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

TL;DR

This paper models adversarial decision-making in complex networks using game theory, combining physics-based models to analyze resource competition and synchronization, revealing how initial resource distribution and agility influence victory.

Contribution

It introduces a novel integration of Kuramoto and Lotka-Volterra models within a game-theoretic framework to analyze strategic interactions in complex socio-physical systems.

Findings

Peripheral resource concentration can sustain competition and victory.

Structural advantages significantly impact outcomes.

Agility in decision-making is crucial when structural advantages are limited.

Abstract

We apply Game Theory to a mathematical representation of two competing teams of agents connected within a complex network, where the ability of each side to manoeuvre their resource and degrade that of the other depends on their ability to internally synchronise decision-making while out-pacing the other. Such a representation of an adversarial socio-physical system has application in a range of business, sporting, and military contexts. Specifically, we unite here two physics-based models, that of Kuramoto to represent decision-making cycles, and an adaptation of a multi-species Lotka-Volterra system for the resource competition. For complex networks we employ variations of the Barab\'asi-Alberts scale-free graph, varying how resources are initially distributed between graph hub and periphery. We adapt as equilibrium solution Nash Dominant Game Pruning as a means of efficiently exploring the dynamical decision tree. Across various scenarios we find Nash solutions where the side initially concentrating resources in the periphery can sustain competition to achieve victory except when asymmetries exist between the two. When structural advantage is limited we find that agility in how the victor stays ahead of decision-state of the other becomes critical.