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As the principal outlet for the Public Policy Section of the American
Political Science Association and for the Policy Studies Organization,
the Policy Studies Journal (PSJ) is the premier channel for
theoretically and empirically grounded research on policy process and
analysis.  PSJ is co-edited by Peter deLeon and Chris Weible (University
of Colorado Denver) 
This is a request for abstracts for a special issue on statistical
models of political networks that will be directed by guest editors,
Mark Lubell (University of California Davis), John Scholz (Florida State
University), Ramiro Berardo (University of Arizona), and Garry Robins
(The University of Melbourne).   The anticipated publication for this
special issue is 2012.  A workshop for the contributors may accompany
this special issue.  Send your questions and abstracts (approximately
300 words) to Mark Lubell ([log in to unmask]) by September 1, 2010.  

The main goal is to demonstrate to the general audience of policy
scholars how statistical models of policy networks can be used to test
core hypotheses of policy theories.  Network concepts are central parts
of many different policy theories (advocacy coalitions, issue networks,
and social capital are three prominent examples), and cutting-edge
research in policy networks aims to use statistical models to
empirically represent these policy theory concepts. The primary task of
this enterprise is to translate policy theory into the empirical realm
of network analysis, which can help identify how network structures
evolve in different settings and how these structures affect other
variables including individual behavior. 	
Statistical models of networks are useful for testing hypotheses that
seek to uncover the inner workings of these complex processes.  The
majority of published research on policy networks relies on a
combination of network visualization techniques and descriptive
statistics. Statistical models complement descriptive network analysis
by providing a framework for making inferences about the social
processes underlying network formation.  Most statistical models are
based on (explicit or implicit) probability distributions of graphs that
describe how links are formed between nodes and/or the structural
patterns present among links. Some directly model the network structure;
others use simulated null distributions against which to compare
observed network structure. Other statistical models examine diffusion,
influence and other processes on a network; or infer latent classes of
nodes with similar structural roles or positions. Some approaches model
cross-sectional, and others longitudinal, network data. The magnitude
and direction of the empirically estimated parameters are linked to
policy theory hypotheses.

This special issue seeks to be a one-stop shop for policy scientists
interested in applying these models, including some of the underlying
methodological intuitions and interpretation.  The first article reviews
how policy theory and statistical models of networks have converged in
policy research.  The second article summarizes the basic idea of
statistical models of networks for readers more familiar with
traditional multivariate analysis, and introduces some of the most
commonly used statistical models in policy research, including
Exponential Random Graph Models and Stochastic Actor Oriented Models.
The remaining articles are solicited from the research community, and
will include applications of network models that test policy theory in
different substantive policy domains.  US policy, comparative, and
international policy applications are all within the scope of this
solicitation. 	   

Garry Robins
University of Melbourne, Australia

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