Whither Now, Opinion Modelers?

Sobkowicz, Paweł

doi:10.3389/fphy.2020.587009

Cited by 19 publications

(17 citation statements)

References 165 publications

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“…However, they show that this is an area that is attracting increasing attention from researchers in a variety of disciplines. The popularity of the topic is also demonstrated by the number of recent review articles on opinion dynamics [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. Most opinion models can be classified into one of two main families: continuous or discrete opinion models [ 2 , 3 , 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Consensus, Polarization and Hysteresis in the Three-State Noisy q-Voter Model with Bounded Confidence

Doniec

Lipiecki

Sznajd-Weron

2022

Entropy

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In this work, we address the question of the role of the influence of group size on the emergence of various collective social phenomena, such as consensus, polarization and social hysteresis. To answer this question, we study the three-state noisy q-voter model with bounded confidence, in which agents can be in one of three states: two extremes (leftist and rightist) and centrist. We study the model on a complete graph within the mean-field approach and show that, depending on the size q of the influence group, saddle-node bifurcation cascades of different length appear and different collective phenomena are possible. In particular, for all values of q>1, social hysteresis is observed. Furthermore, for small values of q∈(1,4), disagreement, polarization and domination of centrists (a consensus understood as the general agreement, not unanimity) can be achieved but not the domination of extremists. The latter is possible only for larger groups of influence. Finally, by comparing our model to others, we discuss how a small change in the rules at the microscopic level can dramatically change the macroscopic behavior of the model.

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Section: Introductionmentioning

confidence: 99%

Consensus, Polarization and Hysteresis in the Three-State Noisy q-Voter Model with Bounded Confidence

Doniec

Lipiecki

Sznajd-Weron

2022

Entropy

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“…Most often, in the models the opinions change fast and significantly, typically converging towards an equilibrium state, whereas in real data there are plenty of examples of opinion distributions that remain almost constant and continue to change very slowly. This suggests that, as recently observed 67 , most of the actual social dynamics lead to transient, non-equilibrium phenomena: ad-hoc models should be developed to capture this effect. In this context, understanding the timescale of phenomena influencing opinion formation is crucial to map the time of model simulation to the time of real-world opinion evolution, a still unresolved challenge 67 .…”

Section: Discussionmentioning

confidence: 81%

“…This suggests that, as recently observed 67 , most of the actual social dynamics lead to transient, non-equilibrium phenomena: ad-hoc models should be developed to capture this effect. In this context, understanding the timescale of phenomena influencing opinion formation is crucial to map the time of model simulation to the time of real-world opinion evolution, a still unresolved challenge 67 . 4.…”

Section: Discussionmentioning

confidence: 81%

“…characteristic qualitative distributions, which should be taken into account when evaluating opinion formation models. The critical role of initial conditions in opinion formation models has been pointed out as a long-overlooked problem67 : different initial conditions can lead to grossly different final states and it is then fundamental to assign the initial opinion distribution appropriately, which remains an open challenge, in particular when large-scale interaction networks are considered.…”

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confidence: 99%

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A framework to analyze opinion formation models

Devia

Giordano

2022

Preprint

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Comparing model predictions with real data is crucial to improve and validate a model. For opinion formation models, validation based on real data is uncommon and difficult to obtain, also due to the lack of systematic approaches for a meaningful comparison. We introduce a framework to assess opinion formation models, which can be used to determine the qualitative outcomes that an opinion formation model can produce, and compare model predictions with real data. The proposed approach relies on a histogram-based classification algorithm, and on transition tables. The algorithm classifies an opinion distribution as perfect consensus, consensus, polarization, clustering, or dissensus; these qualitative categories were identified from World Values Survey data. The transition tables capture the qualitative evolution of the opinion distribution between an initial and a final time. We compute the real transition tables based on World Values Survey data from different years, as well as the predicted transition tables produced by the French-DeGroot, Weighted-Median, and Bounded Confidence models, and we compare them. Our results provide insight into the evolution of real-life opinions and highlight key directions to improve opinion formation models.

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“…However, there are still many aspects in opinion dynamics that have been introduced only in occasional models and where community efforts are very much needed [31]. And most models tend to be comparable with similar implementations only.…”

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confidence: 99%

Agent mental models and Bayesian rules as a tool to create opinion dynamics models

Martins¹

2021

Preprint

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Traditional opinion dynamics models are simple and yet, enough to explore the consequences in basic scenarios. But, to better describe problems such as polarization and extremism, we might need to include details about human biases and other cognitive characteristics. In this paper, I explain how we can describe and use mental models and assumptions of the agents using Bayesian-inspired model building. The relationship between human rationality and Bayesian methods will be explored, and we will see that Bayesian ideas can indeed be used to explain how humans reason. We will see how to use Bayesian-inspired rules using the simplest version of the Continuous Opinions and Discrete Actions (CODA) model. From that, we will explore how we can obtain update rules that include human behavioral characteristics such as confirmation bias, motivated reasoning, or our tendency to change opinions much less than we should.

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Whither Now, Opinion Modelers?

Cited by 19 publications

References 165 publications

Consensus, Polarization and Hysteresis in the Three-State Noisy q-Voter Model with Bounded Confidence

Consensus, Polarization and Hysteresis in the Three-State Noisy q-Voter Model with Bounded Confidence

A framework to analyze opinion formation models

Agent mental models and Bayesian rules as a tool to create opinion dynamics models

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