Harvesting heterogeneous renewable resources: Uncoordinated, selfish, team-, and community-oriented strategies

Brede, M. and De Vries, H.J.M. (2010) Harvesting heterogeneous renewable resources: Uncoordinated, selfish, team-, and community-oriented strategies. Environmental Modelling & Software, 25, (1), 117-128. (doi:10.1016/j.envsoft.2009.07.007).


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Using the example of a fishing fleet harvesting in different fishing zones with different carrying capacities and growth rates, we investigate strategies for the exploitation of distributed renewable resources by a crowd of agents without centralized coordination. In agent-based simulations we compare the performance of uncoordinated random harvesting, team playing, selfish individualistic and community-oriented (Collective Intelligence or COIN) behaviours operating with long and short timehorizon planning. Demonstrating the usefulness of COIN-based harvesting, more cooperative long-term planning strategies are found to relieve the pressure on the resource, reduce fluctuations and diminish the risk of overharvesting. Further, the outcome of an evolutionary dynamics where strategies in the agent population spread proportional to relative economic performance are strongly influenced by the harvesting pressure. In order of decreasing resource abundance we find that first an uncoordinated random, then a cooperative COIN-strategy and later the selfish strategy for an overharvested resource dominate the agent population. We also report that increasing harvesting pressure increasingly favours short-term and more individualistic strategies.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1016/j.envsoft.2009.07.007
ISSNs: 1364-8152 (print)
Keywords: sustainable resource exploitation, collective intelligence, evolutionary stable strategies, agent-based model
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
S Agriculture > SH Aquaculture. Fisheries. Angling
Divisions : Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Agents, Interactions & Complexity
ePrint ID: 272853
Accepted Date and Publication Date:
January 2010Published
Date Deposited: 26 Sep 2011 16:06
Last Modified: 31 Mar 2016 14:22
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/272853

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