disciplines, including protein structures, neuroscience and social networks, showing that fast responses to known stimuli involve a network core of few, strongly connected nodes. In unexpected situations the core may fail to provide a coherent response, thus the stimulus propagates to the periphery of the network. Here the final response is determined by a large number of weakly connected nodes mobilizing the collective memory and opinion, i.e. the slow democracy exercising the 'wisdom of crowds'. This mechanism resembles to Kahneman's "Thinking, Fast and Slow" discriminating fast, pattern-based and slow, contemplative decision making. The generality of the response also shows that democracy is neither only a moral stance nor only a decision making technique, but a very efficient general learning strategy developed by complex systems during evolution. The duality of fast core and slow majority may increase our understanding of metabolic, signaling, ecosystem, swarming or market processes, as well as may help to construct novel methods to explore unusual network responses, deep-learning neural network structures and core-periphery targeting drug design strategies.

 (Illustrative videos can be downloaded from here:this http URL)

Fast and slow thinking -- of networks: The complementary 'elite' and 'wisdom of crowds' of amino acid, neuronal and social networks
Peter Csermely ;

Via Complexity Digest
Complexity Digest's curator insight, November 18, 2015 6:13 PM

See Also: 

António F Fonseca's curator insight, November 23, 2015 3:30 AM

Interesting  paper about fast cores and slow periphery,  conflict in the elite vs democratic consensus.

Marcelo Errera's curator insight, November 24, 2015 11:32 AM

Yes, there must be few fasts and many slows.  It's been predicted by CL in many instances.

Rescooped by Bernard Ryefield from Networks and Graphs!

KONECT - The Koblenz Network Collection

KONECT - The Koblenz Network Collection | Complexity & Systems |

KONECT is the Koblenz Network Collection. KONECT is a project to collect large network datasets of all types in order to perform research in the area of network mining, collected by the Institute of Web Science and Technologies of the University of Koblenz–Landau. KONECT contains over a hundred network datasets of various types.

A network as provided by KONECT is a set of nodes connected by links. An example of a network is a social network: a set of users connected by links which represent friendship relations. A network is represented mathematically by a graph, in which nodes are called vertices and links are called edges.

Most networks are asymmetric: The fact that user A follows user B on the microblogging site Twitter does not imply that user B follows user A. The Twitter graph is thus directed. In the DBLP authorship network, scientific publications are connected to their authors. The DBLP publication network thus has two classes of nodes; it is bipartite.

KONECT provides:

Code to generate all network datasets from the Web
Statistics and plots viewable online
Download of selected datasets (where legally possible)

To be added in the future:

Analysis code to generate all statistics and plots



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Scooped by Bernard Ryefield!

Economics 2.0: The Natural Step towards a Self-Regulating, Participatory Market Society

source :

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Scooped by Bernard Ryefield!

Dynamical Systems on Networks: A Tutorial

We give a tutorial for the study of dynamical systems on networks, and we focus in particular on ``simple" situations that are tractable analytically. We briefly motivate why examining dynamical systems on networks is interesting and important. We then give several fascinating examples and discuss some theoretical results. We also discuss dynamical systems on dynamical (i.e., time-dependent) networks, overview software implementations, and give our outlook on the field.

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Rescooped by Bernard Ryefield from CASR3PM!

Networked for complexity

Networked for complexity | Complexity & Systems |

In the 21st century, then, the industrial era has given way to the social era, and it is time to rethink both how we work and how we organise ourselves to do so. 

Via Kenneth Mikkelsen, Christophe Bredillet
Kenneth Mikkelsen's curator insight, November 25, 2013 5:39 PM

Terrific blog post by Richard Martin. You should follow Richard on [url=/u/2565370 x-already-notified=1]Richard Martin[/url] and on Twitter here

Stephen Dale's curator insight, November 27, 2013 5:01 AM

I picked out this abstract which resonated with me..." a company is like ‘a social network of productive relationships in which stakeholders are deployed where they are of greatest use. It is designed as a flow of input that can come from anywhere in the network. The work is asynchronous in time and place, and people contribute whatever expertise they have, irrespective of rank or experience." 

Great post.