The problem of institutional fit : uncovering patterns with boosted decision trees
Epstein, Graham (University of Waterloo. School of Environment, Resources and Sustainability)
Apetrei, Cristina I. (Leuphana University, Faculty of Sustainability)
Baggio, Jacopo (University of Central Florida. School of Politics, Security and International Affairs)
Chawla, Sivee (James Cook University. ARC Centre of Excellence for Coral Reef Studies)
Cumming, Graeme S. (James Cook University. ARC Centre of Excellence for Coral Reef Studies)
Gurney, Georgina (James Cook University. ARC Centre of Excellence for Coral Reef Studies)
Morrison, Tiffany (James Cook University. ARC Centre of Excellence for Coral Reef Studies)
Unnikrishnan, Hita (Sheffield University. Urban Institute)
Villamayor Tomás, Sergio (Universitat Autònoma de Barcelona. Institut de Ciència i Tecnologia Ambientals)

Data:	2024
Descripció:	16 pàg.
Resum:	Complex social-ecological contexts play an important role in shaping the types of institutions that groups use to manage resources, and the effectiveness of those institutions in achieving social and environmental objectives. However, despite widespread acknowledgment that "context matters", progress in generalising how complex contexts shape institutions and outcomes has been slow. This is partly because large numbers of potentially influential variables and non-linearities confound traditional statistical methods. Here we use boosted decision trees - one of a growing portfolio of machine learning tools - to examine relationships between contexts, institutions, and their performance. More specifically we draw upon data from the International Forest Resources and Institutions (IFRI) program to analyze (i) the contexts in which groups successfully self-organize to develop rules for the use of forest resources (local rulemaking), and (ii) the contexts in which local rulemaking is associated with successful ecological outcomes. The results reveal an unfortunate divergence between the contexts in which local rulemaking tends to be found and the contexts in which it contributes to successful outcomes. These findings and our overall approach present a potentially fruitful opportunity to further advance theories of institutional fit and inform the development of policies and practices tailored to different contexts and desired outcomes.
Nota:	Unidad de excelencia María de Maeztu CEX2019-000940-M
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Collective action ; Community-based management ; Context ; Environmental governance ; Institutional fit ; Machine learning ; Collective action; Community-based management; Context; Environmental governance; Institutional fit; Machine learning
Publicat a:	International Journal of the Commons, Vol. 18, issue 1 (2024) , p. 1-16, ISSN 1875-0281

DOI: 10.5334/ijc.1226

16 p, 1.2 MB

El registre apareix a les col·leccions:
Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències > Institut de Ciència i Tecnologia Ambientals (ICTA)
Articles > Articles de recerca
Articles > Articles publicats