Governing Social-Ecological Systems
In: Handbook of Computational Economics
AbstractSocial-ecological systems are complex adaptive systems where social and biophysical agents are interacting at multiple temporal and spatial scales. The main challenge for the study of governance of social-ecological systems is improving our understanding of the conditions under which cooperative solutions are sustained, how social actors can make robust decisions in the face of uncertainty and how the topology of interactions between social and biophysical actors affect governance. We review the contributions of agent-based modeling to these challenges for theoretical studies, studies which combines models with laboratory experiments and applications of practical case studies.Empirical studies from laboratory experiments and field work have challenged the predictions of the conventional model of the selfish rational agent for common pool resources and public-good games. Agent-based models have been used to test alternative models of decision-making which are more in line with the empirical record. Those models include bounded rationality, other regarding preferences and heterogeneity among the attributes of agents. Uncertainty and incomplete knowledge are directly related to the study of governance of social-ecological systems. Agent-based models have been developed to explore the consequences of incomplete knowledge and to identify adaptive responses that limited the undesirable consequences of uncertainties. Finally, the studies on the topology of agent interactions mainly focus on land use change, in which models of decision-making are combined with geographical information systems.Conventional approaches in environmental economics do not explicitly include non-convex dynamics of ecosystems, non-random interactions of agents, incomplete understanding, and empirically based models of behavior in collective action. Although agent-based modeling for social-ecological systems is in its infancy, it addresses the above features explicitly and is therefore potentially useful to address the current challenges in the study of governance of social-ecological systems.
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- Happe, Kathrin & Balmann, Alfons, 2008. "Doing Policy In The Lab! Options For The Future Use Of Model-Based Policy Analysis For Complex Decision-Making," 107th Seminar, January 30-February 1, 2008, Sevilla, Spain 6588, European Association of Agricultural Economists.
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TSE Working Papers
09-100, Toulouse School of Economics (TSE).
- Stefan Ambec & Alexis Garapin & Laurent Muller & Arnaud Reynaud & Carine Sebi, 2009. "Comparing regulations to protect the commons: an experimental investigation," LERNA Working Papers 09.18.294, LERNA, University of Toulouse.
- Ambec, S. & Garapin, A. & Muller, L. & Reynaud, A. & Sebi, C., 2013. "Comparing regulations to protect the commons: an experimental investigation," Working Papers 2013-07, Grenoble Applied Economics Laboratory (GAEL).
- Tesfatsion, Leigh, 2006. "Agent-Based Computational Modeling And Macroeconomics," Staff General Research Papers 12402, Iowa State University, Department of Economics.
- Jeroen Bergh, 2007.
"Evolutionary thinking in environmental economics,"
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