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Title: Modelling Dynamic Policy Networks for Managing Climate Change in Ireland. PhD Candidate Paul Wagner Research Description: The importance of dynamic policy networks has long been emphasised within the field of policy analysis. However, few attempts have been made to investigate the explanatory power of policy networks using social network analysis techniques. Moreover, there is a scarcity of formal SNA models which are specifically elaborated to test theories about the policy network process and which may in turn contribute to the more general theoretical and methodological developments in the SNA field. The empirical focus of this doctoral research project is the policy networks process for climate change policy making in Ireland and in a comparative perspective. The dangers resulting from global climate change are already well documented. However the policy responses have varied widely by country. To understand the dynamics of the socio-political responses to global climate change, a systematic analysis is required of the composition of social networks and advocacy coalitions in order to understand how the actors within these networks interact with one another so as to influence the policy-making processes in Ireland. Part of this PhD research project, will also contribute to and participate in an international research programme COMPON (http://compon.org/), which is headed by Principal Investigator Dr. Jeffrey Broadbent of the University of Minnesota. Within the COMPON research programme, there are in excess of 20 participating national research teams who are applying a common research protocol, which facilitates cross-national comparative research also. Within the field of SNA with few exceptions, statistical analysis of social networks is currently focused on cross-sectional data. The first part of this research applies a cross-sectional analysis of policy networks: The analysis of actor’s positions in communication and resource exchange networks helps to identify central or peripheral actors. The analysis of actor positions with regard to policy preferences, brokerage positions, and influence reputation permit the assessment of the actors’ and advocacy coalitions’ relative impact on policy output. The analysis of cliques, cores, and blocks in networks generates information about network differentiation and integration. Key questions at this level are related to the intensity of communication (measured as density) between sectoral organizational clusters (e.g. between science and government). Information on overall network density, integration, and cohesion enables the comparison of the Irish policy network with other national networks. Cross-national comparison of national network analyses will begin to reveal more general principles about the structural and dynamic conditions that allow one or another type of advocacy groups and power coalitions to dominate the policy-formation process The second part of this research moves beyond this cross-sectional approach, by recognising that social networks are inherently dynamic and seeks to examine the Irish climate change policy process using a longitudinal network approach. First the research identifies a representative sample of the core, ongoing policy issues within climate change arena in Ireland. The research identifies the two-mode (bipartite) policy networks which represent affiliations of network actors with policy issue preferences, and one-mode policy actor networks representing affective, cooperation and exchange relations between these actors. Using a longitudinal design, this research examines if it is plausible that affiliation patterns will influence cooperation relations, and cooperation relations will influence affiliation patterns within the policy process. A continuous-time Markov chain model is proposed for the simultaneous dynamics of a one-mode and a two-mode network, and some specifications of the structural influences are discussed (Snijders et al, 2009). The project will also examine a representative sample of events in the policy process around climate change and which typically are routinely observed and regularly reported on in the digital media. As such this type of longitudinal network data consists of sequences of time-stamped events encoding interactions between actors. The availability of event data has grown considerably with the advent of automated data-collection facilities. The dyadic interaction identifies actor ties based communicating, working together, etc and apart from the advantage of a finer timegranularity, event data are often available in larger quantities and are independent of respondents’ subjectivity in assessing their relations. Drawing on the earlier research in this field by Brandes et al. (2009)1 we elaborate on this modelling approach for network data consisting of time-stamped, dyadic, weighted events, where the weight indicates the quality (hostile vs. friendly) of an event. to determine how the rate and quality of events is influenced by the state of the policy network. Examples or cooperative events are visits, agreements, and provision of financial aid; hostile events include accusations, threats, and sanctions or other such actions against another actor. Ulrik Brandes∗, J¨urgen Lerner∗, and Tom A. B. Snijders (2009) “Networks Evolving Step by Step: Statistical Analysis of Dyadic Event Data”. Advances in Social Network Analysis and Mining 1 Data The research will collect require the collection of different types of data, using a mix of data collection techniques such as policy expert interviews, network survey and software tools that automatically extract and code daily events from news. reports.. A standardised survey has been conceived of by the current participants of the COMPON project. It contains fixed-choice questions that aim to generate quantitative data describing the networks, policy preferences and resources of the relevent actors and organisations involved in climate policy debates. This standardised survey has been intentionally designed in a way that makes it applicable to all research teams affiliated with COMPON. The use of a standardised survey will generate variations in data that will be unique to each region being investigated. This will enable researchers to undertake a comparative analysis of how the issue of climate change is framed in relation to other participating countries. Proposed Research Supervision and Collaboration This project will be supervised by Dr Diane Payne (Principal Supervisor) , Professor Nial Friel. The doctoral training will involve spending some time at the University of Oxford and at the University of Groningen. Within the COMPON research programme, there are regular doctoral training events and a number of doctoral exchange opportunities available. Likewise the doctoral candidate will be encouraged to attend and present at international workshops and conferences where appropriate.