7,815 research outputs found
Fermionic Networks: Modeling Adaptive Complex Networks with Fermionic Gases
We study the structure of Fermionic networks, i.e., a model of networks based
on the behavior of fermionic gases, and we analyze dynamical processes over
them. In this model, particle dynamics have been mapped to the domain of
networks, hence a parameter representing the temperature controls the evolution
of the system. In doing so, it is possible to generate adaptive networks, i.e.,
networks whose structure varies over time. As shown in previous works, networks
generated by quantum statistics can undergo critical phenomena as phase
transitions and, moreover, they can be considered as thermodynamic systems. In
this study, we analyze Fermionic networks and opinion dynamics processes over
them, framing this network model as a computational model useful to represent
complex and adaptive systems. Results highlight that a strong relation holds
between the gas temperature and the structure of the achieved networks.
Notably, both the degree distribution and the assortativity vary as the
temperature varies, hence we can state that fermionic networks behave as
adaptive networks. On the other hand, it is worth to highlight that we did not
find relation between outcomes of opinion dynamics processes and the gas
temperature. Therefore, although the latter plays a fundamental role in gas
dynamics, on the network domain its importance is related only to structural
properties of fermionic networks.Comment: 19 pages, 5 figure
Social Influence and the Collective Dynamics of Opinion Formation
Social influence is the process by which individuals adapt their opinion,
revise their beliefs, or change their behavior as a result of social
interactions with other people. In our strongly interconnected society, social
influence plays a prominent role in many self-organized phenomena such as
herding in cultural markets, the spread of ideas and innovations, and the
amplification of fears during epidemics. Yet, the mechanisms of opinion
formation remain poorly understood, and existing physics-based models lack
systematic empirical validation. Here, we report two controlled experiments
showing how participants answering factual questions revise their initial
judgments after being exposed to the opinion and confidence level of others.
Based on the observation of 59 experimental subjects exposed to peer-opinion
for 15 different items, we draw an influence map that describes the strength of
peer influence during interactions. A simple process model derived from our
observations demonstrates how opinions in a group of interacting people can
converge or split over repeated interactions. In particular, we identify two
major attractors of opinion: (i) the expert effect, induced by the presence of
a highly confident individual in the group, and (ii) the majority effect,
caused by the presence of a critical mass of laypeople sharing similar
opinions. Additional simulations reveal the existence of a tipping point at
which one attractor will dominate over the other, driving collective opinion in
a given direction. These findings have implications for understanding the
mechanisms of public opinion formation and managing conflicting situations in
which self-confident and better informed minorities challenge the views of a
large uninformed majority.Comment: Published Nov 05, 2013. Open access at:
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.007843
Ordering dynamics in the voter model with aging
The voter model with memory-dependent dynamics is theoretically and
numerically studied at the mean-field level. The `internal age', or time an
individual spends holding the same state, is added to the set of binary states
of the population, such that the probability of changing state (or activation
probability ) depends on this age. A closed set of integro-differential
equations describing the time evolution of the fraction of individuals with a
given state and age is derived, and from it analytical results are obtained
characterizing the behavior of the system close to the absorbing states. In
general, different age-dependent activation probabilities have different
effects on the dynamics. When the activation probability is an increasing
function of the age , the system reaches a steady state with coexistence of
opinions. In the case of aging, with being a decreasing function, either
the system reaches consensus or it gets trapped in a frozen state, depending on
the value of (zero or not) and the velocity of approaching
. Moreover, when the system reaches consensus, the time ordering of
the system can be exponential () or power-law like ().
Exact conditions for having one or another behavior, together with the
equations and explicit expressions for the exponents, are provided
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