Modeling Confirmation Bias and Peer Pressure in Opinion Dynamics

Liu, Longzhao; Wang, Xin; Chen, Xuyang; Tang, Shaoting; Zheng, Zhiming

doi:10.3389/fphy.2021.649852

Cited by 12 publications

(9 citation statements)

References 52 publications

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“…Some of them introduced various new factors [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ], such as the opinion leader [ 23 ], the memory [ 24 ], the expression and private opinion [ 25 ], the fuzzy inference [ 26 ], the stubbornness [ 27 , 28 , 29 ], and the noise [ 23 , 30 , 31 , 32 , 33 ], to the classic bounded confidence model. Others consider different possibilities of evolutionary mechanisms of the system [ 27 , 34 , 35 , 36 , 37 , 38 , 39 ], such as the heterogeneous bounded confidence [ 27 , 34 , 35 ], the heterogeneous pressure [ 36 , 37 ], and the circular opinion space [ 38 , 39 ]. One of the most important topics in the bounded confidence model is how to promote the opinion consensus.…”

Section: Introductionmentioning

confidence: 99%

Opinion Dynamics with Higher-Order Bounded Confidence

Wang

2022

Entropy

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The higher-order interactions in complex systems are gaining attention. Extending the classic bounded confidence model where an agent’s opinion update is the average opinion of its peers, this paper proposes a higher-order version of the bounded confidence model. Each agent organizes a group opinion discussion among its peers. Then, the discussion’s result influences all participants’ opinions. Since an agent is also the peer of its peers, the agent actually participates in multiple group discussions. We assume the agent’s opinion update is the average over multiple group discussions. The opinion dynamics rules can be arbitrary in each discussion. In this work, we experiment with two discussion rules: centralized and decentralized. We show that the centralized rule is equivalent to the classic bounded confidence model. The decentralized rule, however, can promote opinion consensus. In need of modeling specific real-life scenarios, the higher-order bounded confidence is more convenient to combine with other higher-order interactions, from the contagion process to evolutionary dynamics.

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Section: Introductionmentioning

confidence: 99%

Opinion Dynamics with Higher-Order Bounded Confidence

Wang

2022

Entropy

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“…Due to its importance there have been efforts to integrate this bias in opinion dynamics models [17,18,19]. More specifically, in [20] the authors proposed a model where agents ignore opinions that are too different from their own.…”

Section: Introductionmentioning

confidence: 99%

Impact of memory and bias in kinetic exchange opinion models on random networks

Oestereich,

Crokidakis,

Cajueiro

2022

Preprint

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In this work we consider the effects of memory and bias in kinetic exchange opinion models. We propose a model in which agents remember the sign of their last interaction with each one of their pairs. This introduces memory effects in the model, since past interactions can affect future ones. We have also considered the impact of a parameter p that regulates how often an agent changes its interaction to match its opinion, thus introducing bias in the interactions. For high values of p an agent is more likely to start having a negative interaction with an agent of opposing opinion and a positive interaction with an agent of the same opinion. The model is defined on the top of random networks with mean connectivity k . We analyze the impact of both p and k on the emergence of ordered and disordered states in the population. Our results suggest a rich phenomenology regarding critical phenomena, with the presence of metastable states and a non-monotonic behavior of the order parameter. We show that the fraction of neutral agents in the disordered state decreases as the bias p increases.

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“…Two of the main factors that shape people's opinions are confirmation bias and social influence [84]. According to Vicario et al [125], the observed polarization of offline and online communities might be the result of the conjugate effect of these two forces.…”

Section: Introductionmentioning

confidence: 99%

Network polarization, filter bubbles, and echo chambers: An annotated review of measures, models, and case studies

Interian¹,

Marzo²,

Bernardini³

et al. 2022

Preprint

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Polarization arises when the underlying network connecting the members of a society is formed by highly connected groups with weak intergroup connectivity. The increasing polarization, the strengthening of echo chambers, and the isolation caused by information filters in social networks are increasingly attracting the attention of researchers from different areas of knowledge such as computer science, economics, social and political sciences. Despite hundreds of publications in this area, there was little effort to systematize or present the knowledge developed in the field in an organized way. This study presents an annotated review of network polarization measures, models used to handle existing polarization, their applications, and case studies. Altogether, 405 scientific articles and conference papers were examined, with 74 filtered for this review. Several approaches for measuring polarization in graphs and networks were identified, including those based on homophily, modularity, random walks, and balance theory. The models used for reducing polarization included methods that propose edge or node editions (including edge insertions or deletions, and edge weight modifications), changes in social network design, or changes in the recommendation systems embedded in these networks. This review will be helpful to researchers investigating polarized social networks from a theoretical and applied perspective.

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Modeling Confirmation Bias and Peer Pressure in Opinion Dynamics

Cited by 12 publications

References 52 publications

Opinion Dynamics with Higher-Order Bounded Confidence

Opinion Dynamics with Higher-Order Bounded Confidence

Impact of memory and bias in kinetic exchange opinion models on random networks

Network polarization, filter bubbles, and echo chambers: An annotated review of measures, models, and case studies

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