title: Human adaptive behavior in common pool resource systems.
abstract: Overexploitation of common-pool resources, resulting from uncooperative harvest behavior, is a major problem in many social-ecological systems. Feedbacks between user behavior and resource productivity induce non-linear dynamics in the harvest and the resource stock that complicate the understanding and the prediction of the co-evolutionary system. With an adaptive model constrained by data from a behavioral economic experiment, we show that users' expectations of future pay-offs vary as a result of the previous harvest experience, the time-horizon, and the ability to communicate. In our model, harvest behavior is a trait that adjusts to continuously changing potential returns according to a trade-off between the users' current harvest and the discounted future productivity of the resource. Given a maximum discount factor, which quantifies the users' perception of future pay-offs, the temporal dynamics of harvest behavior and ecological resource can be predicted. Our results reveal a non-linear relation between the previous harvest and current discount rates, which is most sensitive around a reference harvest level. While higher than expected returns resulting from cooperative harvesting in the past increase the importance of future resource productivity and foster sustainability, harvests below the reference level lead to a downward spiral of increasing overexploitation and disappointing returns.
authors.affiliation: Systems Ecology, Leibniz Center for Tropical Marine Ecology, Bremen, Germany. gunnar.brandt@zmt-bremen.de
authors.forename: Gunnar
authors.initials: G
authors.lastname: Brandt
authors.forename: Agostino
authors.initials: A
authors.lastname: Merico
authors.forename: Björn
authors.initials: B
authors.lastname: Vollan
authors.forename: Achim
authors.initials: A
authors.lastname: Schlüter
mesh_terms: D000223:Adaptation, Psychological; D001519:Behavior; D003247:Conservation of Natural Resources; D006801:Humans; D008962:Models, Theoretical