Learning to Manage Mobile Resources: A Dynamic Spatial Framework under Uncertainty

This paper develops a dynamic model of natural resource management under uncertainty about spatial migration patterns. We consider a renewable resource, such as a fish stock, distributed across two spatial patches, where non-cooperative harvesters face uncertainty about self-retention and dispersal rates. Harvesters update beliefs over time by observing stock levels, actively learning through escapement decisions. Learning introduces two behavioral forces: a learning effect, which encourages reduced extraction to gain information, and a flexibility effect, which pushes for increased extraction to hedge against future disutility. Their interaction shapes optimal experimentation and strategic harvester behavior. Without learning, harvesters tend to mirror each other, but with learning, one harvester’s choices may lead the other to do the opposite—extracting less when the other extracts more—due to informational spillovers with learning through experimenting. In a cooperative setting, coordinated learning can result in higher total extraction than under non-cooperative behavior, challenging conventional wisdom from the tragedy of the commons. These findings offer novel insights for designing adaptive management strategies in contexts where spatial dynamics and uncertain resource mobility are critical.