Abstract Survey research examining social influence in political behavior has often relied on name generators to measure individuals’ social networks. As the term implies, name generators ask respondents for a list of their network ties—these batteries often focus on core networks, or the handful of intimate contacts with whom the individual discusses important matters. Recent work has broadened its focus from these small, dense, core networks to larger, but less intimate acquaintance networks; this is the broader set of people an individual knows on a first-name basis, and these contacts have been measured with aggregated relational data (ARD) batteries. In this paper we use data from the Civic Health and Institutions Project (CHIP50) and a series of Cooperative Election Study (CES) modules fielded in 2018 and 2024 to examine the reliability and validity of acquaintance network data. Our analysis pays particular attention to points of similarity and distinction between ARD measures and standard name generators, and we close by offering some suggestions to researchers studying patterns of interpersonal interaction in mass publics.
Abstract Attitude change is not solely an individual process—it unfolds within social networks. While interventions to shift individuals’ political attitudes are widespread, from campaign persuasion to efforts reducing prejudice and partisan animosity, little is known about how such interventions depend on and interact with social structures. We report results from a field experiment in sixteen Ugandan villages that provides a rare, direct view of how attitude change occurs within social networks. We combine measures of village social networks with surveys of attitudes before and after a perspective-taking intervention seeking to warm attitudes toward nearby refugees. Targeted individuals did exhibit warmer attitudes toward refugees immediately after treatment on average. Two to three weeks later, however, substantial additional change had occurred throughout the villages—even among those never treated directly. We show evidence that these attitude shifts occurred within and because of social networks. Attitudes of network neighbors moved toward one another, even more than would be expected by common positive shifts. The new attitudes of targeted individuals spill over to influence the attitudes of network neighbors. Some people react to the intervention especially strongly right away; network distance to these people predicts longer-term attitude change, where attitudes of individuals closer to strong positive or negative reactors shift in the corresponding direction over time. These patterns do not predict immediate responses, only later ones after people have had time to interact with their network ties. Our findings demonstrate that political attitude change is ultimately shaped by real-world social networks.
Abstract As ethnic diversity increases in Western countries, concerns about negative outgroup attitudes and interethnic polarization are growing. In diverse classrooms, processes such as peer influence and homophilic friend selection contribute to changing outgroup attitudes. This study explores if and when individual attitude shifts driven by these processes might scale up to intergroup hostility in empirical multiethnic school cohorts that initially have a rather positive interethnic climate. Using an agent-based model calibrated with longitudinal network and attitude data, we conduct a series of computational experiments to assess the effects of hypothetical macro-level shocks, such as violent incidents where a minority group is associated with the perpetrators. Our findings indicate that correlational peer influence on attitudes towards Turks, when pushed to extreme parameter values, can over time magnify even small outgroup biases, leading to interethnic hostility. This dynamic is accelerated under strong ethnic homophily. We further provide a detailed analysis of how these results emerge from the empirical context and the interacting processes modeled.
Plant xylem consists of a network of interconnected vessels, through which water is transported under negative pressure. Filling of vessels with air, or embolism, disturbs this transport process and, in extreme cases, leads to tree mortality. Despite this significance, embolism propagation dynamics are still poorly understood, primarily because xylem is opaque to direct observation. Furthermore, existing models of embolism spreading build excessively on physiological and anatomical parameters, and many misrepresent the intervessel pit membrane as a 2D surface. Here, we first extend these physiological models by implementing the pit membrane as a 3D object. Then, we introduce a susceptible-infected (SI) model, a simple stochastic model for tracking spreading through a population, for embolism propagation. After correctly fitting the spreading probability, our SI model reproduces vulnerability curves produced by both the physiological model and empirical data, highlighting that the SI model can address embolism spreading dynamics in plant species, for which detailed physiological data are not available. Furthermore, relating the SI model to the physiological one allows interpreting embolism spreading as a directed percolation process. Elucidating the exact mapping between directed percolation and embolism spreading will likely yield new fundamental insights into the relationships between xylem network architecture and embolism dynamics.
Social network experiments provide a powerful framework for identifying causal network effects but also allow a specific form of network endogeneity. Random assignment eliminates the correlation between individual differences and treatment assignment but not between individual differences and treatment response. Individual differences can shape how participants enact their assigned networks. We use data from three networks to demonstrate an underappreciated approach for estimating causal network effects in the presence of endogeneity. The pre-experiment network captures individual differences, the treatment network defines the assigned structure, and the behavioral network reflects the interactions that occur during the experiment. Because the treatment network is exogenously assigned, it can serve as an instrument for the behavioral network, isolating the causal component of behavioral network effects. Using data from a coordination experiment, we estimate the causal effect of brokerage in the behavioral network on an important team outcome: perceived leadership. We also examine the influence of pre-experiment networks, finding that individuals who enter with closed networks sometimes emerge as brokers. The result shows that behavioral networks form through interdependent choices and interactions among multiple individuals and that endogenous network structures can generate effects beyond the control or intentions of any single individual.
