Fathoming the Mandela Effect: Deploying Reinforcement Learning to Untangle the Multiverse

Abstract

Multiverse is a hypothetical idea that other universes can exist beyond our own. Various scientific theories have suggested scenarios such as the existence of bubble universes that constantly expand or string theory that attempts to merge gravity with other forces. Thus, a multiverse is a complex theoretical phenomenon that can best be conceived through computer simulation. Albeit within the multiverse, the causality of the Mandela effect is entirely possible. To examine the behavior of the multiverse as a representative ensemble, each universe as a specific ensemble element needs to be generated. Our universe generation is based on unique universes for two binary attributes of a population of n=303. The maximum possible universes this could produce within the multiverse is in the exponent of 182. To computationally confine the simulation to the scope of this study, the sample count of the multiverse is nmultiverse=606. Parameters representing the existence of each multiverse are implemented through the μ and σ values of each universe’s attributes. By using a developed reinforcement learning algorithm, we generate a multiverse yielding various universes. The computer gains consciousness of the parameters that can represent the expanse of possibility to exist for multiple universes. Furthermore, for each universe, a heart attack prediction model is performed to understand the universe’s environment and behavior. We test the Mandela effect or déjà vu of each universe by comparing error test losses with the training size of order M. Our model can measure the behavior of environments in different regions referred to as specific ensemble elements. By explicitly exploiting the attributes of each universe, we can get a better idea of the possible outcomes for the creation of other specific ensemble elements, as seen in the multiverse space planes.