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Dynamic Programming and Learning Models for Management of a Nonnative Species

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Author Info

  • Mark Eiswerth
  • G. Cornelis van Kooten
  • Jeff Lines
  • Alison Eagle

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.

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File URL: https://web.uvic.ca/~repa/publications/REPA%20working%20papers/WorkingPaper2005-07.pdf
File Function: Final version, 2005
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Bibliographic Info

Paper provided by University of Victoria, Department of Economics, Resource Economics and Policy Analysis Research Group in its series Working Papers with number 2005-07.

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Length: 29 pages
Date of creation: Jul 2005
Date of revision:
Handle: RePEc:rep:wpaper:2005-07

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Related research

Keywords: Invasive weed species; optimal control; adaptive management;

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Cited by:
  1. Carrasco, L.R. & Mumford, J.D. & MacLeod, A. & Knight, J.D. & Baker, R.H.A., 2010. "Comprehensive bioeconomic modelling of multiple harmful non-indigenous species," Ecological Economics, Elsevier, vol. 69(6), pages 1303-1312, April.
  2. Eiswerth, Mark E. & Yen, Steven T. & van Kooten, G. Cornelis, 2011. "Factors determining awareness and knowledge of aquatic invasive species," Ecological Economics, Elsevier, vol. 70(9), pages 1672-1679, July.
  3. Kimberly Burnett & Sittidaj Pongkijvorasin & James Roumasset, 2012. "Species Invasion as Catastrophe: The Case of the Brown Tree Snake," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 51(2), pages 241-254, February.

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