Optimal Quota for a Multi-species Fishing Models

TL;DR

This paper compares dynamic programming and supervised learning methods for optimizing fishing quotas in multi-species stochastic models, aiming to improve control strategies for fisheries management.

Contribution

It introduces a novel approach combining gradient methods and deep neural networks to optimize quotas in multi-species stochastic fisheries models.

Findings

Gradient method effectively optimizes quotas based on probability density.

Deep neural networks can be trained to control biomass while preserving Markov properties.

Extension to distributed sites stabilizes biomass through quota adjustments.

Abstract

A Stochastic Control Problem can be solved by Dynamic Programming or Distributed Optimal Control with the Kolmogorov equation for the probability density of the Markov process of the problem. It can be solved also with Supervised Learning. We shall compare these two classes of methods for the control of fisheries. Fishing quotas are unpleasant but efficient to control the productivity of a fishing site. A popular model has a vector-valued stochastic differential equation for the biomass of the different species. Optimization of quota will be obtained by a gradient method applied to the least square difference with an ideal state weighted by the probability density of the biomasses. Alternatively a deep neural network which preserves the Markov property of the problem can be trained with a stochastic gradient algorithm. The model is extended to distributed fishing sites and biomass is stabilized by adjusting the quota to its time derivative.