Agent-Based Modeling of Harvest Decisions by Small Scale Forest Landowners in Maine, USA
Small-scale forests are an excellent example of coupled social-ecological systems, which involve human and biophysical subsystems with complex two-way feedback interactions. The multifaceted nature of landowner decisions drives a significant need to better understand decision-making processes, react...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
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| Series: | International Journal of Forestry Research |
| Online Access: | http://dx.doi.org/10.1155/2013/563068 |
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| Summary: | Small-scale forests are an excellent example of coupled social-ecological systems, which involve human and biophysical subsystems with complex two-way feedback interactions. The multifaceted nature of landowner decisions drives a significant need to better understand decision-making processes, reactions to policy, and combined impacts on ecosystems in a comprehensive manner. Small-scale forests require an integrated approach to modeling the social and biophysical components comprehensively. Agent-based modeling involves modeling individualistic behavior and interpreting patterns that emerge. The interaction between agents and their environments makes this a valuable tool to assess repeated decisions of individual landowners responding to changing environmental conditions. Agent-based models can be used to determine potential ecological, economic, and social outcomes of landowner decisions and reactions to changing conditions. A forest landowner agent-based model experiment was developed to model timber harvesting in Maine, USA. We present baseline simulation results and compare the effect of a social change (an increased tax rate) and a biophysical change (a pest outbreak resulting in increased tree mortality) on the system. These three scenarios were analyzed using ANOVA and MANOVA tests on harvested hectares and landowner goal scores to assess landowner behavior and priorities by action. We conclude by reviewing implications for future modeling efforts. |
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| ISSN: | 1687-9368 1687-9376 |