Download Modelling Dynamic Policy Networks for Managing

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.