A Robust Decision Making Framework for Optimal Strategy Selection in Warfare under Model Uncertainty

TL;DR

This paper introduces a robust decision-making framework for selecting optimal combat strategies under model uncertainty, using a stochastic Lanchester model to improve decision robustness in warfare scenarios.

Contribution

It develops an extendable framework that robustly accounts for uncertainty in combat parameters for optimal force allocation decisions.

Findings

Framework effectively handles uncertainty in combat models.

Optimal strategies are identified under various uncertain conditions.

The approach enhances decision robustness in warfare simulations.

Abstract

In this paper is presented a framework for treating uncertainty in optimal decision problems occuring in combat situations, in order to robustly select the optimal strategy. A stochastic version of the popular Lanchester's aimed-fire model is considered as the underlying combat system describing the combet dynamics, and upon this an optimal decision rule for allocating forces is constructed. This approach results to a very extendable optimal decision framework, where the optimal strategy is chosen by simultaneously treating robustly uncertainty regarding critical combat parameters.