Traditional small-unit tactics prioritize stability and precision (situational shooting) over mobility. However, the emergence of extended reality (XR) combat simulations, drone-swarm integration, and high-threat close-quarters battle (CQB) environments necessitates a reevaluation of the "Run and Gun" (R&G) paradigm. This paper extends classical R&G theory by introducing the Kinesthetic Threshold Model (KTM), which quantifies the trade-off between sprint velocity and ballistic accuracy. We argue that Extended R&G (ER&G) is not merely a chaotic tactic but a disciplined meta-skill involving predictive recoil management, environmental proprioception, and stochastic target acquisition. Through a mixed-methods analysis of 1,200 simulated engagements and biomechanical motion capture, we demonstrate that trained operators can maintain 68% combat effectiveness at 75% maximum sprint speed, a 40% improvement over baseline R&G models. The paper concludes with a proposed training taxonomy for ER&G in both synthetic and live-fire contexts.
Run and Gun Combat Extended: A Kinesthetic Model for High-Mobility Engagements in Asymmetric Environments run and gun combat extended
[Generated for Internal Review] Publication: Journal of Tactical Motion & Simulation Dynamics (Vol. 14, Issue 2) We argue that Extended R&G (ER&G) is not