A HYPERGAME MODEL OF CONFLICT CUSP HELIX FOR NONCOOPERATIVE BOUNDEDLY RATIONAL MULTI-POLAR ACTORS USING DEEP ADVERSARIAL REINFORCEMENT LEARNING AND MISPERCEPTION INFORMATION STRATEGIES
DOI:
https://doi.org/10.2478/eoik-2025-0010Keywords:
warfare, multi-polar world, deep reinforcement learning, catastrophe theory, bounded rationality, Helix conflict, hypergame theory, complex systems, chaosAbstract
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.
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