Dynamic Programming and Learning Models for Management of a Nonnative Species
Gerrit van Kooten,
Jeff Lines and
No 2005-07, Working Papers from University of Victoria, Department of Economics, Resource Economics and Policy Analysis Research Group
Nonnative invasive species result in sizeable economic damages and expensive control costs. Because dynamic optimization models break down if controls depend in complex ways on past controls, non-uniform or scale-dependent spatial attributes, etc., decision support systems that allow learning may be preferred. We compare three models of an invasive weed in California’s grazing lands: (1) a stochastic dynamic programming model, (2) a reinforcement-based, experience-weighted attraction (EWA) learning model, and (3) an EWA model that also includes stochastic forage growth and penalties for repeated application of environmentally harmful control techniques. Results indicate that EWA learning models may be appropriate for invasive species management.
Keywords: Invasive weed species; optimal control; adaptive management (search for similar items in EconPapers)
JEL-codes: C73 Q57 (search for similar items in EconPapers)
Pages: 29 pages
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
https://web.uvic.ca/~repa/publications/REPA%20work ... kingPaper2005-07.pdf Final version, 2005 (application/pdf)
Journal Article: Dynamic Programming and Learning Models for Management of a Nonnative Species (2007)
Working Paper: Dynamic Programming and Learning Models for Management of a Nonnative Species (2005)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:rep:wpaper:2005-07
Access Statistics for this paper
More papers in Working Papers from University of Victoria, Department of Economics, Resource Economics and Policy Analysis Research Group Contact information at EDIRC.
Bibliographic data for series maintained by G.C. van Kooten ().