Is the Voter Model a Model for Voters?

Abstract

The voter model has been studied extensively as a paradigmatic opinion dynamics' model. However, its ability for modeling real opinion dynamics has not been addressed. We introduce a noisy voter model (accounting for social influence) with agents' recurrent mobility (as a proxy for social context), where the spatial and population diversity are taken as inputs to the model. We show that the dynamics can be described as a noisy diffusive process that contains the proper anisotropic coupling topology given by population and mobility heterogeneity. The model captures statistical features of the US presidential elections as the stationary vote-share fluctuations across counties, and the long-range spatial correlations that decay logarithmically with the distance. Furthermore, it recovers the behavior of these properties when the geographical space is coarse-grained at different scales from the county level through congressional districts and up to states. Finally, we analyze the role of the mobility range and the randomness in decision making which are consistent with the empirical observations. Opinion dynamics focuses on the way different options compete in a population, giving raise to either consensus (every individual holding the same opinion or option) or coexistence of several opinions. Many theoretical efforts have been devoted to clarify the implications on the macroscopic outcome, among other aspects, of different interaction mechanisms, different topologies of the interaction networks, the inclusion of opinion leaders or of zealots, external fields [1,2]. To advance our understanding on social phenomena these theoretical efforts need to be complemented with empirical [3-5] and experimental results [6][7][8][9]. In this context elections offer an opportunity for contrasting opinion dynamics' models with empirical results [10]. On one hand, the data are publicly available in many countries, with a good level of spatial resolution and several temporal observations. On the other hand, there is evidence that voting behavior is strongly influenced by the social context of the individuals [6,7,9,[11][12][13][14][15][16][17][18][19][20]. Thus it is natural to model electoral processes as systems of interacting agents with the aim of explaining the statistical regularities [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38], as for example, the universal scaling of the distribution of votes in proportional elections [21,22] or signatures of irregularities in the democratic process [23,26].In this work we asses the capacity of the voter model to capture real voter choices and propose a microscopic foundation for modeling voting behavior in elections. The model is based on social influence and recurrent mobility (SIRM): social influence will be modeled as a noisy voter model, while recurrent mobility serves as a proxy of the social context. In the voter model each agent updates its state by randomly copying the opinion of one of its neighbors [39][40][41]. We will consider that agents interact a...

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

“…In their most basic form, these models consist of voters, represented by nodes on a social network, having only two possible opinions, 0 or 1. Each voter may change her mind by using various interaction mechanisms, for example, randomly adopting the opinion of a connected neighbor (essentially a noisy majority-vote rule, see [3, 17, 58]), or by applying local majority rules [1, 3, 5]. The stochastic dynamics of these simple interaction models ultimately leads to a uniform state corresponding to the all-nodes-0 or all-nodes-1 states where all voters share the same political choices.…”

“…Obviously, consensus states are not observed in real-world political elections, and thus the basic models cannot be plausibly considered as realistic models that are able to describe empirical voting data. Accordingly, more realistic models of opinion dynamics have been proposed that incorporate, among other features, social impact theory [60–62], opinion leaders and zealots [18–19, 29–38, 62–63], external influences and fields [2, 18–19, 64–70], individual’s biases [71–72], contrarians [73], individual’s own current opinion [74–75], word-of-mouth spreading [52], non-overlapping cliques [59], or noisy diffusive process [58]. Below we further elaborate on the themes of opinion leaders and zealots, external influences, and individual’s biases—themes that play an important role in our model, and that have been seen empirically by studies of electoral behavior (see Introduction).…”

Evolution of cooperation in Axelrod tournament using cellular automata

“…First fitness function is the ''random'', that is, when an individual dies, the new individual will choose any of the strategies among its neighbors. If we consider the after death characteristic of such approach, it may be partially interpreted either as a voter model [30] or a death-birth process [31].…”

Noise-induced polarization switching in complex networks

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