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Persistence in Voting Behavior: Stronghold Dynamics in Elections

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9021))

Abstract

Influence among individuals is at the core of collective social phenomena such as the dissemination of ideas, beliefs or behaviors, social learning and the diffusion of innovations. Different mechanisms have been proposed to implement inter-agent influence in social models from the voter model, to majority rules, to the Granoveter model. Here we advance in this direction by confronting the recently introduced Social Influence and Recurrent Mobility (SIRM) model, that reproduces generic features of vote-shares at different geographical levels, with data in the US presidential elections. Our approach incorporates spatial and diversity as inputs for the opinion dynamics while individuals’ mobility provides a proxy for social context, and peer imitation accounts for social influence. The model captures the observed stationary background fluctuations in the vote-shares across counties. We study the so-called political strongholds, i.e., locations where the votes-shares for a party are systematically higher than average. A quantitative definition of a stronghold by means of persistence in time of fluctuations in the voting spatial distribution is introduced, and results from the US Presidential Elections during the period 1980-2012 are analyzed within this framework. We compare electoral results with simulations obtained with the SIRM model finding a good agreement both in terms of the number and the location of strongholds. The strongholds duration is also systematically characterized in the SIRM model. The results compare well with the electoral results data revealing an exponential decay in the persistence of the strongholds with time.

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Correspondence to Toni Pérez .

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Pérez, T., Fernández-Gracia, J., Ramasco, J.J., Eguíluz, V.M. (2015). Persistence in Voting Behavior: Stronghold Dynamics in Elections. In: Agarwal, N., Xu, K., Osgood, N. (eds) Social Computing, Behavioral-Cultural Modeling, and Prediction. SBP 2015. Lecture Notes in Computer Science(), vol 9021. Springer, Cham. https://doi.org/10.1007/978-3-319-16268-3_18

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  • DOI: https://doi.org/10.1007/978-3-319-16268-3_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16267-6

  • Online ISBN: 978-3-319-16268-3

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