A Hypergame Model of Conflict Cusp Helix for Noncooperative Boundedly Rational Multi-Polar Actors Using Deep Adversarial Reinforcement Learning and Misperception Information Strategies

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Abstract In the aftermath of the Cold War era, the world shifted from a tale of two competing superpowers, to a highly complex and multi-polar narrative. In a new world order, geopolitical relations, diplomacy, power plays and conflicts among countries became so perplex that any attempt to emulate realistic scenarios, conduct robust simulations, all the more so deduct foreseeable outcomes, would be nothing short of tenuous, if not practically infeasible. Besides, unlike physical systems for which universal uniformities factually and fundamentally exist, social systems do not obey implicit and exact “natural-laws-of-physics”. And while a plethora of modeling approaches have been deployed hitherto to tackle with the intrinsic complexity of warfare dynamics, however the required interdisciplinary synthesis has not been explored in the literature. In a first attempt to attain this fusion of cutting-edge methodologies, I propose a novel hybrid approach to conflict analysis.