The emergence of autonomous, self-optimizing algorithmic systems in finance, governance, and defence has introduced a novel form of systemic risk: Algorithmic Wars. Unlike traditional conflicts, these arise from misaligned, competing algorithms executing at superhuman speeds, amplifying fragility and destabilizing critical systems. From high-frequency trading-induced flash crashes to AI-driven disinformation campaigns and autonomous weaponry, early signals of such wars are already observable (Kroll et al., 2017; Mökander & Floridi, 2022). Yet global governance responses remain fragmented, aspirational, and reactive, anchored in static principles ill-suited for dynamic escalation risks.

This paper formally defines Algorithmic Wars, identifies their core drivers and pathways, and highlights the catastrophic potential of leaving such systems unchecked. It critically assesses the inadequacy of existing AI governance frameworks, which fail to prevent escalation, enforce accountability, or embed sovereign-calibrated deterrence. As a doctrinal response, the paper introduces the DFAS-PostObjective Ethical Response, an enforceable governance framework derived from Dynamic Financial Applied Science (DFAS) and its PostObjective philosophy (Alaali, 2025a; 2025b). The response operationalizes ethical foresight, authorship integrity, predictive auditability, and escalation governance through DFAS-FEP, DFAS-AAP, DFAS-DAIF, and DFAS-IFRS Manuscripts. Finally, the paper calls for global adoption of doctrinal deterrence mechanisms to pre-empt full-scale Algorithmic Wars.