Baseline Models for Two‐Mode Social Network Data

Baseline models have been used in the analysis of social networks as a way to understand how empirical networks differ from “random” ones. For the purposes of social network analysis, a “random” network is one chosen—at random—from a population of possible graphs derived from a given generating function. Although these principled hypothesis tests have a long history, many of their properties and extensions to multiple data structures—here, specifically two‐mode data—have been overlooked. This article focused on applications of different baseline models to two data sets: donations and voting of the 111th U.S. Congress, and organizations involved in forums on watershed policy in San Francisco, USA. Tests using each data set, but with different baseline reference distributions, will illustrate the range of possible questions baseline models can address and the differences between them. The ability to apply different models and generate a constellation of results provides a deeper understanding of the structure of the system.     

Partnership Networks for Urban Development: How Structure is Shaped by Risk

In delivering public policy, governments worldwide increasingly partner with diverse sets of stakeholders. This spreads commercial risk, but particularly where agendas diverge, introduces new risks related to trust in relationships. The “risk hypothesis” distinguishes between networks for “cooperation” problems, where partners have high individual payoffs for uncooperative behaviors, and “coordination” problems, where partners subscribe to a common goal and uncooperative behaviors are less rewarding. We used mixed‐methods to study networks of local and state government, developers, and consultants that center on joint‐venture partnerships for developing new urban, residential projects. Statistical network methods showed that within the mix of partners involved in development projects, only state governments displayed structural patterns associated with solving “cooperation” problems (rather than coordination). In other words, the patterns of state government interactions showed they are most exposed to risky relationships. In contrast to the state governments’ apparent exposure to risk, qualitative data showed they are not only well trusted but also overall the partnership networks reported very low levels of conflict. By exploring the distribution of “cooperation” and “coordination,” we identified which stakeholders perceived most risk. In our case, how the state governments’ structure interactions in response to risky relationships leads to an overall network characterized by trust.     

Impacts of Market Liberalization on Regulatory Network: A Longitudinal Analysis of the Swiss Telecommunications Sector

This article looks at the reconfiguration of the regulatory actors' network, as induced by the liberalization and reregulation processes in utility sectors. It investigates the changes in governance structures and patterns of collaborative ties between actors resulting from these processes. Applying stochastic actor‐oriented modeling (SAOM) to data on the liberalization of the Swiss telecommunications sector over two decades, we test whether and to what extent structural changes driven by liberalization and reregulation express themselves through network effects, that is, through changing patterns of interactions between political authorities, regulators, regulatees, and interest groups. Our empirical tests highlight a rearrangement of the regulatory network and a reorganization of relational patterns around new actors, such as the sector‐specific regulatory agency, coregulators, and new operators.     

The Performance of Policy Networks: The Relation between Network Structure and Network Performance

The importance of policy networks has long been emphasized within the field of policy analysis. However, few attempts have been made to investigate the explanatory power of policy networks using the tools and theoretical concepts provided by social network analysis (SNA). This paper aims to address this need by determining if a relationship exists between the structural features of policy networks, their organizing capacities, and their performance. A comparative case study of four networks within the higher education policy sector confirms the assumption related to the existence of such a relation. It is proposed that an efficient and innovative policy network consists of a heterogeneous set of actors that are centrally and densely integrated. Furthermore, while the level of network heterogeneity is positively related to the function of resource mobilization in the process of policymaking, the level of centralized integration promotes the function of prioritizing. These findings are believed to contribute to our understanding of policymaking in contemporary society. The current paper indicates that a significant explanatory power exists in the concept of policy networks and that SNA is one way of advancing its possibilities.     

Network Structures within Policy Processes: Coalitions,Power, and Brokerage in Swiss Climate Policy

The Advocacy Coalition Framework (ACF) is a prominent approach to investigate the formation of coalition and their impact on policy outputs. Although the ACF combines both the network structures of a political process with actors' values and belief systems, most empirical tests focus mainly on beliefs rather than network structures. Considering a relational approach makes particular sense when one wants to investigate the structural patterns of a subsystem and to assess coalition formation and maintenance. The author therefore proceeds by taking two steps to study the existence of coalitions, power relations, and policy preferences: first, social network analysis frames the empirical study of network structures, based on the assumption that common beliefs are reflected in relations among actors involved in policy processes. Second, using a sophisticated mathematical algorithm, the multicriteria analysis furnishes a systematic evaluation of the elite's belief system. This methodological combination constitutes the added value of this research and allows for testing to establish if common beliefs are reflected in network structures.     

Reconceptualizing Major Policy Change in the Advocacy Coalition Framework: A Discourse Network Analysis of German Pension Politics

How does major policy change come about? This article identifies and rectifies weaknesses in the conceptualization of innovative policy change in the Advocacy Coalition Framework. In a case study of policy belief change preceding an innovative reform in the German subsystem of old‐age security, important new aspects of major policy change are carved out. In particular, the analysis traces a transition from one single hegemonic advocacy coalition to another stable coalition, with a transition phase between the two equilibria. The transition phase is characterized (i) by a bipolarization of policy beliefs in the subsystem and (ii) by state actors with shifting coalition memberships due to policy learning across coalitions or due to executive turnover. Apparently, there are subsystems with specific characteristics (presumably redistributive rather than regulative subsystems) in which one hegemonic coalition is the default, or the “normal state.” In these subsystems, polarization and shifting coalition memberships seem to interact to produce coalition turnover and major policy change. The case study is based on discourse network analysis, a combination of qualitative content analysis and social network analysis, which provides an intertemporal measurement of advocacy coalition realignment at the level of policy beliefs in a subsystem.     

Reconceptualizing the Policy Subsystem: Integration with Complexity Theory and Social Network Analysis

The concept of the policy subsystem is an essential building block for several of the basic frameworks of policy process studies. Over time issues have become more complex, crossing subsystem boundaries, and so subsystems have escalated in their complexity as well. It is increasingly insufficient to study just one policy subsystem and so scholars have turned to studying boundary‐spanning regimes or policy networks. In this essay, we review the major contributions to developing the concept of a policy subsystem and trace its evolution into broader conceptualizations like issue and policy networks. We argue that the future for theories of the policy process is in more explicit integration of complexity theory and more effective modeling of subsystems with the utilization of social network analysis. In closing, we discuss the enduring nature of the concept of policy subsystems and highlight studies that continue using it in innovative ways.