Editor's Choice – Caution when using efficient algorithms

A selected Editor's Choice paper from ICES Journal of Marine Science (IJMS) is now available. Read about inconsistency in assessment model results and why caution is needed even with high accuracy in parameter estimation.
Published: 18 April 2018

​​​​​​​​​​​​​Models have become standard tools when assessing the state of fish stocks, evaluating recovery strategies, and in making sustainable harvest decisions. They typically consist of several interrelated equations to describe complex biological and ecological processes. These equations are expressed in terms of unknown variables (parameters) that collectively define the model parameter set.

This parameter set is estimated through computational procedures that use uncertain observations. A procedure (algorithm) is considered efficient when it is stable (i.e., given the same input information, it returns identical estimates of parameters), fast, and precise. Model predictions or model-based inference that use parameters from these efficient algorithms are considered reliable and optimal. The ADMB/TMB platforms currently used for fisheries modelling and stock assessment are built on such an algorithm.

This study uses simple illustrative examples to demonstrate how inconsistent parameter estimates may result from an algorithm that is considered fast and precise. Ignoring such inconsistencies may result in non-optimal parameter estimates, wrongly calibrated models, and erroneous model-based decisions. For fish stock assessment models, this may lead to erroneous inference about population size, and wrong estimates of parameters that are central to management. As consequence, management decisions may result in non-sustainable fisheries or misguided recovery plans.

Though these issues are central to modelling in fisheries science, discussion on them has been limited to scientists with a strongly quantitative background. This paper adopts language that allows for a more inclusive discussion.​

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​​Illustration of the disproportionate consequence of an insignificant variation in the starting point. One of the caveats associated with the algorithms. 

​​Paper title: Parameter estimation in stock assessment modelling: caveats with gradient-based algorithms 
Author: Sam Subbey​
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Editor's Choice – Caution when using efficient algorithms

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