Dynamic Programming and Learning Models for Management of a Nonnative Species

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.


Issue Date:
2005-07
Publication Type:
Working or Discussion Paper
PURL Identifier:
http://purl.umn.edu/37015
Total Pages:
29
JEL Codes:
C73; Q57
Series Statement:
REPA Working Paper
2005-07




 Record created 2017-04-01, last modified 2017-04-04

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