Search

Recently an article in "Personality and Social Psychology Review" urged social psychologists to reacquire their "sociological imagination" and incorporate broader, structural factors in their work (Oishi, Kesebir, and Snyder 2009). Studies of social inequality in particular seem ripe for this kind of collaboration. Psychological investigations relating to inequality typically center on interpersonal prejudice, stigma, or power relations. To some extent, these perspectives incorporate sociostructural factors, but only rarely. In contrast, sociology has much to offer in explaining how dimensions of stratification (e.g., race or occupation) drive psychological reactions between people at the face-to-face level. In this article, the authors discuss where the future (understanding) of inequality lies.

Social networks are fundamental to the broad scale cooperation observed in human populations. But by structuring the flow of benefits from cooperation, networks also create and sustain macro-level inequalities. Here we ask how two aspects of inequality shape the evolution of cooperation in dynamic social networks. Results from a crowdsourced experiment (N = 1080) show that inequality alters the distribution of cooperation within networks such that participants engage in more costly cooperation with their wealthier partners in order to maintain more valuable connections to them. Inequality also influences network dynamics, increasing the tendency for participants to seek wealthier partners, resulting in structural network change. These processes aggregate to alter network structures and produce greater system-level inequality. The findings thus shed critical light on how networks serve as both boon and barrier to macro-level human flourishing. [ABSTRACT FROM AUTHOR]

This study introduces the referent network, the set of relations defined by knowing others reward levels, and makes initial predictions about how the structure of that network shapes perceptions of income fairness. Previous research recognizes that justice assessments are driven by social comparisons; yet there is a paucity of research on how the structure of interpersonal contacts shapes justice assessments. Using a social networks perspective, the arguments here suggest that the referent network affects social comparisons, which in turn affect justice assessments of rewards. This reasoning is also integrated with the notion that people make both local and referential comparisons when determining their just rewards. An experiment evaluates these arguments. Results show support for the argument linking network structures to justice assessments and partial support for the extension to local and referential comparisons. Supplemental analyses show how reward levels qualify the arguments. Implications and directions for future research are discussed. [ABSTRACT FROM AUTHOR]

Humans' propensity to cooperate is driven by our embeddedness in social networks. A key mechanism through which networks promote cooperation is clustering. Within clusters, conditional cooperators are insulated from exploitation by noncooperators, allowing them to reap the benefits of cooperation. Dynamic networks, where ties can be shed and new ties formed, allow for the endogenous emergence of clusters of cooperators. Although past work suggests that either reputation processes or network dynamics can increase clustering and cooperation, existing work on network dynamics conflates reputations and dynamics. Here we report results from a large-scale experiment (total n = 2,675) that embedded participants in clustered or random networks that were static or dynamic, with varying levels of reputational information. Results show that initial network clustering predicts cooperation in static networks, but not in dynamic ones. Further, our experiment shows that while reputations are important for partner choice, cooperation levels are driven purely by dynamics. Supplemental conditions confirmed this lack of a reputation effect. Importantly, we find that when participants make individual choices to cooperate or defect with each partner, as opposed to a single decision that applies to all partners (as is standard in the literature on cooperation in networks), cooperation rates in static networks are as high as cooperation rates in dynamic networks. This finding highlights the importance of structured relations for sustained cooperation, and shows how giving experimental participants more realistic choices has important consequences for whether dynamic networks promote higher levels of cooperation than static networks. [ABSTRACT FROM AUTHOR]

Research on the evolution of cooperation in networked populations has assumed that ties are simply present or absent. Here we bring relational sociological insights about the strength of ties to bear on the problem of cooperation in dynamic networks. We argue that the value of ties affects their strength, which in turn promotes cooperation. We evaluate this argument with two studies. First, results from an agent-based model are consistent with the logic of our argument and are robust across a variety of initial conditions. Second, results from a controlled laboratory experiment with human participants support the key predictions. Across both studies we demonstrate that tie strength, operationalized as relationship duration, mediates the impact of tie value on cooperation. [ABSTRACT FROM AUTHOR]

Purpose -- This study examines how the structure of referent networks, or the social network defined by knowing others' reward levels, affects perceptions of distributive justice. The homogeneity of rewards in the referent network, the amount of inequality in the referent network, and an individual's reward level are all associated with distributive justice perceptions. Several moderating relationships are also examined. Methodology/approach -- We relied on data from a controlled laboratory experiment to test a series of theoretically derived hypotheses. Findings -- The study shows that several aspects about the structure of the referent network are important for shaping perceptions of distributive justice. Specifically, the reward heterogeneity and amount of inequality in the network are found to be negatively associated with distributive justice, while reward levels are found to be positively associated with distributive justice. Furthermore, the effect of reward levels on distributive justice is moderated by both (i) the presence of a referential standard for rewards and (ii) the amount of inequality in the network. Research limitations/implications -- While being among the first studies to demonstrate effects of referent networks on perceptions of fairness, it is unclear how group memberships combine with referent network effects and which factors may blur these relationships in uncontrolled environments. Subsequent scholarship on the effect of referent networks on justice perceptions should leverage multiple data sources. Originality/value of chapter -- Research on the effects of referents on justice perceptions has focused on particular referent individuals. We recast this issue in terms of referent networks, which highlights the empirical reality that individuals have a variety of sources or alters which could operate as referents. [ABSTRACT FROM AUTHOR]

Identifying communities or clusters in networked systems has received much attention across the physical and social sciences. Most of this work focuses on single layer or one-mode networks, including social networks between people or hyperlinks between websites. Multilayer or multi-mode networks, such as affiliation networks linking people to organizations, receive much less attention in this literature. Common strategies for discovering the community structure of multi-mode networks identify the communities of each mode simultaneously. Here I show that this combined approach is ineffective at discovering community structures when there are an unequal number of communities between the modes of a multi-mode network. I propose a dual-projection alternative for detecting communities in multi-mode networks that overcomes this shortcoming. The evaluation of synthetic networks with known community structures reveals that the dual-projection approach outperforms the combined approach when there are a different number of communities in the various modes. At the same time, results show that the dual-projection approach is as effective as the combined strategy when the number of communities is the same between the modes. [ABSTRACT FROM AUTHOR]

The article focuses on the logic of multi-mode networks that partition graphs into communities. It states that a simple and elegant tool is obtained which is used to discover the community structure of multi mode graphs. It mentions that a generalized protocol was employed to a three mode network and depicts the finding that results of algorithm are consistent with substantive knowledge.