A Computational Turn in Policy Process Studies: Coevolving Network Dynamics of Policy Change. (13th April 2022)

• Record Type: Journal Article
• Authors: Stauffer, Maxime; Mengesha, Isaak; Seifert, Konrad; Krawczuk, Igor; Fischer, Jens; Di Marzo Serugendo, Giovanna
• Other Names: Teixeira de Melo Ana Academic Editor.
• Is Part Of: Complexity. Volume 2022(2022)
• Publication Date: 2022-04-13
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2022/8210732
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : The past three decades of policy process studies have seen the emergence of a clear intellectual lineage with regard to complexity. Implicitly or explicitly, scholars have employed complexity theory to examine the intricate dynamics of collective action in political contexts. However, the methodological counterparts to complexity theory, such as computational methods, are rarely used and, even if they are, they are often detached from established policy process theory. Building on a critical review of the application of complexity theory to policy process studies, we present and implement a baseline model of policy processes using the logic of coevolving networks. Our model suggests that an actor's influence depends on their environment and on exogenous events facilitating dialogue and consensus-building. Our results validate previous opinion dynamics models and generate novel patterns. Our discussion provides ground for further research and outlines the path for the field to achieve a computational turn.