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1.
Phys Rev E Stat Nonlin Soft Matter Phys ; 81(6 Pt 2): 066120, 2010 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-20866491

RESUMO

In the 70s Schelling introduced a multiagent model to describe the segregation dynamics that may occur with individuals having only weak preferences for "similar" neighbors. Recently variants of this model have been discussed, in particular, with emphasis on the links with statistical physics models. Whereas these models consider a fixed number of agents moving on a lattice, here, we present a version allowing for exchanges with an external reservoir of agents. The density of agents is controlled by a parameter which can be viewed as measuring the attractiveness of the city lattice. This model is directly related to the zero-temperature dynamics of the Blume-Emery-Griffiths spin-1 model, with kinetic constraints. With a varying vacancy density, the dynamics with agents making deterministic decisions leads to a variety of "phases" whose main features are the characteristics of the interfaces between clusters of agents of different types. The domains of existence of each type of interface are obtained analytically as well as numerically. These interfaces may completely isolate the agents leading to another type of segregation as compared to what is observed in the original Schelling model, and we discuss its possible socioeconomic correlates.

2.
Phys Rev E Stat Nonlin Soft Matter Phys ; 68(3 Pt 2): 036128, 2003 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-14524853

RESUMO

We compute the probability distribution of the interface width at the depinning threshold, using recent powerful algorithms. It confirms the universality classes found previously. In all cases, the distribution is surprisingly well approximated by a generalized Gaussian theory of independent modes which decay with a characteristic propagator G(q)=1/q(d+2zeta); zeta, the roughness exponent, is computed independently. A functional renormalization analysis explains this result and allows one to compute the small deviations, i.e., a universal kurtosis ratio, in agreement with numerics. We stress the importance of the Gaussian theory to interpret numerical data and experiments.

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