4,872 research outputs found
Dynamical complexity in the perception-based network formation model
Many link formation mechanisms for the evolution of social networks have been
successful to reproduce various empirical findings in social networks. However,
they have largely ignored the fact that individuals make decisions on whether
to create links to other individuals based on cost and benefit of linking, and
the fact that individuals may use perception of the network in their decision
making. In this paper, we study the evolution of social networks in terms of
perception-based strategic link formation. Here each individual has her own
perception of the actual network, and uses it to decide whether to create a
link to another individual. An individual with the least perception accuracy
can benefit from updating her perception using that of the most accurate
individual via a new link. This benefit is compared to the cost of linking in
decision making. Once a new link is created, it affects the accuracies of other
individuals' perceptions, leading to a further evolution of the actual network.
As for initial actual networks, we consider homogeneous and heterogeneous
cases. The homogeneous initial actual network is modeled by Erd\H{o}s-R\'enyi
(ER) random networks, while we take a star network for the heterogeneous case.
In any cases, individual perceptions of the actual network are modeled by ER
random networks with controllable linking probability. Then the stable link
density of the actual network is found to show discontinuous transitions or
jumps according to the cost of linking. As the number of jumps is the
consequence of the dynamical complexity, we discuss the effect of initial
conditions on the number of jumps to find that the dynamical complexity
strongly depends on how much individuals initially overestimate or
underestimate the link density of the actual network. For the heterogeneous
case, the role of the highly connected individual as an information spreader is
discussed.Comment: 8 pages, 7 figure
Coevolution of a network and perception
How does an individual's cognition change a system which is a collective
behavior of individuals? Or, how does a system affect an individual's
cognition? To examine the interplay between a system and individuals, we study
a cognition-based network formation. When a network is not fully observable,
individuals' perception of a network plays an important role in decision
making. Assuming that a communication link is costly, and more accurate
perception yields higher network utility, an agent decides whether to form a
link in order to get better information or not. Changes in a network with newly
added links affect individuals' perception accuracy, which may cause further
changes in a network. We characterize the early stage of network dynamics and
information dispersion. Network structures in a steady state are also examined.
Additionally, we discuss local interactions and a link concentration in a
frequently changing network.Comment: 32 pages, 8 figure
ディーゼル機関における燃焼および排出ガス改善に関する研究
京都大学新制・課程博士博士(エネルギー科学)甲第24253号エネ博第451号京都大学大学院エネルギー科学研究科エネルギー変換科学専攻(主査)教授 川那辺 洋, 教授 林 潤, 教授 澄川 貴志学位規則第4条第1項該当Doctor of Energy ScienceKyoto UniversityDFA
Relevance of Abelian Symmetry and Stochasticity in Directed Sandpiles
We provide a comprehensive view on the role of Abelian symmetry and
stochasticity in the universality class of directed sandpile models, in context
of the underlying spatial correlations of metastable patterns and scars. It is
argued that the relevance of Abelian symmetry may depend on whether the dynamic
rule is stochastic or deterministic, by means of the interaction of metastable
patterns and avalanche flow. Based on the new scaling relations, we conjecture
critical exponents for avalanche, which is confirmed reasonably well in
large-scale numerical simulations.Comment: 4 pages, 3 figures; published versio
Gravity model explained by the radiation model on a population landscape
Understanding the mechanisms behind human mobility patterns is crucial to
improve our ability to optimize and predict traffic flows. Two representative
mobility models, i.e., radiation and gravity models, have been extensively
compared to each other against various empirical data sets, while their
fundamental relation is far from being fully understood. In order to study such
a relation, we first model the heterogeneous population landscape by generating
a fractal geometry of sites and then by assigning to each site a population
independently drawn from a power-law distribution. Then the radiation model on
this population landscape, which we call the radiation-on-landscape (RoL)
model, is compared to the gravity model to derive the distance exponent in the
gravity model in terms of the properties of the population landscape, which is
confirmed by the numerical simulations. Consequently, we provide a possible
explanation for the origin of the distance exponent in terms of the properties
of the heterogeneous population landscape, enabling us to better understand
mobility patterns constrained by the travel distance.Comment: 14 pages, 4 figure
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