Information Network Topologies for Enhanced Local Adaptive Management |
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Authors: | Örjan Bodin Jon Norberg |
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Institution: | (1) Department of Systems Ecology, Stockholm University, 106 91, Stockholm, Sweden |
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Abstract: | We examined the principal effects of different information network topologies for local adaptive management of natural resources.
We used computerized agents with adaptive decision algorithms with the following three fundamental constraints: (1) Complete
understanding of the processes maintaining the natural resource can never be achieved, (2) agents can only learn by experimentation
and information sharing, and (3) memory is limited. The agents were given the task to manage a system that had two states:
one that provided high utility returns (desired) and one that provided low returns (undesired). In addition, the threshold
between the states was close to the optimal return of the desired state. We found that networks of low to moderate link densities
significantly increased the resilience of the utility returns. Networks of high link densities contributed to highly synchronized
behavior among the agents, which caused occasional large-scale ecological crises between periods of stable and high utility
returns. A constructed network involving a small set of experimenting agents was capable of combining high utility returns
with high resilience, conforming to theories underlying the concept of adaptive comanagement. We conclude that (1) the ability
to manage for resilience (i.e., to stay clear of the threshold leading to the undesired state as well as the ability to re-enter
the desired state following a collapse) resides in the network structure and (2) in a coupled social–ecological system, the
systemwide state transition occurs not because the ecological system flips into the undesired state, but because managers
lose their capacity to reorganize back to the desired state.
An erratum to this article can be found at . |
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Keywords: | Adaptive management Local adaptive management Social networks Multiagent simulation Information exchange Resilience |
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