2.0 Specifications For The Simulation Model

The selective fishery simulation model (SFM) was designed to have the capability to run in either deterministic or stochastic mode. Although the use of a stochastic model complicates both the computer coding of the model and the interpretation of results, such an approach is warranted for several reasons:

1. A Monte Carlo simulation facilitates the presentation of a confidence interval rather than a point estimate for statistics of interest. A confidence interval provides managers with an understanding of the uncertainty of the results. It is unlikely that the result in any year would be equal to the point estimate;

2. A Monte Carlo simulation provides a means to evaluate the effect of selective fisheries with respect to other processes affecting the stock. The change in escapement which would result from selective fisheries will be the result of a number of stochastic processes. These processes may have a large affect upon escapement relative to a selective fishery;

3. Due to Jensen's Inequality (Dudewicz and Mishra 1988), the expected (or average) value of a statistic defined by a nonlinear function is not equal to the function evaluated at the expected values of the parameters; and

4. The distribution of a statistic that is the result of a sequence of stochastic processes (e.g., catch by stock in a fishery) may not be adequately defined by the distribution of a higher order statistic that is dependent upon those stochastic processes (e.g., exploitation rate of a stock in a fishery).