Abstract In coercive relations, threats of negative sanctions extract valued positive sanctions from coercees. Only when coercion is direct, however, are the negative sanctions controlled by the coercer who benefits from the threats. Not previously investigated, indirect coercion relies on threats and negative sanctions that are external to the exploitative relation. We suggest that indirect coercion is ubiquitous. From their inception states have used the threat of external enemies to justify rulers’ increased powers and to provide a patina of legitimacy while, on a smaller scale, criminal organizations such as the mafia have long profited from offering protection. The purpose of this paper is to theoretically model and experimentally investigate indirect coercion and compare its effectiveness in extracting valued resources to that of direct coercion. Previous research has shown that all power structures, whether exchange, conflict or coercive, take two distinct forms, strong and weak. Therefore, experiments on strong and weak indirect coercion are run and are compared to new and previous experiments on strong and weak direct coercion. Theoretically grounded predictions are derived and tested for those structures.
{"title":"External Threat as Coercion","authors":"Pamela Emanuelson, David Willer","doi":"10.21307/JOSS-2019-016","DOIUrl":"https://doi.org/10.21307/JOSS-2019-016","url":null,"abstract":"Abstract In coercive relations, threats of negative sanctions extract valued positive sanctions from coercees. Only when coercion is direct, however, are the negative sanctions controlled by the coercer who benefits from the threats. Not previously investigated, indirect coercion relies on threats and negative sanctions that are external to the exploitative relation. We suggest that indirect coercion is ubiquitous. From their inception states have used the threat of external enemies to justify rulers’ increased powers and to provide a patina of legitimacy while, on a smaller scale, criminal organizations such as the mafia have long profited from offering protection. The purpose of this paper is to theoretically model and experimentally investigate indirect coercion and compare its effectiveness in extracting valued resources to that of direct coercion. Previous research has shown that all power structures, whether exchange, conflict or coercive, take two distinct forms, strong and weak. Therefore, experiments on strong and weak indirect coercion are run and are compared to new and previous experiments on strong and weak direct coercion. Theoretically grounded predictions are derived and tested for those structures.","PeriodicalId":35236,"journal":{"name":"Journal of Social Structure","volume":"16 1","pages":"1 - 16"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67666711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract How has the passage of time impacted the ego networks of males and females? I compare the homophily and social distances of males and females using the 1985 and 2004 GSS networks modules. The results indicate that change has been gradual and incremental rather than radical. In 2004 less social distance separates associates for women than for men, and males differentiate more among levels of education. The results suggest that macro-level structural changes have not been sufficient to produce similarly large changes in ego network composition.
{"title":"A Longitudinal Analysis of Gendered Association Patterns: Homophily and Social Distance in the General Social Survey","authors":"Matthew E. Brashears","doi":"10.21307/JOSS-2019-013","DOIUrl":"https://doi.org/10.21307/JOSS-2019-013","url":null,"abstract":"Abstract How has the passage of time impacted the ego networks of males and females? I compare the homophily and social distances of males and females using the 1985 and 2004 GSS networks modules. The results indicate that change has been gradual and incremental rather than radical. In 2004 less social distance separates associates for women than for men, and males differentiate more among levels of education. The results suggest that macro-level structural changes have not been sufficient to produce similarly large changes in ego network composition.","PeriodicalId":35236,"journal":{"name":"Journal of Social Structure","volume":"16 1","pages":"1 - 26"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67666648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Selecting an appropriate method of clustering for network data a priori can be a frustrating and confusing process. To address the problem we build on an a posteriori approach developed by Grimmer and King (2011) that compares hundreds of possible clustering methods at once through concise and intuitive visualization. We adapt this general method to the context of social networks, extend it with additional visualization features designed to enhance interpretability, and describe its principled use, outlining steps for selecting a class of methods to compare, interpreting visual output, and making a final selection. The interactive method, implemented in R, is demonstrated using Zachary’s karate club, a canonical dataset from the network literature.
{"title":"Choosing a Clustering: An A Posteriori Method for Social Networks","authors":"Samuel D. Pimentel","doi":"10.21307/JOSS-2019-022","DOIUrl":"https://doi.org/10.21307/JOSS-2019-022","url":null,"abstract":"Abstract Selecting an appropriate method of clustering for network data a priori can be a frustrating and confusing process. To address the problem we build on an a posteriori approach developed by Grimmer and King (2011) that compares hundreds of possible clustering methods at once through concise and intuitive visualization. We adapt this general method to the context of social networks, extend it with additional visualization features designed to enhance interpretability, and describe its principled use, outlining steps for selecting a class of methods to compare, interpreting visual output, and making a final selection. The interactive method, implemented in R, is demonstrated using Zachary’s karate club, a canonical dataset from the network literature.","PeriodicalId":35236,"journal":{"name":"Journal of Social Structure","volume":"15 1","pages":"1 - 21"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67666833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Often objects are to be ranked. However, there is no measurable quantity available to express the ranking aim and to quantify it. The consequence is that indicators are selected, serving as proxies for the ranking aim. Although this set of indicators is of great importance for its own right, the most commonly used practice to obtain a ranking is an aggregation method. Any aggregation, however suffers from the effect of compensation, because the aggregation technique is in the broadest sense an averaging method. Here an alternative is suggested which avoids this averaging and which is derived from simple elements of the theory of partially ordered sets (posets). The central concept in partial order is the ‘concept of comparison’ and the most general outcome is a web of relations between objects according to their indicator values, respecting the ranking aim. As an example the ‘Failed State Index’ (FSI), annually prepared by the Fund of Peace is selected. The FSI is based on twelve individual contextual different indicators, subsequently transformed into a single composite indicator, by simple addition of the single indicator values. Such an operation leaves space for compensation effects, where one or more indicators level out the effect of others. Hence, a comparison between the single states (in total 177) based on their mutual FSI ranking has its limitations as the comparisons are made based on the composite indicator. We show that brain drain is one of the indicators in the FSI-study that plays a crucial role in the ranking, whereby the ranking aim is the stabilization of nations.
{"title":"An Analysis of the ‘Failed States Index’ by Partial Order Methodology","authors":"L. Carlsen, R. Brüggemann","doi":"10.21307/JOSS-2019-025","DOIUrl":"https://doi.org/10.21307/JOSS-2019-025","url":null,"abstract":"Abstract Often objects are to be ranked. However, there is no measurable quantity available to express the ranking aim and to quantify it. The consequence is that indicators are selected, serving as proxies for the ranking aim. Although this set of indicators is of great importance for its own right, the most commonly used practice to obtain a ranking is an aggregation method. Any aggregation, however suffers from the effect of compensation, because the aggregation technique is in the broadest sense an averaging method. Here an alternative is suggested which avoids this averaging and which is derived from simple elements of the theory of partially ordered sets (posets). The central concept in partial order is the ‘concept of comparison’ and the most general outcome is a web of relations between objects according to their indicator values, respecting the ranking aim. As an example the ‘Failed State Index’ (FSI), annually prepared by the Fund of Peace is selected. The FSI is based on twelve individual contextual different indicators, subsequently transformed into a single composite indicator, by simple addition of the single indicator values. Such an operation leaves space for compensation effects, where one or more indicators level out the effect of others. Hence, a comparison between the single states (in total 177) based on their mutual FSI ranking has its limitations as the comparisons are made based on the composite indicator. We show that brain drain is one of the indicators in the FSI-study that plays a crucial role in the ranking, whereby the ranking aim is the stabilization of nations.","PeriodicalId":35236,"journal":{"name":"Journal of Social Structure","volume":"14 1","pages":"1 - 31"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67666608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Network analysis has become a popular tool to examine data from online social networks to politics to ecological systems. As more computing power has become available, new technology-driven methods and tools are being developed that can support larger and richer network data, including dynamic network analysis. This timely merger of abundant data and cutting edge techniques affords researchers the ability to better understand networks over time, accurately show how they evolve, find patterns of growth, or study models such as the diffusion of innovation. We combine traditional methods in social network analysis with new innovative visualizations and methods in dynamic network studies to explore an online tobacco-control community called GLOBALink, using almost twenty years of longitudinal data. We describe the methods used for the study, and perform an exploratory network study that links empirical results to real-world events.
{"title":"Using Visualizations to Explore Network Dynamics","authors":"Kar-Hai Chu, H. Wipfli, T. Valente","doi":"10.21307/JOSS-2019-026","DOIUrl":"https://doi.org/10.21307/JOSS-2019-026","url":null,"abstract":"Abstract Network analysis has become a popular tool to examine data from online social networks to politics to ecological systems. As more computing power has become available, new technology-driven methods and tools are being developed that can support larger and richer network data, including dynamic network analysis. This timely merger of abundant data and cutting edge techniques affords researchers the ability to better understand networks over time, accurately show how they evolve, find patterns of growth, or study models such as the diffusion of innovation. We combine traditional methods in social network analysis with new innovative visualizations and methods in dynamic network studies to explore an online tobacco-control community called GLOBALink, using almost twenty years of longitudinal data. We describe the methods used for the study, and perform an exploratory network study that links empirical results to real-world events.","PeriodicalId":35236,"journal":{"name":"Journal of Social Structure","volume":"46 1","pages":"1 - 24"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67666693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract An affiliation network consists of actors and events. Actors are affiliated with each other by virtue of the events they mutually attend. This article introduces a family of affiliation measures that captures the extent of actors’ affiliations in the network. At one extreme, one might have an actor who attended many events, but none of these events were attended by any of the other actors in the network. Although of high degree, in no reasonable interpretation would such an actor be considered highly affiliated with other actors in the network. At the other extreme, one might have an actor defined by a collection of events, all of which were attended by another actor(s), making the actor as enmeshed in the network as possible. Most actors will be between these extremes, with some events being shared by varying others, and some not. This article introduces a family of affiliation measures based on the entries of the co-occurrence matrix. After defining the measures, the cumulative distribution function of first-order affiliation is derived and expressed as a difference of binomials.
{"title":"A Family of Affiliation Indices for Two-Mode Networks*","authors":"Frank Tutzauer","doi":"10.21307/JOSS-2019-024","DOIUrl":"https://doi.org/10.21307/JOSS-2019-024","url":null,"abstract":"Abstract An affiliation network consists of actors and events. Actors are affiliated with each other by virtue of the events they mutually attend. This article introduces a family of affiliation measures that captures the extent of actors’ affiliations in the network. At one extreme, one might have an actor who attended many events, but none of these events were attended by any of the other actors in the network. Although of high degree, in no reasonable interpretation would such an actor be considered highly affiliated with other actors in the network. At the other extreme, one might have an actor defined by a collection of events, all of which were attended by another actor(s), making the actor as enmeshed in the network as possible. Most actors will be between these extremes, with some events being shared by varying others, and some not. This article introduces a family of affiliation measures based on the entries of the co-occurrence matrix. After defining the measures, the cumulative distribution function of first-order affiliation is derived and expressed as a difference of binomials.","PeriodicalId":35236,"journal":{"name":"Journal of Social Structure","volume":"14 1","pages":"1 - 19"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67667012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Apkarian, J. Fletcher, C. Chase-Dunn, R. Hanneman
Abstract Building on world-systems theory, simulation models of 5-line intersocietal networks were generated in an effort to understand systemic power hierarchies. The societal nodes were exclusively connected by three types of interaction: migration, warfare, and unequal trade. These networks can be considered “mixed relation” networks due to the ways in which these types of ties combine positive and negative sanction flows. Insights from elementary theory were employed to understand how exclusion from these different types of ties might influence the resulting power distributions. Additionally, the resource carrying capacity of the nodes was varied by structural position in an effort to differentiate the influence of structural position and individual attributes on location in the hierarchy. It was determined that exclusion from interaction is likely a structural, scale invariant mechanism that helps to determine power distributions above and beyond the inherent attributes of network actors.
{"title":"Hierarchy in Mixed Relation Networks: Warfare Advantage and Resource Distribution in Simulated World-Systems*","authors":"J. Apkarian, J. Fletcher, C. Chase-Dunn, R. Hanneman","doi":"10.21307/JOSS-2019-023","DOIUrl":"https://doi.org/10.21307/JOSS-2019-023","url":null,"abstract":"Abstract Building on world-systems theory, simulation models of 5-line intersocietal networks were generated in an effort to understand systemic power hierarchies. The societal nodes were exclusively connected by three types of interaction: migration, warfare, and unequal trade. These networks can be considered “mixed relation” networks due to the ways in which these types of ties combine positive and negative sanction flows. Insights from elementary theory were employed to understand how exclusion from these different types of ties might influence the resulting power distributions. Additionally, the resource carrying capacity of the nodes was varied by structural position in an effort to differentiate the influence of structural position and individual attributes on location in the hierarchy. It was determined that exclusion from interaction is likely a structural, scale invariant mechanism that helps to determine power distributions above and beyond the inherent attributes of network actors.","PeriodicalId":35236,"journal":{"name":"Journal of Social Structure","volume":"14 1","pages":"1 - 17"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67666899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Advances in text analysis, particularly the ability to extract network based information from texts, is enabling researches to conduct detailed socio-cultural ethnographies rapidly by retrieving characteristic descriptions from texts and fusing the results from varied sources. We describe this process and illustrate it in the context of conflict in the Sudan. We show how network information can be extracted from vast quantities of unstructured texts-based information using computer assisted processes. This is illustrated by an examination of changes in the political networks in Sudan as extracted from the Sudan Tribune. We find that this approach enables rapid high level assessment of a socio-cultural environment, generates results that are viewed as accurate by subject matter experts, and match actual historical events. The relative value of this socio-cultural analysis approach is discussed.
{"title":"Network Text Analysis in Computer-Intensive Rapid Ethnography Retrieval: An Example from Political Networks of Sudan*","authors":"Laurent Tambayong, Kathleen M. Carley","doi":"10.21307/JOSS-2019-028","DOIUrl":"https://doi.org/10.21307/JOSS-2019-028","url":null,"abstract":"Abstract Advances in text analysis, particularly the ability to extract network based information from texts, is enabling researches to conduct detailed socio-cultural ethnographies rapidly by retrieving characteristic descriptions from texts and fusing the results from varied sources. We describe this process and illustrate it in the context of conflict in the Sudan. We show how network information can be extracted from vast quantities of unstructured texts-based information using computer assisted processes. This is illustrated by an examination of changes in the political networks in Sudan as extracted from the Sudan Tribune. We find that this approach enables rapid high level assessment of a socio-cultural environment, generates results that are viewed as accurate by subject matter experts, and match actual historical events. The relative value of this socio-cultural analysis approach is discussed.","PeriodicalId":35236,"journal":{"name":"Journal of Social Structure","volume":"13 1","pages":"1 - 24"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67666963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract In this paper, we employ archival materials from multiple institutional sources to reconstruct the dynamic network of interorganizational collaboration that emerged in response to the Hurricane Katrina disaster of late 2005. Over the period from initial storm formation through the first week following landfall in Louisiana, we record active participation by over 1,500 organizations in response activities. We here conduct an exploratory analysis of the growth and evolution of the network of collaboration among responding organizations, an identification of organizations that emerged as central actors in the response process, and the cohesive subgroups that crystallized within the larger network. Finally, we conclude with a discussion of several issues related to the use of archival methods in research on interorganizational networks in disaster settings, and to the use of automated methods for network extraction.
{"title":"Interorganizational Collaboration in the Hurricane Katrina Response*","authors":"C. Butts, Ryan M. Acton, C. Marcum","doi":"10.21307/JOSS-2019-027","DOIUrl":"https://doi.org/10.21307/JOSS-2019-027","url":null,"abstract":"Abstract In this paper, we employ archival materials from multiple institutional sources to reconstruct the dynamic network of interorganizational collaboration that emerged in response to the Hurricane Katrina disaster of late 2005. Over the period from initial storm formation through the first week following landfall in Louisiana, we record active participation by over 1,500 organizations in response activities. We here conduct an exploratory analysis of the growth and evolution of the network of collaboration among responding organizations, an identification of organizations that emerged as central actors in the response process, and the cohesive subgroups that crystallized within the larger network. Finally, we conclude with a discussion of several issues related to the use of archival methods in research on interorganizational networks in disaster settings, and to the use of automated methods for network extraction.","PeriodicalId":35236,"journal":{"name":"Journal of Social Structure","volume":"13 1","pages":"1 - 37"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67666808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Bonacich (1987) suggested a family of centrality measures that provide a useful way of modeling questions of power and network constraint. However, the literature offers little guidance regarding the choice of β, the parameter which alters the way the measure accounts for the effect of having powerful contacts in ones network. In this paper I explore the way the choice of the β parameter affects the power indices the Bonacich measure generates. I consider three network properties which might affect the way the choice of β influences the Bonacich power indices. I find that in high density networks with few internal ‘chains’ and few pendants, the choice of β is largely immaterial. Conversely, in sparse networks, those with a high proportion of pendant nodes, or those with many chains, the value of β has a substantial effect on the power indices the measure generates. Next I consider whether power indices produced by interior values of β might be represented as a linear combination of “pure” vectors, those generated with values of β at either end of the parameter range and β = 0. I find that in the vast majority of cases a linear combination of “pure” vectors power is equivalent to using indices produced by interior values of β, making the choice of β largely moot. Finally, in the unlikely case that this disaggregation is inappropriate, I discuss the question of determining an appropriate value of β empirically.
{"title":"Choosing the ‘β’ Parameter When Using the Bonacich Power Measure","authors":"Simon Rodan","doi":"10.21307/JOSS-2019-032","DOIUrl":"https://doi.org/10.21307/JOSS-2019-032","url":null,"abstract":"Abstract Bonacich (1987) suggested a family of centrality measures that provide a useful way of modeling questions of power and network constraint. However, the literature offers little guidance regarding the choice of β, the parameter which alters the way the measure accounts for the effect of having powerful contacts in ones network. In this paper I explore the way the choice of the β parameter affects the power indices the Bonacich measure generates. I consider three network properties which might affect the way the choice of β influences the Bonacich power indices. I find that in high density networks with few internal ‘chains’ and few pendants, the choice of β is largely immaterial. Conversely, in sparse networks, those with a high proportion of pendant nodes, or those with many chains, the value of β has a substantial effect on the power indices the measure generates. Next I consider whether power indices produced by interior values of β might be represented as a linear combination of “pure” vectors, those generated with values of β at either end of the parameter range and β = 0. I find that in the vast majority of cases a linear combination of “pure” vectors power is equivalent to using indices produced by interior values of β, making the choice of β largely moot. Finally, in the unlikely case that this disaggregation is inappropriate, I discuss the question of determining an appropriate value of β empirically.","PeriodicalId":35236,"journal":{"name":"Journal of Social Structure","volume":"12 1","pages":"1 - 23"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67667050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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