TY - JOUR
T1 - Synchronized deforestation induced by social learning under uncertainty of forest-use value
AU - Satake, Akiko
AU - Janssen, Marco A.
AU - Levin, Simon A.
AU - Iwasa, Yoh
N1 - Funding Information:
This work was supported in part by a fellowship and a grant-in aid from the Japan Society for the Promotion of Science (JSPS) to AS, a grant in aid from JSPS to YI, and the National Science Foundation, award DEB-0083566 to SAL. We thank the two anonymous reviewers for their helpful comments.
PY - 2007/8/1
Y1 - 2007/8/1
N2 - 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.
AB - 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.
KW - Decision-making
KW - Expected utility
KW - Markov chain
KW - Memory
KW - Slow regeneration
KW - Stochastic decision
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U2 - 10.1016/j.ecolecon.2006.11.018
DO - 10.1016/j.ecolecon.2006.11.018
M3 - Article
AN - SCOPUS:34248215211
SN - 0921-8009
VL - 63
SP - 452
EP - 462
JO - Ecological Economics
JF - Ecological Economics
IS - 2-3
ER -