Algorithmic Multiverse Simulation (AMS)
Year: 2026
Type: preprint
Abstract: This paper introduces Algorithmic Multiverse Simulation (AMS), a novel algorithm for dynamically generating and refining a self-improving multiverse model. The core claim is to create a system capable of exploring evolutionary pathways across a vast multiverse, identifying emergent phenomena through reinforcement learning and iterative model refinement. AMS utilizes a reinforcement learning agent to adjust the parameters of a simulated multiverse, prompting the generation of diverse scenarios and ultimately revealing underlying patterns and potential realities. The development of this algorithm holds significant potential for accelerating scientific discovery and providing insights into the fundamental nature of reality.
Author: Zhang, Jincheng
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7207721437