Introduction

Victor, Jennifer Nicoll; Montgomery, Alexander H.; Lubell, Mark

doi:10.1093/oxfordhb/9780190228217.013.1

Cited by 14 publications

(21 citation statements)

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“…We clarify that our treatment here is by no means exhaustive. For example, there are numerous other structural quantities that could be added to the measures we considered here in Section 3 as described, for example, in [3, 5, 20, 25, 26, 27]. Our choice of the measures and models investigated here has been dictated to a substantial extent by their accessibility to practicing social scientists in the form of R packages.…”

Section: Resultsmentioning

confidence: 99%

“…Descriptive methods exploit measures based upon the structure of the network to quantify properties such as how connected or centralized the network is and quantifies the importance of each node in the network. Such methods abound in the literature and a comprehensive review is beyond the scope of this article [see, e.g., 28, 3, 13, 20, 25, 26, for excellent reviews]. Stochastic methods formulate statistical models for the network and include random graph models [24], latent space approaches [15], and several other probability models using matrix-variate distributions [8].…”

Section: Introductionmentioning

confidence: 99%

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Bayesian modeling and uncertainty quantification for descriptive social networks

Nemmers

Narayan

Banerjee

2019

Statistics and Its Interface

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This article presents a simple and easily implementable Bayesian approach to model and quantify uncertainty in small descriptive social networks. While statistical methods for analyzing networks have seen burgeoning activity over the last decade or so, ranging from social sciences to genetics, such methods usually involve sophisticated stochastic models whose estimation requires substantial structure and information in the networks. At the other end of the analytic spectrum, there are purely descriptive methods based upon quantities and axioms in computational graph theory. In social networks, popular descriptive measures include, but are not limited to, the so called Krackhardt’s axioms. Another approach, recently gaining attention, is the use of PageRank algorithms. While these descriptive approaches provide insight into networks with limited information, including small networks, there is, as yet, little research detailing a statistical approach for small networks. This article aims to contribute at the interface of Bayesian statistical inference and social network analysis by offering practicing social scientists a relatively straightforward Bayesian approach to account for uncertainty while conducting descriptive social network analysis. The emphasis is on computational feasibility and easy implementation using existing R packages, such as sna and rjags, that are available from the Comprehensive R Archive Network (https://cran.r-project.org/). We analyze a network comprising 18 websites from the US and UK to discern transnational identities, previously analyzed using descriptive graph theory with no uncertainty quantification, using fully Bayesian model-based inference.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian modeling and uncertainty quantification for descriptive social networks

Nemmers

Narayan

Banerjee

2019

Statistics and Its Interface

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“…Their work demonstrates the strong visual appeal of SNA and its effectiveness as a systems thinking tool in arranging the governance structure for FSM systems. In general, SNA is a powerful tool for highlighting the importance of public policy and governance that requires more exploration (Neely, 2013; Victor et al, 2017). However, there are several significant limitations of SNA methods for analyzing the risks emerging from governance structures.…”

Section: Literature Reviewmentioning

confidence: 99%

A systems theoretic process analysis (STPA) approach for analyzing the governance structure of fecal sludge management in Japan

Bugalia

Choudhury

Maemura

et al. 2022

Environment and Planning B: Urban Analytics and City Science

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Fecal sludge management (FSM) stakeholders have hitherto been unable to benefit from analytical approaches based on systems thinking principles, as studies utilizing theoretical frameworks to analyze governance structures remain in a nascent stage. One such prominent framework is the Systems Theoretic Process Analysis (STPA), which has no FSM-related applications in the literature. Relying on official documents such as ministerial ordinances, this study conducts a multistage STPA for a well-established and sustainable FSM system, that is, the Johkasou system in Japan. The analysis maps the key stakeholders and their interactions to comprehensively identify potential risks and corresponding novel indicators to help manage risks within the Johkasou system. The features of governance structure thus obtained also highlight the steps necessary to address governance issues for Japan and other countries attempting to establish sustainable FSM. The study extends the STPA literature by providing a substantial validity of STPA’s capability for analyzing the FSM governance structure against robust and independent criteria that define the objectives of a “good” governance structure, that is, efficiency, accountability, and legitimacy. The STPA results identify novel leading indicators to guide Japanese policymakers to improve FSM management. The results from STPA also effectively provide valuable insights for other countries by highlighting the various features contributing to an effective governance structure of the Johkasou, such as centralized decision-making in a combination of a hierarchy of goals to establish a clear division of responsibility across various actors. The study also notes potential challenges for field implementation of STPA and discusses directions that future work could explore.

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“…Structured interaction is a specific version of relationship that manifests itself through different types of organizational ties. In this sense, our approach echoes political network analysis: we consider established relationships to be (possible) constraints on the action of individual actors (Victor et al, 2017). That said, we still assume parties and groups at the outset choose the extent to which they would like to get or keep being involved in structured relations with others and hereby become organizationally interdependent.…”

Section: Relationships As Structured Interactionmentioning

confidence: 99%

Conceptualizing and measuring party-interest group relationships

et al. 2020

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Relationships between political parties and interest groups form structures that enable and constrain political action. Yet there is a lack of consensus on what ‘party-group relationships’ means. We propose a conceptualization focusing on ties as means for structured interaction, which is different from sharing or transfer of resources and ideological kinship. Our conceptual discussion suggests that organizational ties form a single yet hierarchical scale of strength: groups that have many formal ties with particular parties would also have weaker ties with these parties, but not vice versa. To validate our conceptual map, we furthermore check whether the distinction between organizational ties, resource sharing/provision and ideological kinship holds empirically. We explore our expectations by means of novel interest group survey data from seven mature democracies. The results of our scaling analysis provide support for our predictions and have multiple implications for future research on the causes and effects of party-group relationships.

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Introduction

Cited by 14 publications

References 0 publications

Bayesian modeling and uncertainty quantification for descriptive social networks

Bayesian modeling and uncertainty quantification for descriptive social networks

A systems theoretic process analysis (STPA) approach for analyzing the governance structure of fecal sludge management in Japan

Conceptualizing and measuring party-interest group relationships

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