Boundary effects in extended dynamical systems

In the framework of spatially extended dynamical systems, we present three examples in which the presence of walls lead to dynamic behavior qualitatively different from the one obtained in an infinite domain or under periodic boundary conditions. For a nonlinear reaction-diffusion model we obtain boundary-induced spatially chaotic configurations. Nontrivial average patterns arising from boundaries are shown to appear in spatiotemporally chaotic states of the Kuramoto-Sivashinsky model. Finally, walls organize novel states in simulations of the complex Ginzburg-Landau equation.Comment: Proceedigs of LAWNP'99. To be published in Physica A. Uses the Elsart style. This short paper is intended as a summary of our recent work on boundary influences in extended dynamical systems, with links to more detailed papers. Related material at http://www.imedea.uib.es/PhysDept

A Herding Model with Preferential Attachment and Fragmentation

We introduce and solve a model that mimics the herding effect in financial markets when groups of agents share information. The number of agents in the model is growing and at each time step either (i) with probability p an incoming agent joins an existing group, or (ii) with probability 1-p a group is fragmented into individual agents. The group size distribution is found to be power-law with an exponent that depends continuously on p. A number of variants of our basic model are discussed. Comparisons are made between these models and other models of herding and random growing networks

A model for the size distribution of customer groups and businesses

We present a generalization of the dynamical model of information transmission and herd behavior proposed by Eguiluz and Zimmermann. A characteristic size of group of agents s0 is introduced. The fragmentation and coagulation rates of groups of agents are assumed to depend on the size of the group. We present results of numerical simulations and mean field analysis. It is found that the size distribution of groups of agents ns exhibits two distinct scaling behavior depending on s ≤ s0 or s > s0. For s ≤ s0, ns ∼ s-(5/2 + δ), while for s > s0, ns ∼ s-(5/2 -δ), where δ is a model parameter representing the sensitivity of the fragmentation and coagulation rates to the size of the group. Our model thus gives a tunable exponent for the size distribution together with two scaling regimes separated by a characteristic size s0. Suitably interpreted, our model can be used to represent the formation of groups of customers for certain products produced by manufacturers. This, in turn, leads to a distribution in the size of businesses. The characteristic size s0, in this context, represents the size of a business for which the customer group becomes too large to be kept happy but too small for the business to become a brand name

Cultural transmission and optimization dynamics

We study the one-dimensional version of Axelrod's model of cultural transmission from the point of view of optimization dynamics. We show the existence of a Lyapunov potential for the dynamics. The global minimum of the potential, or optimum state, is the monocultural uniform state, which is reached for an initial diversity of the population below a critical value. Above this value, the dynamics settles in a multicultural or polarized state. These multicultural attractors are not local minima of the potential, so that any small perturbation initiates the search for the optimum state. Cultural drift is modelled by such perturbations acting at a finite rate. If the noise rate is small, the system reaches the optimum monocultural state. However, if the noise rate is above a critical value, that depends on the system size, noise sustains a polarized dynamical state.Comment: 11 pages, 10 figures include

The Spread of Infectious Disease with Household-Structure on the Complex Networks

In this paper we study the household-structure SIS epidemic spreading on general complex networks. The household structure gives us the way to distinguish inner and the outer infection rate. Unlike household-structure models on homogenous networks, such as regular and random networks, here we consider heterogeneous networks with arbitrary degree distribution p(k). First we introduce the epidemic model. Then rate equations under mean field appropriation and computer simulations are used here to analyze our model. Some unique phenomena only existing in divergent network with household structure is found, while we also get some similar conclusions that some simple geometrical quantities of networks have important impression on infection property of infectous disease. It seems that in our model even when local cure rate is greater than inner infection rate in every household, disease still can spread on scale-free network. It implies that no disease is spreading in every single household, but for the whole network, disease is spreading. Since our society network seems like this structure, maybe this conclusion remind us that during disease spreading we should pay more attention on network structure than local cure condition.Comment: 12 pages, 2 figure