Quantifying intergenerational changes in educational homophily is challenging when education level of marriageable individuals is generation-specific. To address this challenge, the literature proposed various indicators. We argue that suitable indicators should meet several analytical criteria. However, our main criterion is empirical in nature: suitable indicators, when computed from data on American couples born after 1930, should exhibit a U-shaped trend at the national level and for the majority of the US states. A cardinal homophily indicator constructed with the NM-method, based on an old forgotten and recently reinvented ordinal indicator, is one of the few indicators that meets the criteria.
This paper explores how minority group size moderates the effect of employer prejudice on hiring discrimination. We propose a simple job search model in which prejudiced employers retain hiring discretion but are legally required to offer equal pay to all workers. Our analysis suggests that employers are more likely to exclude smaller minority groups from employment; however, as the minority population increases, employment discrimination diminishes and may even vanish. We further show that this result persists even when employer prejudice varies across firms and intensifies with minority group size.
In a recent essay titled "Status Organizes Cooperation: An Evolutionary Theory of Status and Social Order," Noah P. Mark (2018) has advanced an illuminating model of how status behaviors could have evolved to organize social cooperation in the context of social dilemmas such as the prisoner's dilemma. In this essay we argue Mark's central result hinges on a number of modeling assumptions that bias the model in favor of cooperation which, once relaxed, preempt cooperative status behaviors from evolving. Nevertheless, we believe that the basic insight behind Mark's ideas can still be recovered in the case of a less daunting kind of social dilemma.
Individuals inhabit multiple social roles - citizen, neighbor, worker - but make residential decisions with a single body. Classic segregation models assume fixed, singular identities. This paper extends Schelling's model by introducing agents with multidimensional identities and salience rules that determine which identity guides behavior. Across 1,920 simulations, we show that coordination failures produce persistent dissatisfaction and instability, while attention alignment can reduce or concentrate segregation. These findings challenge the assumption that identity complexity promotes integration. Instead, spatial outcomes depend on how identity salience is cognitively and socially managed. The model contributes to mathematical sociology and formal intersectionality by revealing how behavioral coordination mechanisms shape emergent patterns of segregation.
Affect Control Theory (ACT) posits that the micro-foundation of the social order lies in affective meanings consensually shared within a culture. This study challenges the assumption of consensus in affective culture by integrating ACT with Cultural Consensus Theory and sociological perspectives, offering a view of affective culture as both consensual and fragmented. Using Bayesian methods and data from surveys conducted in France and Germany, the analysis reveals that despite broad consensus, systematic patterns of fragmentation emerge, particularly in the evaluation dimension, which interacts with potency and activity. Moreover, the study shows that cultural competence varies across social positions, reflecting broader social dynamics. These findings advance ACT research methodologically and contribute to understanding how cultural consensus and fragmentation interact in the affective culture.
During previous pandemics, social distancing was organized top-down, through the imposition of a minimum distance. An alternative approach toward social distancing asks individuals to try to maximize their distance to others. Here, we ask whether people can thus efficiently self-organize spatial arrangements. We studied 953 social distancing decisions made in 150 groups under controlled conditions. Results show that subject behavior approximates what optimal mathematical strategies achieve. At scale, the observed behavior produces greater distancing than the mere respecting of an externally imposed minimum distance. These findings suggest that the encouragement of maximal distancing may reduce the propagation of viruses that spread through close-range contact beyond what is achieved with minimum-distance policies alone.
Organizations in emergency settings must cope with various sources of disruption, most notably personnel loss. Death, incapacitation, or isolation of individuals within an organizational communication network can impair information passing, coordination, and connectivity, and may drive maladaptive responses such as repeated attempts to contact lost personnel (“calling the dead”) that themselves consume scarce resources. At the same time, organizations may respond to such disruption by reorganizing to restore function, a behavior that is fundamental to organizational resilience. Here, we use empirically calibrated models of communication for 17 groups of responders to the World Trade Center Disaster to examine the impact of exogenous removal of personnel on communication activity and network resilience. We find that removal of high-degree personnel and those in institutionally coordinative roles is particularly damaging to these organizations, with specialist responders being slower to adapt to losses. However, all organizations show adaptations to disruption, in some cases becoming better connected and making more complete use of personnel relative to control after experiencing losses.
This paper shows the importance of building flexible models of social influence in online contexts, aimed to better understand the drivers of different opinion patterns such as political consensus or polarization. Mirroring empirical research on users' behavior and characteristics online, I formalize an opinion formation model separating Elite and Normal users by network centrality as a measure of influence. Each group has a distinct opinion updating rule, capturing behavioral differences in online interactions among ideologically opposed (aligned) visible Elites or passive engagement with content by Normal users. I run simulations and investigate the conditions driving different long-run opinion patterns. Findings align with political science literature showing partisan Elite polarization, rather than mass polarization.
Based on community detection methods in social networks, a new approach is presented for understanding and fitting social mobility processes. As a community detection algorithm, eigenspectrum decomposition is applied to identify optimal community structures in which most shares of the relations are within community ties. Through this approach, one may construct a log-linear model by adding community effects to an independence model until the model fits the given data, where the community effects are derived from the community detection analysis of the residuals. To illustrate, this approach is employed in mobility tables from the General Social Survey. Consequently, a set of parsimonious models that fit the observed mobility tables is presented, which offers novel and interesting interpretations of mobility patterns.
We investigate a set of models in opinion dynamics with a relaxation of bounded confidence against a background of noisy communication. Instead of a rigid threshold of disagreement, agents experience diminishing interaction effect as their disagreement grows. We re-explore concepts, such as consensus and social stability, and derive explicit necessary and sufficient conditions for agent pairs to remain (stochastically) stable in their opinion differences. We then generalize the results to arbitrarily large societies with heterogeneous agents, i.e. agent-level fixed effects. We apply this model environment and the stability results to two existing models and a brand new model. Finally, we generalize to asymmetric updating mechanics, where we provide strong results and conjectures for stability as well.
The drivers of message-dependent actions - actions like retransmission, endorsement, or task performance taken in response to an incoming communication - are central to understanding the relationship between communication and other social behaviors. Here, we derive a first-order model for the relationship of other message-dependent actions with retransmission, showing that correlations between the underlying drivers of both can be inferred from aggregated data. We apply this model to data on engagement with online messages by the US National Weather Service, finding that transmission-driving factors are distinct from those driving engagement. This is inconsistent with the theory that message-dependent actions are governed by an underlying "saliency bottleneck," and suggests the need for different models for different action types.
This study proposes a new methodological approach by utilizing a machine learning-based clustering algorithm to measure academic performance inequality in Chinese middle schools. Unlike traditional methods that use single summary statistics, our approach clusters schools based on the entire empirical cumulative distribution function of student test scores, capturing more complex patterns of inequality. We classify schools into three distinct clusters reflecting varying degrees of inequality. Our findings reveal that schools with higher concentrations of rural migrant students are more likely to fall into more unequal clusters, where students face greater academic challenges. By comparing our method with traditional measures, we demonstrate its ability to detect subtle inequality patterns that traditional measures may overlook. This methodology provides valuable insights for targeted policy interventions to address disparities.
Calling to report crime represents public cooperation with the police. When rational individuals are predicted to report (and when not) is still poorly understood. We study an interdependent security game under threat of a costly event that can only occur once or is perceived as so costly that the threat of the event occurring more than once is (in foresight) perceived as no more costly than the event occurring only once. Our analysis suggests how the interactions among the benefits, costs and neighborhood effects of police response might affect reporting. When there is spatial contagion of crime, rational individuals may choose to report when more of their neighbors report. When there is spatial contagion of deterrence, the relationship is reversed.
Over the years, a number of methods have been proposed to forecast the unknown inner-cell values of a set of related RxC contingency tables when only their margins are known. This is a classical problem that emerges in many areas, from economics to quantitative history, being particularly ubiquitous when dealing with electoral data in sociology and political science. However, the two current major algorithms to solve this problem, based on Bayesian statistics and iterative linear programming depend on adjustable (hyper-)parameters and do not yield a unique solution: their estimates tend to fluctuate (when convergence is reached) around a stationary distribution. Within the linear programming framework, this paper proposes a new algorithm (lclphom) that always converges to a unique solution, having no adjustable parameters. This characteristic makes it easy to use and robust to claims of hacking. Furthermore, after assessing lclphom with real and simulated data, lclphom is found to yield estimates of (almost) similar accuracy to the current major solutions, being more preferable to the other lphom-family algorithms the more heterogeneous the row-fraction distributions of the tables are. Interested practitioners can easily use this new algorithm as it has been programmed in the R-package lphom.
This article investigates mechanism-based explanations for a well-known macro-level pattern in sociology of education, namely, that Black-White unequal access to advanced coursework is the highest in racially diverse and majority-White schools. The study proposes that school racial composition (defined as the share of Whites) could influence the emergence of Black-White course-taking inequalities through the combination of two factors: (a), the relevance of network-based resources for advanced enrollment; and (b), the known relationship between racial composition and network structure. Through an empirically informed agent-based model, this study provides support for the proposed theoretical pathway and shows that, when combined with empirically representative structural inequalities, this racial composition effect can help explaining the pattern of interest. Possible policy implications are discussed.
Known by many names and arising in many settings, the forced linear diffusion model is central to the modeling of power and influence within social networks (while also serving as the mechanistic justification for the widely used spatial/network autocorrelation models). The standard equilibrium solution to the diffusion model depends on strict timescale separation between network dynamics and attribute dynamics, such that the diffusion network can be considered fixed with respect to the diffusion process. Here, we consider a relaxation of this assumption, in which the network changes only slowly relative to the diffusion dynamics. In this case, we show that one can obtain a perturbative solution to the diffusion model, which depends on knowledge of past states in only a minimal way.
Collective problem-solving networks are common in modern life. They often benefit from having diverse members with complementary skills and perspectives, however the benefits of information exchange and synthesis among them may be squandered if they self-select away from diverse counterparts and towards homogeneous groups or perceived competency. Building on the extensive tradition of “exploration and exploitation” agent-based models (ABMs), we initialize communicative networks with diverse groups of agents who solve a complex problem represented by Kauffman’s NK problem space. We compare three types of ABMs, where the initial network and agent setups are identical but differentiated by agents’ attachment proclivities: (1) diversity- seeking networks, where agents prefer ties with different-agents; (2) homophily-seeking networks, where agents prefer ties with similar-agents; and (3) merit-seeking networks, where agents prefer ties with agents who have found better solutions. We find that diversity-seeking networks perform well because diversity promotes more exploration for solutions, but it also fosters network structures that disseminate these higher quality solutions more effectively than merit-seeking and homophily-seeking networks.
Qualitative Comparative Analysis (QCA) has been increasingly used in recent years due to its purported construction of a middle path between case-oriented and variable-oriented methods. Despite its popularity, a key element of the method has been criticized for possibly not distinguishing random from real patterns in data, rendering its usefulness questionable. Critics of the method suggest a straightforward technique to test whether QCA will return a configuration when given random data. We adapt this technique to determine the probability that a given QCA application would return a random result. This assessment can be used as a hypothesis test for QCA, with an interpretation similar to a p-value. Using repeated applications of QCA to randomly-generated data, we first show that generally, the tendency for QCA to return spurious results is attenuated by using reasonable consistency score and configurational N thresholds; however, this varies considerably according to the basic structure of the data. Second, we suggest an application-specific assessment of QCA results, illustrated using the case of Tea Party rallies in Florida. This method, which we coin the Bootstrapped Robustness Assessment for QCA (baQCA), can provide researchers with recommendations for consistency score and configurational N thresholds.
This paper honors Thomas J. Fararo by highlighting his foundational contributions to two chief theoretical methods - generativity and unification - noting the footsoldiers episode, surprise, and beauty along with other elements of his legacy that accelerate progress in generativity and unification, such as mathematical functions and probability distributions. Generativity and unification enable progress toward the goal of understanding more and more by less and less, generativity by linking theoretical elements to observables in testable propositions, unification by unifying elements from two or more theories. Though deductive strategies are classical in origin, generativity - both the word and its expanded meaning that covers not only deductive strategies but also the Toulmin-type nondeductive strategies and computer simulation strategies - was Fararo's invention. U nification, too, was classical in origin, but Fararo expanded it from a purely theoretical operation to a spirit of unification with the potential to make peace between warring intellectual factions. Fararo's guiding hand is discernible in both the substance and methods of wide swaths of theoretical work in sociology, and this paper illustrates that aspect of his legacy by discussing his contributions to the author's early work on justice theory, including strategies of generativity later used also in status theory, and the more recent proposed unification of justice, status, and power. All of Fararo's work, not only writing and teaching but also guiding others, exemplified, in Toulmin's words: "the personal attitudes needed for effective work in science - adventurous skepticism and critical open-mindedness."
This study shows the importance of considering two social network features in agent-based models of cultural and opinion dynamics - network segregation by groups (e.g. race, class, or ideology) and interaction frequency by structural embeddedness. I formalize the two features as modeling conditions and apply them to an existing model that shows cultural or opinion polarization can emerge in a small-world network by the bridging of long-range ties. I find that when a small-world network is segregated, the inactivity of long-range ties (i.e. infrequent interactions) - instead of the bridging - becomes the key feature that contributes conversely to consensus or more extreme polarization. This implies that a systemic understanding of dyadic-level tie characteristics and suitable approaches to considering social networks in agent-based models are necessary.
