Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
157 tokens/sec
GPT-4o
8 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

Dynamic opinion updating with endogenous networks (2405.01341v1)

Published 2 May 2024 in econ.TH

Abstract: Polarization is a well-documented phenomenon across a wide range of social issues. However, prevailing theories often compartmentalize the examination of herding behavior and opinion convergence within different contexts. In this study, we delve into the micro-foundations of how individuals strategically select reference groups, offering insight into a dynamic process where both individual opinions and the network evolve simultaneously. We base our model on two parameters: people's direct benefit from connections and their adaptability in adjusting their opinions. Our research highlights which conditions impede the network from achieving complete connectivity, resulting in enduring polarization. Notably, our model also reveals that polarization can transiently emerge during the transition towards consensus. We explore the connection between these scenarios and a critical network metric: the initial diameter, under specific conditions related to the initial distribution of opinions.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (34)
  1. Akerlof, G. A. and W. T. Dickens (1982). The economic consequences of cognitive dissonance. American Economic Review 72(3), 307–319.
  2. The welfare effects of social media. American Economic Review 110(3), 629–676.
  3. Dissonance minimization and conversation in social networks. Journal of Economic Behavior & Organization 215, 167–191.
  4. Four degrees of separation. In Proceedings of the 4th Annual ACM Web Science Conference, pp.  33–42. ACM.
  5. Badev, A. (2021). Nash equilibria on (un)stable networks. Econometrica 89(3), 1179–1206.
  6. Boucher, V. (2016). Conformism and self-selection in social networks. Journal of Public Economics 136, 30–44.
  7. Greater internet use is not associated with faster growth in political polarization among us demographic groups. Proceedings of the National Academy of Sciences 114(40), 10612–10617.
  8. Cross-country trends in affective polarization. Review of Economics and Statistics, 1–60.
  9. Chung, F. (2005). Laplacians and the cheeger inequality for directed graphs. Annals of Combinatorics 9(1), 1–19.
  10. Chung, F. R. (1996). Laplacians of graphs and cheeger’s inequalities. Combinatorics, Paul Erdos is Eighty 2(157-172), 13–2.
  11. Estimating network externalities in undirected link formation games. Technical report, Working paper.
  12. DeGroot, M. H. (1974). Reaching a consensus. Journal of the American Statistical Association 69(345), 118–121.
  13. Persuasion bias, social influence, and unidimensional opinions. The Quarterly Journal of Economics 118(3), 909–968.
  14. How polarized are citizens? measuring ideology from the ground-up. Measuring Ideology from the Ground-Up (June 1, 2020).
  15. On the measurement of polarization. Econometrica 62(4), 819–851.
  16. Festinger, L. (1957). A theory of cognitive dissonance, Volume 2. Stanford university press.
  17. The law of the few. The American Economic Review 100(4), 1468–1492.
  18. Social networks, confirmation bias and shock elections. arXiv preprint arXiv:2011.00520.
  19. Genicot, G. (2022). Tolerance and compromise in social networks. Journal of Political Economy 130(1), 94–120.
  20. Gentzkow, M. (2016). Polarization in 2016. Toulouse Network of Information Technology white paper.
  21. Golub, B. and M. O. Jackson (2010). Naïve learning in social networks and the wisdom of crowds. American Economic Journal: Microeconomics 2(1), 112–49.
  22. Golub, B. and M. O. Jackson (2012). How homophily affects the speed of learning and best-response dynamics. The Quarterly Journal of Economics 127(3), 1287–1338.
  23. Consensus and synchronization problems on small-world networks. Journal of Mathematical Physics 51(8), 082701.
  24. Opinion dynamics and bounded confidence models, analysis, and simulation. Journal of Artificial Societies and Social Simulation 5(3).
  25. A strategic model of social and economic networks. Journal of Economic Theory 71(1), 44–74.
  26. Kinateder, M. and L. P. Merlino (2017). Public goods in endogenous networks. American Economic Journal: Microeconomics 9(3), 187–212.
  27. Nestedness in networks: A theoretical model and some applications. Theoretical Economics 9(3), 695–752.
  28. Krause, U. (2000). A discrete nonlinear and non-autonomous model of consensus formation. Communications in Difference Equations, 227–236.
  29. Panebianco, F. (2014). Socialization networks and the transmission of interethnic attitudes. Journal of Economic Theory 150, 583–610.
  30. Convergence of cultural traits with time-varying self-confidence in the panebianco (2014) model–a corrigendum. Journal of Economic Theory 175, e3–e13.
  31. Powers, D. L. (1988). Graph partitioning by eigenvectors. Linear Algebra and its Applications 101, 121–133.
  32. Powers, D. L. (1989). Bounds on graph eigenvalues. Linear Algebra and its Applications 117, 1–6.
  33. Prummer, A. and J.-P. Siedlarek (2017). Community leaders and the preservation of cultural traits. Journal of Economic Theory 168, 143–176.
  34. Games on endogenous networks. arXiv preprint arXiv:2102.01587.

Summary

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

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now