Will Trump win again in the 2020 election? An answer from a sociophysics model

Galam, Serge

doi:10.1016/j.physa.2021.125835

Cited by 11 publications

(8 citation statements)

References 32 publications

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“…Opinion dynamics, being one of the essential branches of sociophysics, studies the statistical physics of collective opinion evolution driven by microscopic rules of individuals [ 1 ]. Opinion dynamics models can be broadly classified into two categories concerning the opinion space [ 2 ]: the discrete opinion space [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ], and the continuous opinion space [ 12 , 13 , 14 , 15 , 16 ]. The models based on discrete opinion space usually assume two opposing opinions in the system (e.g., +1, −1, or A, B, etc.).…”

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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“…Opinion dynamics, being one of the essential branches of sociophysics, studies the statistical physics of collective opinion evolution driven by microscopic rules of individuals [1]. Opinion dynamics models can be broadly classified into two categories concerning the opinion space [2]: the discrete opinion space [3][4][5][6][7][8][9][10][11], and the continuous opinion space [12][13][14][15][16]. The models based on discrete opinion space usually assume two opposing opinions in the system (e.g., +1, -1, or A, B, etc.).…”

Section: Introductionmentioning

confidence: 99%

“…The models based on discrete opinion space usually assume two opposing opinions in the system (e.g., +1, -1, or A, B, etc.). The classic discrete opinion dynamics models include the voter model [3][4][5], the Sznajd model [6][7][8], and the Galam model [9][10][11]. Another class of models is based on continuous opinion space, where an individual's opinion is measured by a real number between 0 and 1, inclusive.…”

Section: Introductionmentioning

confidence: 99%

Opinion Dynamics with Higher-order Bounded Confidence

Wang¹

2022

Preprint

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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 convenient to combine with other higher-order dynamics, from the contagion process to evolutionary dynamics.

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“…Versions of this model have been used to predict surprising election results, such as the French rejection of the European constitution in 2005 [30], the Brexit vote [20], and Trump's election for president in 2016 [19]. However, the prediction of a second Trump victory in 2020 failed by a short margin [21]. From a mathematical point of view, these scenarios can be modelled by the repeated application of an update equation that takes as input the current distribution of opinions, given by the proportion of individuals with opinion A, and outputs the new proportion of individuals with opinion A.…”

Section: Introductionmentioning

confidence: 99%

Deviations from the Majority: A Local Flip Model

Toth,

Galam

2021

Preprint

Self Cite

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We study the effect of local distortions to the majority rule on the dynamics of opinions using an extension of the Galam model. At each iteration of the local updates of opinion, the new model accounts for different probabilities of a local flip against the local majority as a function of the ratio of majority / minority within the discussing group. Depending of those probabilities, the model exhibits a wide variety of patterns which include new features in the topology of the landscape driving the dynamics. In particular, we uncover a rich interplay between attractors and tipping points coupled with both monotonic and alternating dynamics. The cases of group sizes 3 and 5 are investigated in detail, and we find regimes that feature competition between three attractors for size 5. Larger groups are also analysed. The local flip model also applies to the study of bottom-top hierarchical voting, where each group elects a representative at the next higher level according to the local majority in the group. The local flip corresponds to a representative who decides to vote against the choice of their electing group, i.e. a 'faithless elector'. The results shed a new light on a series of social phenomena triggered by one single individual who acts against the local majority.

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Will Trump win again in the 2020 election? An answer from a sociophysics model

Cited by 11 publications

References 32 publications

Opinion Dynamics with Higher-Order Bounded Confidence

Opinion Dynamics with Higher-Order Bounded Confidence

Opinion Dynamics with Higher-order Bounded Confidence

Deviations from the Majority: A Local Flip Model

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