Bounded confidence opinion dynamics: A survey

Bernardo, Carmela; Altafini, Claudio; Proskurnikov, Anton; Vasca, Francesco

doi:10.1016/j.automatica.2023.111302

Cited by 24 publications

(2 citation statements)

References 129 publications

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“…Basically, there are two groups of models: either individuals are equipped with one of a finite number of opinions (typically pro and contra) or their opinions are characterized by a continuous spectrum of possibilities (typically the interval

, see [ 5 ]). In particular, the models with a continuous state space are often used to investigate whether a consensus can be reached in the long run [ 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Opinion Models, Election Data, and Political Theory

Gsänger,

Hösel,

Mohamad-Klotzbach

et al. 2024

Entropy

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A unifying setup for opinion models originating in statistical physics and stochastic opinion dynamics are developed and used to analyze election data. The results are interpreted in the light of political theory. We investigate the connection between Potts (Curie–Weiss) models and stochastic opinion models in the view of the Boltzmann distribution and stochastic Glauber dynamics. We particularly find that the q-voter model can be considered as a natural extension of the Zealot model, which is adapted by Lagrangian parameters. We also discuss weak and strong effects (also called extensive and nonextensive) continuum limits for the models. The results are used to compare the Curie–Weiss model, two q-voter models (weak and strong effects), and a reinforcement model (weak effects) in explaining electoral outcomes in four western democracies (United States, Great Britain, France, and Germany). We find that particularly the weak effects models are able to fit the data (Kolmogorov–Smirnov test) where the weak effects reinforcement model performs best (AIC). Additionally, we show how the institutional structure shapes the process of opinion formation. By focusing on the dynamics of opinion formation preceding the act of voting, the models discussed in this paper give insights both into the empirical explanation of elections as such, as well as important aspects of the theory of democracy. Therefore, this paper shows the usefulness of an interdisciplinary approach in studying real world political outcomes by using mathematical models.

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, see [ 5 ]). In particular, the models with a continuous state space are often used to investigate whether a consensus can be reached in the long run [ 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Opinion Models, Election Data, and Political Theory

Gsänger,

Hösel,

Mohamad-Klotzbach

et al. 2024

Entropy

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“…Reinforcement learning has been recently introduced to the study of opinion dynamics with an innovative model explaining polarization through learning from social feedback [ 19 ]. This model stands out by introducing a simple learning mechanism to explain polarization without assuming bounded confidence [ 36 ] or negative influence [ 37 ]. The present study builds on these findings and extends the original model with a reward system accounting for confirmation bias—consistent with behavioural and neuroimaging studies [ 24 – 26 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

The roots of polarization in the individual reward system

Lefebvre,

Deroy,

Bahrami

2024

Proc. R. Soc. B.

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Polarization raises concerns for democracy and society, which have expanded in the internet era where (mis)information has become ubiquitous, its transmission faster than ever, and the freedom and means of opinion expressions are expanding. The origin of polarization however remains unclear, with multiple social and emotional factors and individual reasoning biases likely to explain its current forms. In the present work, we adopt a principled approach and show that polarization tendencies can take root in biased reward processing of new information in favour of choice confirmatory evidence. Through agent-based simulations, we show that confirmation bias in individual learning is an independent mechanism and could be sufficient for creating polarization at group level independently of any additional assumptions about the opinions themselves, a priori beliefs about them, information transmission mechanisms or the structure of social relationship between individuals. This generative process can interact with polarization mechanisms described elsewhere, but constitutes an entrenched biological tendency that helps explain the extraordinary resilience of polarization against mitigating efforts such as dramatic informational change in the environment.

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Opinion Dynamics of Two Populations With Time‐Delayed Coupling

Cicolani,

Pignotti

2024

Math Methods in App Sciences

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We study a Hegselmann–Krause type opinion formation model for a system of two populations. The two groups interact with each other via subsets of individuals, namely, the leaders, and natural time delay effects are considered. By using careful estimates of the system's trajectories, we are able to prove an asymptotic convergence to consensus result. Some numerical tests illustrate the theoretical result and point out some possible applications.

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Bounded confidence opinion dynamics: A survey

Cited by 24 publications

References 129 publications

Opinion Models, Election Data, and Political Theory

Opinion Models, Election Data, and Political Theory

The roots of polarization in the individual reward system

Opinion Dynamics of Two Populations With Time‐Delayed Coupling

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