Ego networks are thought to be influenced by the opportunities provided to associate with others given by our master statuses (e.g., race or sex), by the preferences individuals possess for interaction given our personality traits (e.g., extroverted or neurotic), and by the capacity to manage interactions on an ongoing basis given our cognitive ability to recall network information. However, prior research has been unable to examine all three classes of predictors concurrently. We rectify this deficiency in the literature by using a novel dataset of nearly 1000 respondents collected using controlled laboratory designs; using this dataset, we can simultaneously examine the impact of master statuses, personality traits, and social cognitive competencies on ego network size, structure (i.e., density), and composition (i.e., diversity). We find that all classes of predictors influence our ego networks, though in different ways, and point to new avenues for research into human sociability.
Evidence from 184 countries over the span of 25 years is gathered and analyzed to understand North–North, South–South, and North–South international migration flows. Conceptually, the analysis borrows from network theory and Migration Systems Theory (MST) to develop a model to characterize the structure and evolution of international migration flows. Methodologically, the Stochastic Actor-oriented Model of network dynamics is used to jointly model the three types of flows under analysis. Results show that endogenous network effects at the monadic, dyadic, and triadic levels of analysis are relevant to understand the emergence and evolution of migration flows. The findings also show that a core set of non-network covariates, suggested by MST as key drivers of migration flows, does not always explain migration dynamics in the systems under analysis in a consistent fashion; thus, suggesting the existence of important levels of heterogeneity inherent to these three types of flows. Finally, evidence related to the role of political instability and countries’ care deficits is also discussed as part of the analysis. Overall, the results highlight the importance of analyzing flows across the globe beyond typically studied migratory corridors (e.g., North–South flows) or regions (e.g., Europe).
Advances in computation have opened new vistas for modeling of sociodemographic niches and related constructs, enabling us to rectify limitations that unavoidably plagued earlier generations of researchers. This is especially true for Blau space, a sociodemographic niche model used to explore competition between social entities over resources, such as memberships. While this approach has been successful in using probabilistic representations of social networks and resource niches, its modeling framework has remained essentially unchanged for over 40 years, and lacks the ability to make predictions about the future states of sociodemographic space. We present a novel Hybrid Blau space (HBS) model, which utilizes a cellular framework and probabilistic urn models to simulate competition over resources while suffering from fewer limitations. We apply this new model to the General Social Survey, running two sets of models from a series of variables (age, education, income, and sex) and utilize an adjustable range of sociodemographic information for local simulation of organizational competition. We also demonstrate the model’s predictive ability, as well as introduce new methods of validation and fit.
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