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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Scooped by Bernard Ryefield
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Network Strategies in the Voter Model

We study a simple voter model with two competing parties. In particular, we represent the case of political elections, where people can choose to support one of the two competitors or to remain neutral. People interact in a social network and their opinion depends on those of their neighbors. Therefore, people may change opinion over time, i.e., they can support one competitor or none. The two competitors try to gain the people's consensus by interacting with their neighbors and also with other people. In particular, competitors define temporal connections, following a strategy, to interact with people they do not know, i.e., with all the people that are not their neighbors. We analyze the proposed model to investigate which network strategies are more advantageous, for the competitors, in order to gain the popular consensus. As result, we found that the best network strategy depends on the topology of the social network. Finally, we investigate how the charisma of competitors affects the outcomes of the proposed model.

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Scooped by Bernard Ryefield
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On the Topology of the Facebook Page Network

The Facebook Page Network (FPN) is a platform for Businesses, Public Figures and Organizations (BPOs) to connect with individuals and other BPOs in the digital space. For over a decade scale-free networks have most appropriately described a variety of seemingly disparate physical, biological and social real-world systems unified by similar network properties such as scale-invariance, growth via a preferential attachment mechanism, and a power law degree distribution P(k) = ck^-{\lambda} where typically 2<{\lambda}<3. In this paper we show that both the Facebook Page Network and its BPO-BPO subnetwork suggest power law and scale-free characteristics. We argue that social media analysts must consider the logarithmic and non-linear properties of social media audiences of scale.

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