Synapses in the Network: Learning in Governance Networks in the Context of Environmental Management

Autor(en): Newig, Jens
Guenther, Dirk
Pahl-Wostl, Claudia 
Stichwörter: ADAPTATION; collaboration; collective learning; COMMUNICATION PATTERNS; deliberation; DIMENSIONS; Ecology; effectiveness; Environmental Sciences & Ecology; Environmental Studies; FRAMEWORK; information diffusion; KNOWLEDGE; network governance; network resilience; POLICY; RESOURCES; social network analysis
Erscheinungsdatum: 2010
Herausgeber: RESILIENCE ALLIANCE
Journal: ECOLOGY AND SOCIETY
Volumen: 15
Ausgabe: 4
Zusammenfassung: 
In the face of apparent failures to govern complex environmental problems by the central state, new modes of governance have been proposed in recent years. Network governance is an emerging concept that has not yet been consolidated. In network governance, processes of (collective) learning become an essential feature. The key issue approached here is the mutual relations between network structure and learning, with the aim of improving environmental management. Up to now, there have been few attempts to apply social network analysis (SNA) to learning and governance issues. Moreover, little research exists that draws on structural characteristics of networks as a whole, as opposed to actor-related network measures. Given the ambiguities of the concepts at stake, we begin by explicating our understanding of both networks and learning. In doing so, we identify the pertinent challenge of individual as opposed to collective actors that make up a governance network. We introduce three learning-related functions that networks can perform to different degrees: information transmission, deliberation, and resilience. We address two main research questions: (1) What are the characteristics of networks that foster collective learning in each of the three dimensions? To this end, we consider SNA-based network measures such as network size, density, cohesion, centralization, or the occurrence of weak as opposed to strong ties. (2) How does collective learning alter network structures? We conclude by outlining a number of open issues for further research.
ISSN: 17083087

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