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Publication Detailed Description
An incomplete information static game for the evaluation of the community based forest management in Zagros forests, Iran
Journal Title
Sustainability
Year (definitive publication)
2020
Language
English
Country
Switzerland
More Information
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Abstract
The present study adopts a game theory approach for analyzing land-use planning in Zagros forest, Iran, by applying a Static Game of Incomplete Information (SGII) to the evaluation of participatory management of forest landscape. This tool allows a complete evaluation of sustainable forest planning producing two modeling scenarios based on (i) high or (ii) low social acceptance. According to the SGII results, the Nash Bayesian Equilibrium (NBE) strategy demonstrates the importance of landscape protection in forest management. Results of the NBE analytical strategy show that landscape protection with barbed wires is the most used strategy in local forest management. Response of local community includes cooperation in high social acceptance condition and noncooperation in low social acceptance condition. Overall, social acceptance is an adaptive goal in forest management plans.
Acknowledgements
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Keywords
Decision making,Forest management,Nash Bayesian Equilibrium (NBE),Harsanyi's Transformation
Fields of Science and Technology Classification
- Mathematics - Natural Sciences
- Earth and related Environmental Sciences - Natural Sciences
- Agriculture, Forestry and Fisheries - Agriculture Sciences
- Economics and Business - Social Sciences
Contributions to the Sustainable Development Goals of the United Nations
With the objective to increase the research activity directed towards the achievement of the United Nations 2030 Sustainable Development Goals, the possibility of associating scientific publications with the Sustainable Development Goals is now available in Ciência-IUL. These are the Sustainable Development Goals identified by the author(s) for this publication. For more detailed information on the Sustainable Development Goals, click here.
