Can Mavi (PSAE)
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.