Complexity Economics
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Rescooped by EconStreams.com from Dynamics on complex networks
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#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion

#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion | Complexity Economics | Scoop.it

Via luiy, Shaolin Tan
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luiy's curator insight, March 27, 10:44 AM

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.

António F Fonseca's curator insight, April 2, 3:01 AM

Another paper about popularity prediction.

Rescooped by EconStreams.com from LeadershipABC
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Managing Complexity: The Battle Between Emergence And Entropy

Managing Complexity: The Battle Between Emergence And Entropy | Complexity Economics | Scoop.it

The business news continues to be full of stories of large companies getting into trouble in part because of their complexity. 


So what is a leader to do when faced with a highly complex organisation and a nagging concern that the creeping costs of complexity are starting to outweigh the benefits?


Via Kenneth Mikkelsen
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Olivier Arnould's curator insight, December 1, 2013 12:40 AM

Une approche intéressante des organisations...

luiy's curator insight, January 17, 6:34 AM

1. There is a design process –the allocation of roles and responsibilities through some sort of top-down master plan. We all know how this works.

 

2. There is an emergent process – a bottom-up form of spontaneous interaction between well-intentioned individuals, also known as self-organising. This has become very popular in the field of management, in large part because it draws on insights from the world of nature, such as the seemingly-spontaneous order that is exhibited by migrating geese and ant colonies. Under the right conditions, it seems, individual employees will come together to create effective coordinated action. The role of the leader is therefore to foster “emergent” order among employees without falling into the trap of over-engineering it.

 

3. Finally, there is an entropic process – the gradual trending of an organisational system towards disorder. This is where it gets a bit tricky. The disciples of self-organising often note that companies are “open systems” that exchange resources with the outside world, and this external source of energy is what helps to renew and refresh them. But the reality is that most companies are only semi-open. In fact, many large companies I know are actually pretty closed to outside influences. And if this is the case, the second law of thermodynamics comes into effect, namely that a closed system will gradually move towards a state of maximum disorder (i.e. entropy).

 

Rescooped by EconStreams.com from Dynamics on complex networks
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Transittability of complex networks and its applications to regulatory biomolecular networks : Scientific Reports : Nature Publishing Group

Transittability of complex networks and its applications to regulatory biomolecular networks : Scientific Reports : Nature Publishing Group | Complexity Economics | Scoop.it
We have often observed unexpected state transitions of complex systems. We are thus interested in how to steer a complex system from an unexpected state to a desired state. Here we introduce the concept of transittability of complex networks, and derive a new sufficient and necessary condition for state transittability which can be efficiently verified. We define the steering kernel as a minimal set of steering nodes to which control signals must directly be applied for transition between two specific states of a network, and propose a graph-theoretic algorithm to identify the steering kernel of a network for transition between two specific states. We applied our algorithm to 27 real complex networks, finding that sizes of steering kernels required for transittability are much less than those for complete controllability. Furthermore, applications to regulatory biomolecular networks not only validated our method but also identified the steering kernel for their phenotype transitions.

Via Shaolin Tan
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Complexity Studies in Economics, a new course on the éToile Platform

Complexity Studies in Economics, a new course on the éToile Platform | Complexity Economics | Scoop.it

This course is anchored on the seven main sections associated with the key Economics areas where the complex systems studies approach to economy has been known to have important influence. These sections are: Section I: A Philosophical and Methodological approach to Economy using Complexity Sciences; Section II: The structure of interaction; Section III: Macroeconomics and Growth; Section IV: Financial Markets; Section V: International and Monetary Economy Dynamics; Section VI: Regional Economic Systems; Section VII: Evolutionary Economic Dynamics. Other than discussing the literature, the students will be invited to model, implement and discuss some of the underlying mentioned models using social simulation programming libraries.


Via Jorge Louçã, Complexity Digest
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