Ising-based model of opinion formation in a complex network of interpersonal interactions

Grabowski, Andrzej; Kosiński, R. A.

doi:10.1016/j.physa.2005.06.102

Cited by 95 publications

(57 citation statements)

References 33 publications

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“…If the spin-spin couplings are renormalized according to this principle, the Ising model on scale-free networks always has a ferromagnetic threshold, whereas it is known that, with uniform couplings, networks with infinite degree variance are magnetized at any temperature (Aleksiejuk et al, 2002;Leone et al, 2002). The issue of how opinion dynamics is influenced by the hierarchical structure in societies/organizations has also been investigated (Grabowski and Kosiński, 2006b;Laguna et al, 2005). Other authors investigated fashion (Nakayama and Nakamura, 2004), the interplay between opinions and personal taste (Bagnoli et al, 2004) and the effect of opinion surveys on the outcome of elections (Alves et al, 2002).…”

Section: G Other Modelsmentioning

confidence: 99%

Statistical physics of social dynamics

2009

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Statistical physics has proven to be a very fruitful framework to describe phenomena outside the realm of traditional physics. The last years have witnessed the attempt by physicists to study collective phenomena emerging from the interactions of individuals as elementary units in social structures. Here we review the state of the art by focusing on a wide list of topics ranging from opinion, cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, social spreading. We highlight the connections between these problems and other, more traditional, topics of statistical physics. We also emphasize the comparison of model results with empirical data from social systems.

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Section: G Other Modelsmentioning

confidence: 99%

Statistical physics of social dynamics

2009

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“…In the simplest form of the model (on a 2-dimensional lattice without an external magnetic field) one observes a second-order phase transition of magnetization as a function of temperature. 1 In addition to its prominence in Theoretical Physics, this model -due to its simplicity and the fundamental relevance in systems of interacting two-state elementshas been applied to a multitude of real-world phenomena across a wide range of disciplines, ranging from opinion formation [34] to biological membranes [35]. The model itself and its diverse extensions (e.g., spin glasses, [36], or the Hopfield model, [37]) have also had a strong impact on Systems Biology.…”

Section: Physics and Systems Biology -Some Rules Of Interactionmentioning

confidence: 99%

The Physics behind Systems Biology

Radde

Hütt

2016

EPJ Nonlinear Biomed Phys

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Systems Biology is a young and rapidly evolving research field, which combines experimental techniques and mathematical modeling in order to achieve a mechanistic understanding of processes underlying the regulation and evolution of living systems. Systems Biology is often associated with an Engineering approach: The purpose is to formulate a data-rich, detailed simulation model that allows to perform numerical ('in silico') experiments and then draw conclusions about the biological system. While methods from Engineering may be an appropriate approach to extending the scope of biological investigations to experimentally inaccessible realms and to supporting data-rich experimental work, it may not be the best strategy in a search for design principles of biological systems and the fundamental laws underlying Biology. Physics has a long tradition of characterizing and understanding emergent collective behaviors in systems of interacting units and searching for universal laws. Therefore, it is natural that many concepts used in Systems Biology have their roots in Physics. With an emphasis on Theoretical Physics, we will here review the 'Physics core' of Systems Biology, show how some success stories in Systems Biology can be traced back to concepts developed in Physics, and discuss how Systems Biology can further benefit from its Theoretical Physics foundation.

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“…Agent based variants of such a model have further been studied by Yasutomi [12,13] and Górski et al [14] who by numerical simulations demonstrated that they are able to reveal money emergence as well as its collapse. In general, agent based models [15,16] find an increasing number of promising applications in various areas of economics [12,17,18,19,20,21] and in social sciences [22,23,24,25,26] and the need for this kind of approaching the related phenomena, especially in an economic context, is being expressed more and more forcefully [27,28,29]. For all these reasons in the present contribution, we further pursue simulations based on an agent based model which from an initial barter trading is able to spontaneously elevate one of the commodities to the money status.…”

Section: Introductionmentioning

confidence: 99%

Multiscaling Edge Effects in an Agent-based Money Emergence Model

Oświęcimka¹,

Dróżdż²,

Gębarowski³

et al. 2015

Acta Phys. Pol. B

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An agent-based computational economical toy model for the emergence of money from the initial barter trading, inspired by Menger's postulate that money can spontaneously emerge in a commodity exchange economy, is extensively studied. The model considered, while manageable, is significantly complex, however. It is already able to reveal phenomena that can be interpreted as emergence and collapse of money as well as the related competition effects. In particular, it is shown that -as an extra emerging effect -the money lifetimes near the critical threshold value develop multiscaling, which allow one to set parallels to critical phenomena and, thus, to the real financial markets.

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Ising-based model of opinion formation in a complex network of interpersonal interactions

Cited by 95 publications

References 33 publications

Statistical physics of social dynamics

Statistical physics of social dynamics

The Physics behind Systems Biology

Multiscaling Edge Effects in an Agent-based Money Emergence Model

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