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Bioinvasions and GlobalizationEcology, Economics, Management, and Policy$
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Charles Perrings, Harold Mooney, and Mark Williamson

Print publication date: 2009

Print ISBN-13: 9780199560158

Published to Oxford Scholarship Online: May 2010

DOI: 10.1093/acprof:oso/9780199560158.001.0001

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Optimal Random Exploration for Trade‐related Nonindigenous Species Risk

Optimal Random Exploration for Trade‐related Nonindigenous Species Risk

Chapter:
(p.127) Chapter 10 Optimal Random Exploration for Trade‐related Nonindigenous Species Risk
Source:
Bioinvasions and Globalization
Author(s):

Michael Springborn

Christopher Costello

Peyton Ferrier

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199560158.003.0010

This chapter identifies variables from the port inspection setting that influence the gains to exploration via random inspections. It begins by describing a Bayesian learning model of trade-related non-indigenous species (NIS) risk in Section 10.2, which captures uncertainty over the true probability that trade from a given source is infested, and provides a framework for updating these beliefs as observations accrue. The formal inspection allocation decision problem is expressed in Section 10.3, where the computational demands of the central task are made clear. The analysis in Section 10.4 begins with the simplest possible nontrivial version of the problem. Elements of real-world complexity are subsequently added to build intuition for the ultimate task of exploring random inspection policy in an empirically-based setting. The workhorse method applies Monte Carlo simulation under various policies to characterize performance in terms of interceptions and to identify optimal choices for design and intensity of exploration.

Keywords:   non-indigenous species, invasive species, biological invasions, inspection allocation, Bayesian learning model, random inspections, port inspection

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