The siren song of so-called evidence: Why the evidence for social ecology models is not as strong as we think.

Ecological competition models from biology have been adopted for the study of a wide variety of social entities, including workplace organizations and voluntary associations.Despite their popularity, a number of fundamental challenges to these models have not been sufficiently recognized or addressed. As a result, it's possible that some apparently supportive evidence for ecological competition is in fact the outcome of chance or other processes. We propose a permutation test to compare observed evidence for ecological competition against an appropriate counterfactual population. To demonstrate our approach and validate our concern about the quality of evidence for ecological competition models, we apply the permutation test to one specific case. The results indicate that K-correlation values that have been taken as evidence for a well-established model, the Ecology of Affiliation, are quite common even in the absence of ecological competition. We conclude that the existing evidence for social ecology models may not be as reliable as commonly believed due to the disconnect between theory and empirical testing.

An improved approach for ecological modeling of social phenomena in Blau space.

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.

Blaunet: An R-based graphical user interface package to analyze Blau space.

McPherson's Blau space and affiliation ecology model is a powerful tool for analyzing the ecological competition among social entities, such as organizations, along a combination of sociodemographic characteristics of their members. In this paper we introduce the R-based Graphical User Interface (GUI) package Blaunet, an integrated set of tools to calculate, visualize, and analyze the statuses of individuals and social entities in Blau space, parameterized by multiple sociodemographic traits as dimensions. The package is able to calculate the Blau statuses at the nodal, dyadic, and meso levels based on three types of information: sociodemographic characteristics, group affiliations (e.g., membership in groups/organizations), and network ties. To facilitate this, Blaunet has the following five main capabilities, it can: 1) identify a list of possible salient dimensions; 2) calculate, plot, and analyze niches for social entities by measuring the social distance along the salient dimensions between individuals affiliated with them; 3) generate Blau bubbles for individuals, thereby allowing the study of interpersonal influence of similar others even with limited or no network information; 4) capture niche dynamics cross-sectionally by calculating the intensity of exploitation from the carrying capacity and the membership rate; and 5) analyze the niche movement longitudinally by estimating the predicted niche movement equations. We illustrate these capabilities of Blaunet with example datasets.

Partner selection and social capital in the status attainment process.

Why is individual success so strongly affected by parental socioeconomic status? We argue that parental socioeconomic status affects the socioeconomic status of one's romantic partner, thereby partially determining one's own social capital and socioeconomic status. Censored-inflated structural equation models using data from the NEtherlands Longitudinal Lifecourse Study (NELLS) and British Household Panel Survey (BHPS) confirm these relationships, while cross-lagged analyses suggest that they may be causal in nature. The strong tendency for people to have a romantic partner that reflects their socioeconomic origin shows how far the influence of the parental home stretches and why it does so.

A Mechanistic Model of Human Recall of Social Network Structure and Relationship Affect.

The social brain hypothesis argues that the need to deal with social challenges was key to our evolution of high intelligence. Research with non-human primates as well as experimental and fMRI studies in humans produce results consistent with this claim, leading to an estimate that human primary groups should consist of roughly 150 individuals. Gaps between this prediction and empirical observations can be partially accounted for using "compression heuristics", or schemata that simplify the encoding and recall of social information. However, little is known about the specific algorithmic processes used by humans to store and recall social information. We describe a mechanistic model of human network recall and demonstrate its sufficiency for capturing human recall behavior observed in experimental contexts. We find that human recall is predicated on accurate recall of a small number of high degree network nodes and the application of heuristics for both structural and affective information. This provides new insight into human memory, social network evolution, and demonstrates a novel approach to uncovering human cognitive operations.

Gangs, clubs, and alcohol: The effect of organizational membership on adolescent drinking behavior.

How does adolescent organizational membership in general, and simultaneous membership in distinct types of organizations in particular, impact drinking behavior? While past studies have focused either on the learning effect of involvement with gangs or on the constraining influence of conventional organizations on adolescent problem behavior, we explore the possibility that conventional school clubs can serve as socializing opportunities for existing gang members to engage in drinking behavior with non-gang club members. Using the Add Health data, we show that gang members drink more often, and engage in more binge drinking, than non-members. More importantly, individuals who are members of both gangs and school clubs drink alcohol at greater levels than those who are solely involved in gangs. In addition, non-gang adolescents who are co-members with gang members in the same school club are more likely to drink alcohol than non-members. This result has important implications for understanding the role of organizations in adolescent behavior and suggests that the study of delinquent behaviors would benefit from devoting more attention to individuals who bridge distinct types of organizations.

Humans use compression heuristics to improve the recall of social networks.

The ability of primates, including humans, to maintain large social networks appears to depend on the ratio of the neocortex to the rest of the brain. However, observed human network size frequently exceeds predictions based on this ratio (e.g., "Dunbar's Number"), implying that human networks are too large to be cognitively managed. Here I show that humans adaptively use compression heuristics to allow larger amounts of social information to be stored in the same brain volume. I find that human adults can remember larger numbers of relationships in greater detail when a network exhibits triadic closure and kin labels than when it does not. These findings help to explain how humans manage large and complex social networks with finite cognitive resources and suggest that many of the unusual properties of human social networks are rooted in the strategies necessary to cope with cognitive limitations.

Gender and homophily: differences in male female association in Blau space.

Homophily, the tendency for similar individuals to associate, is one of the most robust findings in social science. Despite this robustness, we have less information about how personal characteristics relate to differences in the strength of homophily. Nor do we know much about the impact of personal characteristics on judgments of relative dissimilarity. The present study compares the strength of age, religious, and educational homophily for male and female non-kin ties using network data from the 1985 General Social Survey. It also compares the patterning of ties among dissimilar alters for both sexes. The results of this exploratory effort indicate that males and females are almost equally homophilous, although religious homophily exerts a stronger influence on females than males. Males and females do, however, differ in their tendency to associate with certain types of dissimilar alters. Education is essentially uniform for both sexes, religious difference is more important for females than males, and those over sixty or under thirty are less different from the middle categories of age for females than for males. The results suggest that males are able to bridge larger areas of social space in their non-kin interpersonal networks and likely accumulate greater social capital as a consequence.