arXiv subjects

Michael D. Siciliano

Publications and source records attributed to Michael D. Siciliano.

5 recordsLinked to original sources

arXiv2024

Network Interventions: Applying Network Science for Pragmatic Action in Public Administration and Policy

Public management and policy scholars have engaged in extensive development of theory and empirical study of networks and collaborative systems of governance. This scholarship has focused on understanding the mechanisms of network formation and the implications of network properties on individual and collective outcomes. Despite rich descriptive work and inferential analyses, little work has attempted to intervene in these systems. In this article, we develop the foundation for a new body of research in our field focused on network interventions. Network interventions are defined as the purposeful use of network data to identify strategies for accelerating behavior change, improving performance, and producing desirable outcomes (Valente, 2012). We extend network intervention strategies from the field of public health to public sector inter-organizational and governance networks. Public sector actors have an interest in network interventions based on the fundamental pursuit of efficiency, effectiveness, and equity. Network interventions can increase the uptake of an organizational change among employees, improve the performance of a governance system, or promote the spread of a successful policy across jurisdictions. We provide scholars and practitioners with a useful way to conceptualize where, why, and how network interventions might be deployed in the pursuit of public value.

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arXiv2024

Network Inference in Public Administration: Questions, Challenges, and Models of Causality

Descriptive and inferential social network analysis has become common in public administration studies of network governance and management. A large literature has developed in two broad categories: antecedents of network structure, and network effects and outcomes. A new topic is emerging on network interventions that applies knowledge of network formation and effects to actively intervene in the social context of interaction. Yet, the question remains how might scholars deploy and determine the impact of network interventions. Inferential network analysis has primarily focused on statistical simulations of network distributions to produce probability estimates on parameters of interest in observed networks, e.g. ERGMs. There is less attention to design elements for causal inference in the network context, such as experimental interventions, randomization, control and comparison networks, and spillovers. We advance a number of important questions for network research, examine important inferential challenges and other issues related to inference in networks, and focus on a set of possible network inference models. We categorize models of network inference into (i) observational studies of networks, using descriptive and stochastic methods that lack intervention, randomization, or comparison networks; (ii) simulation studies that leverage computational resources for generating inference; (iii) natural network experiments, with unintentional network-based interventions; (iv) network field experiments, with designed interventions accompanied by comparison networks; and (v) laboratory experiments that design and implement randomization to treatment and control networks. The article offers a guide to network researchers interested in questions, challenges, and models of inference for network analysis in public administration.

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arXiv2021

Network Effects Research: A Systematic Review of Theoretical Mechanisms and Measures

This article contributes to the network effectiveness literature by identifying the theoretical mechanisms and network measures scholars in public administration and policy use to draw inferences between network structures and network effects. We conducted a systematic review of empirical network effects research in 40 journals of public administration and policy from 1998 to 2019. We reviewed and coded 89 articles and described the main social theories used in the network effectiveness literature and the associated mechanisms that translate network structures to network effects. We also explain how scholars operationalize those theoretical mechanisms through network measures. Overall, our findings reflect that there is a limited use of social theories for the explanation of network effects and in some cases, there is an inconsistent use of network measures associated with theories. Moreover, we identify the main challenges related to network effects, including the difficulty of isolating specific mechanisms related to a particular social theory, the use of network structures both as a mechanism and as a measure, and the lack of data to examine network dynamics and coevolution.

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arXiv2020

Government as Network Catalyst: Accelerating Self-Organization in a Strategic Industry

Governments have long standing interests in preventing market failures and enhancing innovation in strategic industries. Public policy regarding domestic technology is critical to both national security and economic prosperity. Governments often seek to enhance their global competitiveness by promoting private sector cooperative activity at the inter-organizational level. Research on network governance has illuminated the structure of boundary-spanning collaboration mainly for programs with immediate public or non-profit objectives. Far less research has examined how governments might accelerate private sector cooperation to prevent market failures or to enhance innovation. The theoretical contribution of this research is to suggest that government programs might catalyze cooperative activity by accelerating the preferential attachment mechanism inherent in social networks. We analyze the long-term effects of a government program on the strategic alliance network of 451 organizations in the high-tech semiconductor industry between 1987 and 1999, using stochastic network analysis methods for longitudinal social networks.

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arXiv2017

GUIDES - Geospatial Urban Infrastructure Data Engineering Solutions

As the underground infrastructure systems of cities age, maintenance and repair become an increasing concern. Cities face difficulties in planning maintenance, predicting and responding to infrastructure related issues, and in realizing their vision to be a smart city due to their incomplete understanding of the existing state of the infrastructure. Only few cities have accurate and complete digital information on their underground infrastructure (e.g., electricity, water, natural gas) systems, which poses problems to those planning and performing construction projects. To address these issues, we introduce GUIDES as a new data conversion and management framework for urban underground infrastructure systems that enable city administrators, workers, and contractors along with the general public and other users to query digitized and integrated data to make smarter decisions. This demo paper presents the GUIDES architecture and describes two of its central components: (i) mapping of underground infrastructure systems, and (ii) integration of heterogeneous geospatial data.

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