Game-Theoretic Analysis of Adversarial Decision Making in a Complex Socio-Physical System

Abstract The growing integration of technology within human processes has significantly increased the difficulty in optimising organisational decision-making, due to the highly coupled and non-linear nature of these systems. This is particularly true in the presence of dynamics for resource competition models between adversarial teams. While game theory provides a conceptual lens for studying such processes, it often struggles with the scale associated with real-world systems. This paper contributes to resolving this limitation through a parallelised variant of the efficient-but-exact nash dominant game pruning framework, which we employ to study the optimal behaviour under adversarial team dynamics parameterised by the so-called networked Boyd–Kuramoto–Lanchester resource competition model. In doing so, we demonstrate a structural bias in competitive systems towards concentrating organisational resources away from regions of competition to ensure resilience.