Exploring a wider approach to OM conditioning in IOTC MSE work

For the current suite of IOTC MSE work, the general approach to conditioning the required set
of Operating Models (OMs) has been to use the species-specific stock assessment model structure
as the basis for the OMs. In [1] an alternate, complementary approach was outlined where
instead of the assessment being the basis for conditioning, a suite of possible prior states of historical
dynamics and current status are defined. The available, but mostly more contemporary,
data are included within an estimation scheme built on emerging Approximate Bayesian Computation
(ABC) and Synthetic Likelihood (SL) concepts [2, 3]. The aim is to generate a distribution
of current abundance, mortality and status that is consistent with the available data and the suite
of possible prior states of nature defined beforehand. This can then be used to initialise the OMs
used to project the stock into the future and test the candidate MPs.
A stock assessment, in this context, can be viewed as our attempt to do both of these things at
once. Ideally, this is arguably the most sensible option; however, it is not always successful. The
ongoing struggles with the Yellowfin tuna stock assessment, and the conditioning of OMs based
upon it, outline this problem: what if you cannot adequately reconcile the data, assessment
model structures, and the resultant estimates of current status and future projected dynamics?
In [1] we proposed an alternate approach arguing that using a stable, agreed and robust stock
assessment was the preferable first option, but that the ABC approach was a potentially viable -
and scientifically pragmatic - alternative approach if the assessment route was unsuccessful.
In this paper we expand on the ideas outlined in [1] using a relatively simple simulated example
of how the approach can work in the tuna MSE context. The mathematical and statistical tools
required are mostly in the Appendix, but some general principles are outlined. We explore a flexible
approach to fitting to the available data from both parametric and nonparametric viewpoints,
depending on the context. We also clarify what key status variables can be assigned priors, and
how that feeds into the estimation scheme with the observed data and catch to help produce the
abundance, biomass and mortality estimates required to initialise the OMs.