An Agent-based Model of Forest Use with Social Learning

Many people depend on forests for their livelihood, yet they are also traditionally one of the most overexploited resources. Deforestation has been studied using land-use models in which natural processes, such as ecological successions and physical disturbances, and social processes, such as human decision making, are combined. In such models, we assume that each landowner makes a decision that maximizes his utility. However, human understanding of ecological and social dynamics is clouded by uncertainty, and therefore it may be difficult for any individual to know the true utility values. To cope with uncertainty about the utility, landowners learn from their experiences to form an expectation about the relative utilities of their options. In this project, we develop an agent-based model of forest use under social learning, influenced by environmental conditions. We investigate following questions: (1) how social structure influences the learning process, and thus the decision about deforestation, (2) how global resource-use patterns result from the decisions of individual landowners, (3) whether the decision under social learning leads to optimal decision with respect perfect information. Based on results, we will discuss the policy implications for forest policy and management.

Any comments & suggestions are welcome! Akiko Satake