1,721,214 research outputs found
How Opinion Dynamics Shapes Social Networks Topology
Full text linkWe investigate an opinion dynamics model with continuously defined affinities and opinions. We focus here on the effects of the social network's topology on the dynamical evolution and on the scale properties of the model measured through numerical simulations and fittings. We study different network topologies through a set of statistical network measures, namely mean path, mean degree and clustering. We observe that the model's dynamics eventually leads to a uniformisation of the different topologies
The influence of social network topology in a opinion dynamics model
Full text linkWe investigate an opinion dynamics model with continuously defined affinities and opinions. We focus here on the effects of the affinity matrix's topology on the dynamical evolution and on the scale properties of the model measured through numerical simulations and fittings. We study through a set of statistical network measures, namely mean path, mean degree and clustering, different network topologies. We observe that the model's dynamics eventually leads to a uniformization of the different topologies
Behavioral biases and informational inefficiency in an agent-based financial market
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Weighted Fractal Networks
Full text linkIn this paper we define a new class of weighted complex networks sharing several properties with fractal sets, and whose topology can be completely analytically characterized in terms of the involved parameters and of the fractal dimension. General networks with fractal or hierarchical structures can be set in the proposed framework that moreover could be used to provide some answers to the widespread emergence of fractal structures in nature
The Stochastic Evolution of a Protocell: The Gillespie Algorithm in a Dynamically Varying Volume
Full text linkWe propose an improvement of the Gillespie algorithm allowing us to study the time evolution of an ensemble of chemical reactions occurring in a varying volume, whose growth is directly related to the amount of some specific molecules, belonging to the reactions set. This allows us to study the stochastic evolution of a protocell, whose volume increases because of the production of container molecules. Several protocell models are considered and compared with the deterministic models
Face-to-face discussions: networking or opinions exchange?
Full text linkhttps://doi.org/10.1007/978-3-319-00395-5_99We use recent results of [4] on face-to-face contact durations to try to answer the question: why do people engage in face-to-face discussions? In particular we focus on behavior of scientists in academic conferences. We show evidence that macroscopic measured data are compatible with two different micro-founded models of social interaction. We find that the first model, in which discussions are performed with the aim of introducing oneself (networking), explains the data when the group exhibits few well reputed scientists. On the contrary, when the reputation hierarchy is not strong, a model where agents’ encounters are aimed at exchanging opinions explains the data better.We use recent results of [4] on face-to-face contact durations to try to answer
the question: why do people engage in face-to-face discussions? In particular we focus on
behavior of scientists in academic conferences. We show evidence that macroscopic measured
data are compatible with two different micro-founded models of social interaction. We find
that the first model, in which discussions are performed with the aim of introducing oneself
(networking), explains the data when the group exhibits few well reputed scientists. On the
contrary, when the reputation hierarchy is not strong, a model where agents’ encounters are
aimed at exchanging opinions explains the data bette
Emerging structures in social networks guided by opinions' exchanges
Full text linkIn this paper we show that the small world and weak ties phenomena can spontaneously emerge in a social network of interacting agents. This dynamics is simulated in the framework of a simplified model of opinion diffusion in an evolving social network where agents are made to interact, possibly update their beliefs and modify the social relationships according to the opinion exchange
Behavioral Biases and Informational Inefficiency in an Agent-Based Financial Market
Full text linkThe role of competitive markets as efficient aggregators of decentralized information is a fundamental problem in economic theory. This paper studies the informational efficiency of a market with a single traded asset, in which agents expectation formation about future price has two kinds of deviations from rationality. First, traders have adaptive expectations, i.e. they give more importance to the past price than a rational agent. Second, the agents are subject to the confirmatory bias, i.e. they tend to discard new information that substantially differs from their priors. Taken separately, each deviation worsens the informational efficiency of the market. However, for some ranges of parameters, when the two biases are combined, they tend to mitigate each other effect (thus increasing the informational efficiency). We also study the robustness of these findings to alternative specifications concerning market participation, entry of new agents, and the amount of liquidity that agents hold.The role of competitive markets as efficient aggregators of decentralized information is a fundamental problem in economic theory. This paper studies the informational efficiency of a market with a single traded asset, in which agents expectation formation about future price has two kinds of deviations from rationality. First, traders have adaptive expectations, i.e. they give more importance to the past price than a rational agent. Second, the agents are subject to the confirmatory bias, i.e. they tend to discard new information that substantially differs from their priors. Taken separately, each deviation worsens the informational efficiency of the market. However, for some ranges of parameters, when the two biases are combined, they tend to mitigate each other effect (thus increasing the informational efficiency). We also study the robustness of these findings to alternative specifications concerning market participation, entry of new agents, and the amount of liquidity that agents hold
Random walks on hypergraphs
Full text linkIn the past 20 years network science has proven its strength in modeling many real-world interacting systems as generic agents, the nodes, connected by pairwise edges. Nevertheless, in many relevant cases, interactions are not pairwise but involve larger sets of nodes at a time. These systems are thus better described in the framework of hypergraphs, whose hyperedges effectively account for multibody interactions. Here we propose and study a class of random walks defined on such higher-order structures and grounded on a microscopic physical model where multibody proximity is associated with highly probable exchanges among agents belonging to the same hyperedge. We provide an analytical characterization of the process, deriving a general solution for the stationary distribution of the walkers. The dynamics is ultimately driven by a generalized random-walk Laplace operator that reduces to the standard random-walk Laplacian when all the hyperedges have size 2 and are thus meant to describe pairwise couplings. We illustrate our results on synthetic models for which we have full control of the high-order structures and on real-world networks where higher-order interactions are at play. As the first application of the method, we compare the behavior of random walkers on hypergraphs to that of traditional random walkers on the corresponding projected networks, drawing interesting conclusions on node rankings in collaboration networks. As the second application, we show how information derived from the random walk on hypergraphs can be successfully used for classification tasks involving objects with several features, each one represented by a hyperedge. Taken together, our work contributes to unraveling the effect of higher-order interactions on diffusive processes in higher-order networks, shedding light on mechanisms at the heart of biased information spreading in complex networked systems
Turing instabilities on Cartesian product networks
Full text linkThe problem of Turing instabilities for a reaction-diffusion system defined on a complex Cartesian product network is considered. To this end we operate in the linear regime and expand the time dependent perturbation on a basis formed by the tensor product of the eigenvectors of the discrete Laplacian operators, associated to each of the individual networks that build the Cartesian product. The dispersion relation which controls the onset of the instability depends on a set of discrete wavelengths, the eigenvalues of the aforementioned Laplacians. Patterns can develop on the Cartesian network, if they are supported on at least one of its constitutive sub-graphs. Multiplex networks are also obtained under specific prescriptions. In this case, the criteria for the instability reduce to compact explicit formulae. Numerical simulations carried out for the Mimura-Murray reaction kinetics confirm the adequacy of the proposed theory
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