43,962 research outputs found
Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure
Large-scale transitions in societies are associated with both individual
behavioural change and restructuring of the social network. These two factors
have often been considered independently, yet recent advances in social network
research challenge this view. Here we show that common features of societal
marginalization and clustering emerge naturally during transitions in a
co-evolutionary adaptive network model. This is achieved by explicitly
considering the interplay between individual interaction and a dynamic network
structure in behavioural selection. We exemplify this mechanism by simulating
how smoking behaviour and the network structure get reconfigured by changing
social norms. Our results are consistent with empirical findings: The
prevalence of smoking was reduced, remaining smokers were preferentially
connected among each other and formed increasingly marginalised clusters. We
propose that self-amplifying feedbacks between individual behaviour and dynamic
restructuring of the network are main drivers of the transition. This
generative mechanism for co-evolution of individual behaviour and social
network structure may apply to a wide range of examples beyond smoking.Comment: 16 pages, 5 figure
Dynamical Systems on Networks: A Tutorial
We give a tutorial for the study of dynamical systems on networks. We focus
especially on "simple" situations that are tractable analytically, because they
can be very insightful and provide useful springboards for the study of more
complicated scenarios. We briefly motivate why examining dynamical systems on
networks is interesting and important, and we then give several fascinating
examples and discuss some theoretical results. We also briefly discuss
dynamical systems on dynamical (i.e., time-dependent) networks, overview
software implementations, and give an outlook on the field.Comment: 39 pages, 1 figure, submitted, more examples and discussion than
original version, some reorganization and also more pointers to interesting
direction
Opinion dynamics: models, extensions and external effects
Recently, social phenomena have received a lot of attention not only from
social scientists, but also from physicists, mathematicians and computer
scientists, in the emerging interdisciplinary field of complex system science.
Opinion dynamics is one of the processes studied, since opinions are the
drivers of human behaviour, and play a crucial role in many global challenges
that our complex world and societies are facing: global financial crises,
global pandemics, growth of cities, urbanisation and migration patterns, and
last but not least important, climate change and environmental sustainability
and protection. Opinion formation is a complex process affected by the
interplay of different elements, including the individual predisposition, the
influence of positive and negative peer interaction (social networks playing a
crucial role in this respect), the information each individual is exposed to,
and many others. Several models inspired from those in use in physics have been
developed to encompass many of these elements, and to allow for the
identification of the mechanisms involved in the opinion formation process and
the understanding of their role, with the practical aim of simulating opinion
formation and spreading under various conditions. These modelling schemes range
from binary simple models such as the voter model, to multi-dimensional
continuous approaches. Here, we provide a review of recent methods, focusing on
models employing both peer interaction and external information, and
emphasising the role that less studied mechanisms, such as disagreement, has in
driving the opinion dynamics. [...]Comment: 42 pages, 6 figure
Largenet2: an object-oriented programming library for simulating large adaptive networks
The largenet2 C++ library provides an infrastructure for the simulation of
large dynamic and adaptive networks with discrete node and link states. The
library is released as free software. It is available at
http://rincedd.github.com/largenet2. Largenet2 is licensed under the Creative
Commons Attribution-NonCommercial 3.0 Unported License.Comment: 2 pages, 1 figur
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