| Registro completo |
| Provedor de dados: |
AgEcon
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| País: |
United States
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| Título: |
Dynamic Programming and Learning Models for Management of a Nonnative Species
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| Autores: |
Eiswerth, Mark E.
van Kooten, G. Cornelis
Lines, Jeff M.
Eagle, Alison J.
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| Data: |
2008-06-09
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| Ano: |
2005
|
| Palavras-chave: |
Invasive weed species
Optimal control
Adaptive management
Environmental Economics and Policy
C73
Q57
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| Resumo: |
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.
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| Tipo: |
Working or Discussion Paper
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| Idioma: |
Inglês
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| Identificador: |
http://purl.umn.edu/37015
|
| Relação: |
University of Victoria>Resource and Environmental Economics and Policy Analysis Research Group>Working Papers
REPA Working Paper
2005-07
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| Formato: |
29
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