Deviations from the majority: A local flip model

Tóth, Gábor Fejes

doi:10.1016/j.chaos.2022.112130

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…A good part of the work addresses the issue from the perspective of the dynamics of opinion within sociophysics [ 15 , 16 , 17 , 18 ]. Most related papers consider binary variables [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ], with a few having three discrete opinions [ 43 , 44 ]. Among the numerous models is the Galam model, which has been deployed in a multi-dimensional space of parameters to study the competition between discrete opinions [ 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ].…”

Section: Introductionmentioning

confidence: 99%

Unanimity, Coexistence, and Rigidity: Three Sides of Polarization

Galam

2023

Entropy

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Political polarization is perceived as a threat to democracies. Using the Galam model of opinion dynamics deployed in a five-dimensional parameter space, I show that polarization is the byproduct of an essential hallmark of a vibrant democratic society, namely open and informal discussions among agents. Indeed, within a homogeneous social community with floaters, the dynamics lead gradually toward unanimity (zero entropy). Polarization can eventually appear as the juxtaposition of non-mixing social groups sharing different prejudices about the issue at stake. On the other hand, the inclusion of contrarian agents produces a polarization within a community that mixes when their proportion x is beyond a critical value xc=16≈0.167 for discussing groups of size three and four. Similarly, the presence of stubborn agents also produces a polarization of a community that mixes when the proportion of stubborn agents is greater than some critical value. For equal proportions of stubborn agents a along each opinion, ac=29≈0.22 for group size four against ac=14=0.25 for group size three. However, the evaluation of the proportion of individual opinion shifts at the attractor 12 and indicates that the polarization produced by contrarians is fluid with a good deal of agents who keep shifting between the two opposed blocks (high entropy). That favors a coexistence of opposite opinions in a divided community. In contrast, the polarization created by stubborn agents is found to be frozen with very few individuals shifting opinion between the two opinions (low entropy). That yields a basis for the emergence of hate between the frozen opposed blocks.

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Section: Introductionmentioning

confidence: 99%

Unanimity, Coexistence, and Rigidity: Three Sides of Polarization

Galam

2023

Entropy

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“…To this end, various modelling approaches, such as machine learning [9], network theory [10], and agent-based models [11] can be implemented, typically considering the nature of the available data and the specific goals. The above-mentioned human behaviour, giving rise to attitudes and traits such as (anti-)conformity [12][13][14], stubbornness [15], and polarisation [16], can be mapped to some parameter added in these models. For instance, agents can be endowed with features for studying whether and how population dynamics get affected by them-see [17,18].…”

Section: Introductionmentioning

confidence: 99%

The rise of rationality in blockchain dynamics

Di Antonio,

Vinci,

Pietronero

et al. 2023

New J. Phys.

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Taking informed decisions, namely acting rationally, is an individual attitude of paramount relevance in nature and human societies. In this work, we study how rationality spreads in a community. To this end, through an agent-based model, we analyse the dynamics of a population whose individuals, endowed with a rational attitude controlled by a numerical parameter, play a simple game. The latter consists of multiple strategies, each associated with a given reward. The proposed model is then used as a benchmark for studying the behaviour of Bitcoin users, inferred by analysing transactions recorded in the Blockchain. Remarkably, a population undergoing a sharp transition from irrational to rational attitudes shows a behavioural pattern similar to that of Bitcoin users, whose rationality showed up as soon as their cryptocurrency became worth just a few cents (USD). To conclude, a behavioural analysis that relies on an entropy measure combined with a simple agent-based model allows us to detect the rise of rationality across a community. Although further investigations are essential to corroborate our results, we deem the proposed approach could also get used for studying other social phenomena and behaviours.

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A general framework to link theory and empirics in opinion formation models

Kozitsin

2022

Sci Rep

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We introduce a minimal opinion formation model that is quite flexible and can reproduce a wide variety of the existing micro-influence assumptions and models. The model can be easily calibrated on real data, upon which it imposes only a few requirements. From this perspective, our model can be considered as a bridge, connecting theoretical studies on opinion formation models and empirical research on social dynamics. We investigate the model analytically by using mean-field approximation and numerically via Monte Carlo simulations. Our analysis is exemplified by recently reported empirical data drawn from an online social network. We demonstrate that the model calibrated on these data may reproduce fragmented and polarizing social systems. Furthermore, we manage to generate an artificial society that features properties quantitatively and qualitatively similar to those observed empirically at the macro scale. This ability became possible after we had advanced the model with two important communication features: selectivity and personalization algorithms.

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Deviations from the majority: A local flip model

Cited by 5 publications

References 29 publications

Unanimity, Coexistence, and Rigidity: Three Sides of Polarization

Unanimity, Coexistence, and Rigidity: Three Sides of Polarization

The rise of rationality in blockchain dynamics

A general framework to link theory and empirics in opinion formation models

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