Deforestation often has been studied in terms of land-use models, in which natural processes such as ecological succession, physical disturbance and human decision-making are combined. In many land-use models, landowners are assumed to make decisions that maximize their utilities. However, since human understanding of ecological and social dynamics is clouded by uncertainty, landowners may not know true utility values, and may learn these values from their experiences. We develop a decision model for forest use under social learning to explore whether social learning is efficient to improve landowners' decisions and can lead to effective forest management. We assume that a forest is composed of a number of land parcels that are individually managed; landowners choose whether or not to cut trees by comparing the expected utilities of forest conservation and deforestation; landowners learn utility values not only from their own experiences, but also by exchanging and sharing information with others in a society. By analyzing the equilibrium and stability of the landscape dynamics, we observed four possible outcomes: a stationary-forested landscape, a stationary-deforested landscape, an unstable landscape fluctuating near an equilibrium, and a cyclic-forested landscape induced by synchronized deforestation. Synchronized deforestation, which resulted in a resource shortage in a society, was likely to occur when landowners employed a stochastic decision and a short-term memory about past experiences. Social welfare under a cyclic-forested landscape can be significantly lower than that of a stationary-forested landscape. This implies that learning and remembering past experiences are crucial to prevent overexploitation of forest resources and degradation of social welfare.
All Science Journal Classification (ASJC) codes
- General Environmental Science
- Economics and Econometrics