Comparison between perfect information and passive-adaptive social learning models of forest harvesting

This paper compares perfect information and passive-adaptive social learning models of forest harvesting using a simple Markov chain model for land-use dynamics. A perfect information model assumes that landowners know true utility values of forest conservation and harvesting. In contrast, a passive-adaptive social learning model assumes that landowners do not know true utility values and they learn these values by their past experiences and by exchanging information with others in a society. We determine conditions under which the same consequences expected from perfect information and passive-adaptive social learning models. We found that the outcome from a perfect information model resembles that from passive-adaptive social learning model only when the perfect information model incorporates little discounting for future values. The stability analysis of landscape dynamics predicts a cyclic overexploitation of forest resources in a passive-adaptive social learning model with short-term memory, while instability of landscapes is never expected in a perfect information model. We discuss the role of discounting the future and discounting the past in the context of forest management.