Too many options: How to identify coalitions in a policy network?

Thibaud Deguilhem (), Juliette Schlegel (), Jean-Philippe Berrou, Ousmane Djibo () and Alain Piveteau ()
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Thibaud Deguilhem: LADYSS - Laboratoire Dynamiques Sociales et Recomposition des Espaces - UP1 - Université Paris 1 Panthéon-Sorbonne - UP8 - Université Paris 8 Vincennes-Saint-Denis - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité
Juliette Schlegel: LADYSS - Laboratoire Dynamiques Sociales et Recomposition des Espaces - UP1 - Université Paris 1 Panthéon-Sorbonne - UP8 - Université Paris 8 Vincennes-Saint-Denis - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité
Ousmane Djibo: IRD Représentation du Niger - IRD - Institut de Recherche pour le Développement
Alain Piveteau: LAM - Les Afriques dans le monde - IEP Bordeaux - Sciences Po Bordeaux - Institut d'études politiques de Bordeaux - IRD - Institut de Recherche pour le Développement - Institut d'Études Politiques [IEP] - Bordeaux - UBM - Université Bordeaux Montaigne - CNRS - Centre National de la Recherche Scientifique

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Abstract: For different currents in policy analysis as policy networks and the Advocacy Coalition Framework (ACF), identifying coalitions from policy beliefs and coordination between actors is crucial to a precise understanding of a policy process. Focusing particularly the relational dimension of ACF approaches linked with policy network analysis, determining policy subsystems from the actor collaborations and exchanges has recently begun offering fertile links with the network analysis. Studies in this way frequently apply Block Modeling and Community Detection (BMCD) strategies to define homogeneous political groups. However, the BMCD literature is growing quickly, using a wide variety of algorithms and interesting selection methods that are much more diverse than those used in the policy network analysis and particularly the ACF when this current focused on the collaboration networks before or after regarding the belief distance between actors. Identifying the best methodological option in a specific context can therefore be difficult and few ACF studies give an explicit justification. On the other hand, few BMCD publications offer a systematic comparison of real social networks and they are never applied to policy network datasets. This paper offers a new, relevant 5-Step selection method to reconcile advances in both the policy networks/ACF and BMCD. Using an application based on original African policy network data collected in Madagascar and Niger, we provide a useful set of practical recommendations for future ACF studies using policy network analysis: (i) the density and size of the policy network affect the identification process, (ii) the ''best algorithm'' can be rigorously determined by maximizing a novel indicator based on convergence and homogeneity between algorithm results, (iii) researchers need to be careful with missing data: they affect the results and imputation does not solve the problem.

Keywords: Advocacy Coalition Framework; Block modeling; Community detection; Normalized Mutual Information; Policy networks (search for similar items in EconPapers)
Date: 2024-10
New Economics Papers: this item is included in nep-net
Note: View the original document on HAL open archive server: https://hal.science/hal-04689665v1
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Published in Social Networks, 2024, 79, pp.104-121. ⟨10.1016/j.socnet.2024.06.005⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04689665

DOI: 10.1016/j.socnet.2024.06.005

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