This article investigates geosocial patterns of marriage strategies among the leadership of Hasidism, arguably the most prominent socio-religious movement of modern Jewry, known for its unique network of charismatic leaders organized in hereditary dynasties. The article’s core premise is that grasping the network structure of the Hasidic movement’s dynasties, which has been under-researched, is crucial to understanding the movement’s social and cultural dynamics. The study employs social network analysis (SNA) and spatial analysis to examine marital unions among these leaders (2,375 cases), from the early stages of the movement in the eighteenth century until the early twenty-first century. The article explains, for the first time, how Hasidic dynasties expanded, eroded, and negotiated their status within the network of other dynasties. More specifically, we analyze the position of the dynasties within a wider context of social and spatial interconnection patterns, the significance of endogamy, the impact of territorial factors on marriage preferences, and the creation of dynastic clusters. A significant conclusion of this article is that, rather than a set of unrelated dynasties, Hasidic leadership gradually became a web of interconnected families with explicable patterns of organization. These findings can help explain historical processes in Hasidism, such as its persistence through historical crises. It can also illuminate leadership processes in other religions in which, as in Hasidism, the social structure of charismatic leadership is based on clans.

We investigate the effects of centrality on cooperation in groups. Players with centrality keep a group together by having a pivotal position in a network. In some of our experimental treatments, players can vote to exclude others and prevent them from further participation in the group. We find that, in the presence of exclusion, central players contribute significantly less than others, and that this is tolerated by those others. Because of this tolerance, groups with centrality manage to maintain high levels of cooperation.

We study a setting where individuals prefer to coordinate with others but they differ on their preferred action. Our interest is in understanding the role of link formation with others in shaping behavior. So we consider the situation in which interactions are exogenous and a situation where individuals choose links that determine the interactions. Theory is permissive in both settings: conformity (on either of the actions) and diversity (with different groups choosing their preferred actions) are both sustainable in equilibrium. We conduct an experiment to understand how link formation affects equilibrium selection. Our experiment reveals the powerful effect of linking on equilibrium selection: with an exogenous complete network, subjects choose to conform on the majority’s preferred action. By contrast, with endogenous linking—irrespective of the costs of linking—subjects always opt for diversity of actions.

We study distributional preferences in adolescent peer networks. Using incentivized choices between allocations for themselves and a passive agent, children are classified into efficiency-loving, inequality-loving, inequality-averse, and spiteful types. We find that pairs of students who report a friendship link are more likely to exhibit the same preference type than other students who attend the same school. The relation between types is almost completely driven by inequality-loving and spiteful types. The role of peer networks in explaining distributional preferences goes beyond network composition effects. A low rank in academic performance and a central position within the network relate positively to a higher likelihood of being classified as spiteful. Hence, social hierarchies seem to be correlated with distributional preference types.

Experimental and empirical evidence highlights the role of networks on social outcomes. This paper tests the properties of exogenously fixed networks in team production. Subjects make the same decisions in a team work environment under four different organizational networks: the line, the circle, the star, and the complete network. In all the networks, links make information available to neighbors. This design allows us to analyze decisions across networks and a variety of subject types in a standard linear team production game. Contribution levels differ significantly across networks and the star is the most efficient incomplete network. Moreover, our results suggest that subjects act as conditional cooperators with respect to the information received from the network.

Abundant evidence suggests that high levels of contributions to public goods can be sustained through self-governed monitoring and sanctioning. This experimental study investigates the effectiveness of decentralized sanctioning institutions in alternative punishment networks. Our results show that the structure of punishment network significantly affects allocations to the public good. In addition, we observe that network configurations are more important than punishment capacities for the levels of public good provision, imposed sanctions and economic efficiency. Lastly, we show that targeted revenge is a major driver of anti-social punishment.

This paper assesses a major transition in energy usage and distribution in the United Kingdom (UK) between 1953–73 as domestic coal gave way to electricity, and a centralized electricity generation and distribution system reached every home in the country. Our analysis significantly extends and reinterprets the business history of the National Grid by exploring the consequences of its completion. We argue that the National Grid facilitated the removal of the railways as an energy distribution network and enabled prototype “Net Zero” policies in the context of atmospheric pollution. We tie these themes together to conclude that the construction of the national grid was a major environmental success but removed an essential rationale for much of the rail network.

In the late twelfth century, four Chinese stonemasons migrated to Japan from the Chinese coastal city of Ningbo. They participated in the most important contemporary building project in Japan—the reconstruction of the prestigious Tōdaiji monastery in Nara following the Genpei War. Drawing on Chinese and Japanese textual records, archaeological evidence from the quarry sites, and the existing stone works, this study investigates the network that facilitated the movement of the artisans and their materials between China and Japan and sheds light on the relationship between artisans and religious monuments from a transnational perspective. This study explores how the migrant artisans’ expertise and connections enabled them to establish roots in a new society. Additionally, it examines the timing of the artisans’ migration within the broader context of Japan’s societal transformations, aiming to highlight the connectivity fostered by maritime networks in premodern East Asia.

Stochastic actor-oriented models (SAOMs) can be used to analyse dynamic network data, collected by observing a network and a behaviour in a panel design. The parameters of SAOMs are usually estimated by the method of moments (MoM) implemented by a stochastic approximation algorithm, where statistics defining the moment conditions correspond in a natural way to the parameters. Here, we propose to apply the generalized method of moments (GMoM), using more statistics than parameters. We concentrate on statistics depending jointly on the network and the behaviour, because of the importance of their interdependence, and propose to add contemporaneous statistics to the usual cross-lagged statistics. We describe the stochastic algorithm developed to approximate the GMoM solution. A small simulation study supports the greater statistical efficiency of the GMoM estimator compared to the MoM.

Research questions in the human sciences often seek to answer if and when a process changes across time. In functional MRI studies, for instance, researchers may seek to assess the onset of a shift in brain state. For daily diary studies, the researcher may seek to identify when a person’s psychological process shifts following treatment. The timing and presence of such a change may be meaningful in terms of understanding state changes. Currently, dynamic processes are typically quantified as static networks where edges indicate temporal relations among nodes, which may be variables reflecting emotions, behaviors, or brain activity. Here we describe three methods for detecting changes in such correlation networks from a data-driven perspective. Networks here are quantified using the lag-0 pair-wise correlation (or covariance) estimates as the representation of the dynamic relations among variables. We present three methods for change point detection: dynamic connectivity regression, max-type method, and a PCA-based method. The change point detection methods each include different ways to test if two given correlation network patterns from different segments in time are significantly different. These tests can also be used outside of the change point detection approaches to test any two given blocks of data. We compare the three methods for change point detection as well as the complementary significance testing approaches on simulated and empirical functional connectivity fMRI data examples.

Data in the form of zero-one matrices where conditioning on the marginals is relevant arise in diverse fields such as social networks and ecology; directed graphs constitute an important special case. An algorithm is given for the complete enumeration of the family of all zero-one matrices with given marginals and with a prespecified set of cells with structural zero entries. Complete enumeration is computationally feasible only for relatively small matrices. Therefore, a more useable Monte Carlo simulation method for the uniform distribution over this family is given, based on unequal probability sampling and ratio estimation. This method is applied to testing reciprocity of choices in social networks.

A simple property of networks is used as the basis for a scaling algorithm that represents nonsymmetric proximities as network distances. The algorithm determines which vertices are directly connected by an arc and estimates the length of each arc. Network distance, defined as the minimum pathlength between vertices, is assumed to be a generalized power function of the data. The derived network structure, however, is invariant across monotonic transformations of the data. A Monte Carlo simulation and applications to eight sets of proximity data support the practical utility of the algorithm.

There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions (based on temporal correlation, clustering methods, or independent component analysis [ICA]), some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies. In this paper our goal was to explicitly compare the networks obtained from a first-level ICA (ICA on the spatio-temporal functional magnetic resonance imaging (fMRI) data) to those from a second-level ICA (i.e., ICA on computed features rather than on the first-level fMRI data). Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks (spatial correlations as high as 0.85 for task data and 0.65 for rest data, well above the empirical null) and also preserves the relationship of these networks with other variables such as age (for example, default mode network regions tended to show decreased low frequency power for first-level analyses and decreased loading parameters for second-level analyses). In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro-connectome, particularly in the context of data fusion.