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How culture can affect opinion dynamics: the case of vaccination

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Abstract

Culture plays a pivotal role in shaping collective level processes, with implications for public opinion on issues such as COVID-19 vaccination. Drawing on Hofstede's cultural dimension theory, we theoretically examine the influence of two key dimensions of culture, individualism/collectivism and power distance, on the opinion formation of individuals within a dynamic and evolving context. While former models suggest that collectivism promotes opinion consensus, our findings reveal a more complex relationship, particularly in scenarios where centralization or decentralization of a society is considered. By conducting multiple simulation experiments through an agent-based model, we find that collectivism can contribute to opinion consensus in a simple scenario in which only social norms work without any impact from authorities. However, a collectivist society also has the potential to experience high opinion polarization in the presence of greater decentralization among authorities, particularly in settings of high power distance. Simulation results further demonstrate that disagreement between authorities is more likely to result in opinion polarization in individualist cultures compared to collectivist cultures.

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Data availability

The datasets generated and analyzed during the current study can be replicated by running the model implemented with NetLogo 6.0.4, which is openly available on the CoMSES Computational Model Library at: https://www.comses.net/codebase-release/800a4c2e-7a27-47d5-b06e-ddf81bf89cb7/.

Notes

  1. Strictly speaking the mRNA technology is not a vaccine, but for readability we will use this widely used terminology.

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Acknowledgements

The authors are grateful to the ICS members and Shaoni Wang from University College Groningen for their helpful feedback. The second author acknowledges financial support by the Netherlands Organization for Scientific Research (NWO) under the 2018 ORA grant ToRealSim (464.18.112).

Author information

Authors and Affiliations

  1. Department of Sociology/ICS, University of Groningen, Grote Rozenstraat 31, Groningen, 9712 TG, The Netherlands

    Teng Li & Andreas Flache

  2. College of Economics and Management, Huazhong Agricultural University, No. 1 Shizishan Street, Hongshan District, Wuhan, 430070, China

    Teng Li

  3. Groningen Center for Social Complexity Studies, University of Groningen, Hoendiepskade 23/24, Groningen, 9718 BG, The Netherlands

    Wander Jager

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Contributions

TL: Conceptualization, Writing—original draft, Investigation, Methodology, Software, Visualization. AF: Conceptualization, Writing – review & editing, Investigation, Methodology, Supervision. WJ: Writing – review & editing, Conceptualization, Investigation, Supervision.

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Correspondence to Teng Li.

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Appendices

Appendix 1: Establish social networks

The algorithm pseudocode to establish social networks between individuals and networks between authorities and individuals is presented below. The primary principle is that individuals form connections with geographically closest friends or authorities, with the model parameter Nf controlling the network's density.

figure a

Appendix 2: Long-term experiments

We present here supplementary experiments to illustrate whether different polarization outcomes are produced if the model is run for longer periods of time. This supplementary experiment is mainly an extension of Experiment 3 in the main text, considering the involvement of authority. The model run time has been extended to 1000 time steps, given the limitations of the model's running capacity, and the comparative analysis is sufficient to help predict the trend of the results.

Figure 

Fig. 17
figure 17

Change in polarization over time for different authorities with low, medium and high reach (ma). Polarization averaged across 20 repetitions; 95% confidence interval shown. Parameter settings: pdi = 1, idv = 1

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17 depicts the evolution of opinion polarization for three levels of authority reach under different numbers of authorities, which is an extension of Fig. 8. The main difference between the results and those of Fig. 8 is that the polarization eventually shows a decreasing trend after the extension of the time step, and the larger the authority reach the faster the decrease. Earlier analysis has indicated that this is due to the fact that individual opinions eventually shift towards authority opinion. When the authority reach is at its maximum of 100%, this shift towards equilibrium results is achieved first. It is expected that situations with smaller authority reach will also achieve similar outcomes given enough time. However, it is important to note that the fundamental conclusion of Fig. 8 remains unchanged even with the extension of time steps, i.e., the maximum level of polarization is achieved when authority reach is 40%.

Figure 

Fig. 18
figure 18

Change in polarization over time with different disagreements between two authorities. Polarization averaged across 20 repetitions; 95% confidence interval shown. Parameter settings: Na = 2; ma = 100%; pdi = 1, idv = 1

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18 is an extension of Fig. 10, which examines the long-term effect of authority disagreement on opinion polarization. Despite the increased time step, the results are not significantly different. Similarly, Fig. 

Fig. 19
figure 19

Change in polarization over time with different authorities. Polarization averaged across 20 repetitions; 95% confidence interval shown. Parameter settings: ma = 100%; pdi = 1, idv = 1

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19 extends the time step from Fig. 12, but the simulation results do not show a significant difference.

Appendix 3: Different initial opinion distributions

This supplementary experiment explores the effect of different initial opinion distributions on polarization. Taking the case of Na = 2 in Fig. 8 as an example, we analyze the outcomes of four initial distributions, namely the random distribution (achieved by Beta (1, 1)), the unimodal distribution with most values distributed around 0 (Beta (1, 10)) and distributed around 0.5 (Beta (10, 10)), and the bimodal distribution (formed by the combination of Beta (10, 2) and Beta (2, 10)). Figure 

Fig. 20
figure 20

Polarization averaged across 20 repetitions with 95% confidence interval over time. There are two authorities with low, medium and high reach (ma) under four initial distributions. Parameter settings: Na = 2; pdi = 1, idv = 1

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20 displays the polarization evolutions. The highest degree of polarization remains largely unchanged under the condition of 40% authority reach.

The results of the extended experiment for Fig. 10 are shown in Fig. 

Fig. 21
figure 21

Change in polarization over time with different disagreements between two authorities under four initial distributions. Polarization averaged across 20 repetitions; 95% confidence interval shown. Parameter settings: Na = 2; ma = 100%; pdi = 1, idv = 1

Full size image

21, and those for Fig. 12 are shown in Fig. 

Fig. 22
figure 22

Change in polarization over time with different authorities under four initial distributions. Polarization averaged across 20 repetitions; 95% confidence interval shown. Parameter settings: ma = 100%; pdi = 1, idv = 1

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22. Although the evolution curves for polarization differ, the overall conclusions have not been reversed.

Appendix 4: Further explanation of the effects of decentralization of authorities with the same opinion

In Fig. 8 of the main paper, we showed that when the authorities share the same opinion and the total number of connections remains constant, changing only the number of authorities (i.e., varying degrees of decentralization) results in differences in the level of polarization within the population. In the main text we have elaborated an explanation for this possibly counterintuitive result. In short, we attribute this outcome to the influence between individuals. In this appendix we report an additional simulation experiment designed to test this explanation. In this experiment, we set the network degree to zero for all individuals (for all i: Nif = 0) meaning there is no interaction between individuals, using an example where the authority reach is 40% (as shown in Fig. 

Fig. 23
figure 23

Initial settings with network degree set to zero for different numbers of authority. Parameter settings: ma = 40%, Nf = 0

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23). All other parameters are the same as in Experiment 3 (Fig. 8). If the interactions between individuals are responsible for the observed reduction in polarization with three authorities compared to one or two, this difference should disappear in this new experiment. Figure 

Fig. 24
figure 24

Change in opinion heterogeneity over time with different authority numbers. Opinion heterogeneity averaged across 20 repetitions; 95% confidence interval shown. Parameter settings: ma = 40%; pdi = 1, idv = 1; Nf = 0

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24 confirms this hypothesis.

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Li, T., Flache, A. & Jager, W. How culture can affect opinion dynamics: the case of vaccination. J Comput Soc Sc 8, 18 (2025). https://doi.org/10.1007/s42001-024-00347-7

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