6,716 research outputs found

    Study of near consensus complex social networks using Eigen theory

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    This paper extends the definition of an exact consensus complex social network to that of a near consensus complex social network. A near consensus complex social network is a social network with nontrivial topological features and steady state values of the decision certitudes of the majority of the nodes being either higher or lower than a threshold value. By using eigen theories, the relationships among the vectors representing the steady state values of the decision certitudes of the nodes, the influence weight matrix and the set of vectors representing the initial state values of the decision certitudes of the nodes that satisfies a given near consensus specification are characterized

    Connections between the Sznajd Model with General Confidence Rules and graph theory

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    The Sznajd model is a sociophysics model, that is used to model opinion propagation and consensus formation in societies. Its main feature is that its rules favour bigger groups of agreeing people. In a previous work, we generalized the bounded confidence rule in order to model biases and prejudices in discrete opinion models. In that work, we applied this modification to the Sznajd model and presented some preliminary results. The present work extends what we did in that paper. We present results linking many of the properties of the mean-field fixed points, with only a few qualitative aspects of the confidence rule (the biases and prejudices modelled), finding an interesting connection with graph theory problems. More precisely, we link the existence of fixed points with the notion of strongly connected graphs and the stability of fixed points with the problem of finding the maximal independent sets of a graph. We present some graph theory concepts, together with examples, and comparisons between the mean-field and simulations in Barab\'asi-Albert networks, followed by the main mathematical ideas and appendices with the rigorous proofs of our claims. We also show that there is no qualitative difference in the mean-field results if we require that a group of size q>2, instead of a pair, of agreeing agents be formed before they attempt to convince other sites (for the mean-field, this would coincide with the q-voter model).Comment: 15 pages, 18 figures. To be submitted to Physical Revie

    Naturalism, Theism, and the Origin of Life

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    Alvin Plantinga and Phillip E. Johnson strongly attack "metaphysical naturalism", a doctrine based, in part, on Darwinian concepts. They claim that this doctrine dominates American academic, educational, and legal thought, and that it is both erroneous and pernicious. Stuart Kauffman claims that currently accepted versions of Darwinian evolutionary theory are radically incomplete, that they should be supplemented by explicit recognition of the importance of coherent structures — the prevalence of "order for free". Both of these developments are here interpreted in relation to some contemporary theistic notions of "creation", including those of Lewis Ford, Robert Neville, and Robert Sokolowski. Kaufmann’s approach is consistent with the approach of process theism, and is not invalidated by the attacks of Plantinga and Johnson

    Collective Relaxation Dynamics of Small-World Networks

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    Complex networks exhibit a wide range of collective dynamic phenomena, including synchronization, diffusion, relaxation, and coordination processes. Their asymptotic dynamics is generically characterized by the local Jacobian, graph Laplacian or a similar linear operator. The structure of networks with regular, small-world and random connectivities are reasonably well understood, but their collective dynamical properties remain largely unknown. Here we present a two-stage mean-field theory to derive analytic expressions for network spectra. A single formula covers the spectrum from regular via small-world to strongly randomized topologies in Watts-Strogatz networks, explaining the simultaneous dependencies on network size N, average degree k and topological randomness q. We present simplified analytic predictions for the second largest and smallest eigenvalue, and numerical checks confirm our theoretical predictions for zero, small and moderate topological randomness q, including the entire small-world regime. For large q of the order of one, we apply standard random matrix theory thereby overarching the full range from regular to randomized network topologies. These results may contribute to our analytic and mechanistic understanding of collective relaxation phenomena of network dynamical systems.Comment: 12 pages, 10 figures, published in PR

    First-passage distributions for the one-dimensional Fokker-Planck equation

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    We present an analytical framework to study the first-passage (FP) and first-return (FR) distributions for the broad family of models described by the one-dimensional Fokker-Planck equation in finite domains, identifying general properties of these distributions for different classes of models. When in the Fokker-Planck equation the diffusion coefficient is positive (nonzero) and the drift term is bounded, as in the case of a Brownian walker, both distributions may exhibit a power-law decay with exponent -3/2 for intermediate times. We discuss how the influence of an absorbing state changes this exponent. The absorbing state is characterized by a vanishing diffusion coefficient and/or a diverging drift term. Remarkably, the exponent of the Brownian walker class of models is still found, as long as the departure and arrival regions are far enough from the absorbing state, but the range of times where the power law is observed narrows. Close enough to the absorbing point, though, a new exponent may appear. The particular value of the exponent depends on the behavior of the diffusion and the drift terms of the Fokker-Planck equation. We focus on the case of a diffusion term vanishing linearly at the absorbing point. In this case, the FP and FR distributions are similar to those of the voter model, characterized by a power law with exponent -2. As an illustration of the general theory, we compare it with exact analytical solutions and extensive numerical simulations of a two-parameter voter-like family models. We study the behavior of the FP and FR distributions by tuning the importance of the absorbing points throughout changes of the parameters. Finally, the possibility of inferring relevant information about the steady-sate probability distribution of a model from the FP and FR distributions is addressed.Comment: 17 pages, 8 figure

    Weighted random--geometric and random--rectangular graphs: Spectral and eigenfunction properties of the adjacency matrix

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    Within a random-matrix-theory approach, we use the nearest-neighbor energy level spacing distribution P(s)P(s) and the entropic eigenfunction localization length \ell to study spectral and eigenfunction properties (of adjacency matrices) of weighted random--geometric and random--rectangular graphs. A random--geometric graph (RGG) considers a set of vertices uniformly and independently distributed on the unit square, while for a random--rectangular graph (RRG) the embedding geometry is a rectangle. The RRG model depends on three parameters: The rectangle side lengths aa and 1/a1/a, the connection radius rr, and the number of vertices NN. We then study in detail the case a=1a=1 which corresponds to weighted RGGs and explore weighted RRGs characterized by a1a\sim 1, i.e.~two-dimensional geometries, but also approach the limit of quasi-one-dimensional wires when a1a\gg1. In general we look for the scaling properties of P(s)P(s) and \ell as a function of aa, rr and NN. We find that the ratio r/Nγr/N^\gamma, with γ(a)1/2\gamma(a)\approx -1/2, fixes the properties of both RGGs and RRGs. Moreover, when a10a\ge 10 we show that spectral and eigenfunction properties of weighted RRGs are universal for the fixed ratio r/CNγr/{\cal C}N^\gamma, with Ca{\cal C}\approx a.Comment: 8 pages, 6 figure

    Open problems in artificial life

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    This article lists fourteen open problems in artificial life, each of which is a grand challenge requiring a major advance on a fundamental issue for its solution. Each problem is briefly explained, and, where deemed helpful, some promising paths to its solution are indicated
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