44,181 research outputs found
Antipersistant Effects in the Dynamics of a Competing Population
We consider a population of agents competing for finite resources using
strategies based on two channels of signals. The model is applicable to
financial markets, ecosystems and computer networks. We find that the dynamics
of the system is determined by the correlation between the two channels. In
particular, occasional mismatches of the signals induce a series of transitions
among numerous attractors. Surprisingly, in contrast to the effects of noises
on dynamical systems normally resulting in a large number of attractors, the
number of attractors due to the mismatched signals remains finite. Both
simulations and analyses show that this can be explained by the antipersistent
nature of the dynamics. Antipersistence refers to the response of the system to
a given signal being opposite to that of the signal's previous occurrence, and
is a consequence of the competition of the agents to make minority decisions.
Thus, it is essential for stabilizing the dynamical systems.Comment: 4 pages, 6 figure
Inference and Optimization of Real Edges on Sparse Graphs - A Statistical Physics Perspective
Inference and optimization of real-value edge variables in sparse graphs are
studied using the Bethe approximation and replica method of statistical
physics. Equilibrium states of general energy functions involving a large set
of real edge-variables that interact at the network nodes are obtained in
various cases. When applied to the representative problem of network resource
allocation, efficient distributed algorithms are also devised. Scaling
properties with respect to the network connectivity and the resource
availability are found, and links to probabilistic Bayesian approximation
methods are established. Different cost measures are considered and algorithmic
solutions in the various cases are devised and examined numerically. Simulation
results are in full agreement with the theory.Comment: 21 pages, 10 figures, major changes: Sections IV to VII updated,
Figs. 1 to 3 replace
Self-Organization of Balanced Nodes in Random Networks with Transportation Bandwidths
We apply statistical physics to study the task of resource allocation in
random networks with limited bandwidths along the transportation links. The
mean-field approach is applicable when the connectivity is sufficiently high.
It allows us to derive the resource shortage of a node as a well-defined
function of its capacity. For networks with uniformly high connectivity, an
efficient profile of the allocated resources is obtained, which exhibits
features similar to the Maxwell construction. These results have good
agreements with simulations, where nodes self-organize to balance their
shortages, forming extensive clusters of nodes interconnected by unsaturated
links. The deviations from the mean-field analyses show that nodes are likely
to be rich in the locality of gifted neighbors. In scale-free networks, hubs
make sacrifice for enhanced balancing of nodes with low connectivity.Comment: 7 pages, 8 figure
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