Dynamic Programming and Learning Models for Management of a Nonnative Species

Mark E. Eiswerth, Gerrit van Kooten, Jeff M. Lines and Alison J. Eagle

No 37015, Working Papers from University of Victoria, Resource Economics and Policy

Abstract: 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: Environmental; Economics; and; Policy (search for similar items in EconPapers)
Pages: 29
Date: 2005-07
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Related works:
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)
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Persistent link: https://EconPapers.repec.org/RePEc:ags:uvicwp:37015

DOI: 10.22004/ag.econ.37015

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