Data from: Strategies for sustainable management of renewable resources during environmental change

As a consequence of global environmental change, management strategies
that can deal with unexpected change in resource dynamics are becoming
increasingly important. In this paper we undertake a novel approach to
studying resource growth problems using a computational form of
adaptive management to find optimal strategies for prevalent natural
resource management dilemmas. We scrutinize adaptive management, or
learning-by-doing, to better understand how to simultaneously manage
and learn about a system when its dynamics are unknown. We study
important trade-offs in decision-making with respect to choosing
optimal actions (harvest efforts) for sustainable management during
change. This is operationalized through an artificially intelligent
model where we analyze how different trends and fluctuations in growth
rates of a renewable resource affect the performance of different
management strategies. Our results show that the optimal strategy for
managing resources with declining growth is capable of managing
resources with fluctuating or increasing growth at a negligible cost,
creating in a management strategy that is both efficient and robust
towards future unknown changes. To obtain this strategy, adaptive
management should strive for: high learning rates to new knowledge,
high valuation of future outcomes and modest exploration around what
is perceived as the optimal action.