Robust Policy Selection and Harvest Risk Quantification for Natural Resources Management under Model Uncertainty

TL;DR

This paper develops a robust framework for selecting optimal harvesting policies in natural resource management, accounting for model uncertainty using convex risk measures to quantify and mitigate operational and marginal risks.

Contribution

It introduces a novel approach combining neoclassical growth models with convex risk measures, especially Fréchet risk measures, to enhance robustness in resource harvesting strategies under uncertainty.

Findings

Robust harvesting policies effectively mitigate model uncertainty.

Quantification of operational and marginal risks using convex risk measures.

Enhanced decision-making framework for natural resource management.

Abstract

In this work the problem of optimal harvesting policy selection for natural resources management under model uncertainty is investigated. Under the framework of the neoclassical growth model dynamics, the associated optimal control problem is investigated by introducing the concept of model uncertainty on the initial conditions of the operational procedure. At this stage, the notion of convex risk measures, and in particular the class of Fr\'echet risk measures, is employed in order to quantify the total operational and marginal risk, whereas simultaneously obtaining robust to model uncertainty harvesting strategies.