Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
139 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

The Continuous-Time Weighted-Median Opinion Dynamics (2404.16318v2)

Published 25 Apr 2024 in eess.SY and cs.SY

Abstract: Opinion dynamics models are important in understanding and predicting opinion formation processes within social groups. Although the weighted-averaging opinion-update mechanism is widely adopted as the micro-foundation of opinion dynamics, it bears a non-negligibly unrealistic implication: opinion attractiveness increases with opinion distance. Recently, the weighted-median mechanism has been proposed as a new microscopic mechanism of opinion exchange. Numerous advancements have been achieved regarding this new micro-foundation, from theoretical analysis to empirical validation, in a discrete-time asynchronous setup. However, the original discrete-time weighted-median model does not allow for "compromise behavior" in opinion exchanges, i.e., no intermediate opinions are created between disagreeing agents. To resolve this problem, this paper propose a novel continuous-time weighted-median opinion dynamics model, in which agents' opinions move towards the weighted-medians of their out-neighbors' opinions. It turns out that the proof methods for the original discrete-time asynchronous model are no longer applicable to the analysis of the continuous-time model. In this paper, we first establish the existence and uniqueness of the solution to the continuous-time weighted-median opinion dynamics by showing that the weighted-median mapping is contractive on any graph. We also characterize the set of all the equilibria. Then, by leveraging a new LaSalle invariance principle argument, we prove the convergence of the continuous-time weighted-median model for any initial condition and derive a necessary and sufficient condition for the convergence to consensus.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (30)
  1. J. R. P. French Jr., “A formal theory of social power,” Psychological Review, vol. 63, no. 3, pp. 181–194, 1956.
  2. M. H. DeGroot, “Reaching a consensus,” Journal of the American Statistical Association, vol. 69, no. 345, pp. 118–121, 1974.
  3. A. V. Proskurnikov and R. Tempo, “A tutorial on modeling and analysis of dynamic social networks. Part I,” Annual Reviews in Control, vol. 43, pp. 65–79, 2017.
  4. W. Mei, F. Bullo, G. Chen, J. Hendrickx, and F. Dörfler, “Micro-foundation of opinion dynamics: Rich consequences of an inconspicuous change,” Physical Review Research, vol. 4, no. 2, p. 023213, 2022.
  5. W. Mei, J. M. Hendrickx, G. Chen, F. Bullo, and F. Dörfler, “Convergence, consensus and dissensus in the weighted-median opinion dynamics,” IEEE Transactions on Automatic Control, 2024.
  6. A. V. Proskurnikov and R. Tempo, “A tutorial on modeling and analysis of dynamic social networks. Part II,” Annual Reviews in Control, vol. 45, pp. 166–190, 2018.
  7. B. D. O. Anderson and M. Ye, “Recent advances in the modelling and analysis of opinion dynamics on influence networks,” International Journal of Automation and Computing, vol. 16, no. 2, pp. 129–149, 2019.
  8. M. Grabisch and A. Rusinowska, “A survey on nonstrategic models of opinion dynamics,” Games, vol. 11, no. 4, p. 65, 2020.
  9. M. Granovetter, “Threshold models of collective behavior,” The Americal Journal of Sociology, vol. 83, no. 6, pp. 1420–1443, 1978.
  10. T. C. Schelling, “Dynamic models of segregation,” Journal of Mathematical Sociology, vol. 1, no. 2, pp. 143–186, 1971.
  11. P. Clifford and A. Sudbury, “A model for spatial conflict,” Biometrika, vol. 60, no. 3, pp. 581–588, 1973.
  12. R. A. Holley and T. M. Liggett, “Ergodic theorems for weakly interacting infinite systems and the voter model,” The Annals of Probability, vol. 3, no. 4, pp. 643–663, 1975.
  13. C. Castellano, M. A. Muñoz, and R. Pastor-Satorras, “Nonlinear q-voter model,” Physical Review E, vol. 80, no. 4, p. 041129, 2009.
  14. B. L. Granovsky and N. Madras, “The noisy voter model,” Stochastic Processes and their Applications, vol. 55, no. 1, pp. 23–43, 1995.
  15. V. Sood and S. Redner, “Voter model on heterogeneous graphs,” Physical review letters, vol. 94, no. 17, p. 178701, 2005.
  16. W. Ren and R. W. Beard, “Consensus seeking in multiagent systems under dynamically changing interaction topologies,” IEEE Transactions on Automatic Control, vol. 50, no. 5, pp. 655–661, 2005.
  17. A. Nedić and J. Liu, “On convergence rate of weighted-averaging dynamics for consensus problems,” IEEE Transactions on Automatic Control, vol. 62, no. 2, pp. 766–781, 2017.
  18. N. E. Friedkin and E. C. Johnsen, “Social influence and opinions,” Journal of Mathematical Sociology, vol. 15, no. 3-4, pp. 193–206, 1990.
  19. R. Hegselmann and U. Krause, “Opinion dynamics and bounded confidence models, analysis, and simulations,” Journal of Artificial Societies and Social Simulation, vol. 5, no. 3, 2002. [Online]. Available: http://jasss.soc.surrey.ac.uk/5/3/2.html
  20. C. Bernardo, C. Altafini, A. Proskurnikov, and F. Vasca, “Bounded confidence opinion dynamics: A survey,” Automatica, vol. 159, p. 111302, 2024.
  21. C. Altafini, “Consensus problems on networks with antagonistic interactions,” IEEE Transactions on Automatic Control, vol. 58, no. 4, pp. 935–946, 2013.
  22. S. E. Parsegov, A. V. Proskurnikov, R. Tempo, and N. E. Friedkin, “Novel multidimensional models of opinion dynamics in social networks,” IEEE Transactions on Automatic Control, vol. 62, no. 5, pp. 2270–2285, 2017.
  23. N. E. Friedkin, A. V. Proskurnikov, R. Tempo, and S. E. Parsegov, “Network science on belief system dynamics under logic constraints,” Science, vol. 354, no. 6310, pp. 321–326, 2016.
  24. A. Bizyaeva, A. Franci, and N. E. Leonard, “Nonlinear opinion dynamics with tunable sensitivity,” IEEE Transactions on Automatic Control, vol. 68, no. 3, pp. 1415–1430, 2023.
  25. A. Jadbabaie, A. Sandroni, and A. Tahbaz-Salehi, “Non-Bayesian social learning,” Games and Economic Behavior, vol. 76, no. 1, pp. 210–225, 2012.
  26. D. Acemoglu and A. Ozdaglar, “Opinion dynamics and learning in social networks,” Dynamic Games and Applications, vol. 1, no. 1, pp. 3–49, 2011.
  27. F. Amblard and G. Deffuant, “The role of network topology on extremism propagation with the relative agreement opinion dynamics,” Physica A: Statistical Mechanics and its Applications, vol. 343, pp. 725–738, 2004. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0378437104008581
  28. P. Jia, A. MirTabatabaei, N. E. Friedkin, and F. Bullo, “Opinion dynamics and the evolution of social power in influence networks,” SIAM Review, vol. 57, no. 3, pp. 367–397, 2015.
  29. M. Li and H. Dankowicz, “Impact of temporal network structures on the speed of consensus formation in opinion dynamics,” Physica A: Statistical Mechanics and its Applications, vol. 523, pp. 1355–1370, 2019.
  30. J. P. LaSalle, “Stability theory for ordinary differential equations,” Journal of Differential Equations, vol. 4, pp. 57–65, 1968.
Citations (1)

Summary

We haven't generated a summary for this paper yet.

X Twitter Logo Streamline Icon: https://streamlinehq.com