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Voting contagion: Modeling and analysis of a century of U.S. presidential elections

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  • Dan Braha
  • Marcus A M de Aguiar

Abstract

Social influence plays an important role in human behavior and decisions. Sources of influence can be divided as external, which are independent of social context, or as originating from peers, such as family and friends. An important question is how to disentangle the social contagion by peers from external influences. While a variety of experimental and observational studies provided insight into this problem, identifying the extent of contagion based on large-scale observational data with an unknown network structure remains largely unexplored. By bridging the gap between the large-scale complex systems perspective of collective human dynamics and the detailed approach of social sciences, we present a parsimonious model of social influence, and apply it to a central topic in political science—elections and voting behavior. We provide an analytical expression of the county vote-share distribution, which is in excellent agreement with almost a century of observed U.S. presidential election data. Analyzing the social influence topography over this period reveals an abrupt phase transition from low to high levels of social contagion, and robust differences among regions. These results suggest that social contagion effects are becoming more instrumental in shaping large-scale collective political behavior, with implications on democratic electoral processes and policies.

Suggested Citation

  • Dan Braha & Marcus A M de Aguiar, 2017. "Voting contagion: Modeling and analysis of a century of U.S. presidential elections," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-30, May.
    • Handle: RePEc:plo:pone00:0177970
    DOI: 10.1371/journal.pone.0177970
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    Cited by:

    1. Edoardo Gallo & Alastair Langtry, 2020. "Social networks, confirmation bias and shock elections," Papers 2011.00520, arXiv.org.
    2. Shang, Yilun, 2018. "Hybrid consensus for averager–copier–voter networks with non-rational agents," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 244-251.
    3. Schadner, Wolfgang, 2022. "U.S. Politics from a multifractal perspective," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    4. Di Benedetto, Andrea & Wieners, Claudia E. & Dijkstra, Henk A. & Stoof, Henk T.C., 2023. "Media preference increases polarization in an agent-based election model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    5. Sergio Pinto & Panka Bencsik & Tuugi Chuluun & Carol Graham, 2021. "Presidential Elections, Divided Politics, and Happiness in the USA," Economica, London School of Economics and Political Science, vol. 88(349), pages 189-207, January.
    6. Carpentras, Dino & Quayle, Michael, 2022. "Propagation of measurement error in opinion dynamics models: The case of the Deffuant model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    7. Rytis Kazakeviv{c}ius & Aleksejus Kononovicius, 2023. "Anomalous diffusion and long-range memory in the scaled voter model," Papers 2301.08088, arXiv.org, revised Feb 2023.
    8. Gallo, E. & Langtry, A., 2020. "Social Networks, Confirmation Bias and Shock Elections," Cambridge Working Papers in Economics 2099, Faculty of Economics, University of Cambridge.
    9. Rytis Kazakevicius & Aleksejus Kononovicius & Bronislovas Kaulakys & Vygintas Gontis, 2021. "Understanding the nature of the long-range memory phenomenon in socioeconomic systems," Papers 2108.02506, arXiv.org, revised Aug 2021.

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