Chapter 10 Optimal Random Exploration for Trade‐related Nonindigenous Species Risk
Michael Springborn, Christopher Costello, Peyton Ferrier · 2009
Abstract 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.