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

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.

社会影响力在人类行为与决策中发挥着举足轻重的作用。影响源可分为两类：一类是独立于社会情境的外部影响，另一类则源自同伴群体，诸如家人与朋友。学界亟待解决的一个核心问题是，如何将同伴引发的社会传染效应与外部影响区分开来。尽管已有大量实验与观测研究对该问题提供了洞见，但基于未知网络结构的大规模观测数据来量化传染效应的程度，在很大程度上仍未得到充分探索。本研究通过衔接集体人类动力学的大规模复杂系统视角与社会科学的精细化研究范式，提出了一个简约的社会影响力模型，并将其应用于政治学的核心议题——选举与投票行为。我们推导得到了县一级得票率分布的解析表达式，该结果与近一个世纪以来的美国大选观测数据高度契合。通过分析该时期内的社会影响力格局，我们发现社会传染水平存在从低到高的突变相变，且不同区域间存在显著且稳健的差异。上述结果表明，社会传染效应在塑造大规模集体政治行为中的作用愈发关键，该发现对民主选举进程与政策制定具有重要的借鉴意义。