Quantum Reasoning using Lie Algebra for Everyday Life (and AI perhaps...)
It addresses the problem of interconnected decision-making in AI, but the approach is speculative and incremental.
The paper explores applying quantum mechanics and Lie algebra to model decision-making where conclusions on one issue affect confidence in others, suggesting this could improve machine learning efficiency and probabilistic reasoning.
We investigate the applicability of the formalism of quantum mechanics to everyday life. It seems to be directly relevant for situations in which the very act of coming to a conclusion or decision on one issue affects one's confidence about conclusions or decisions on another issue. Lie algebra theory is argued to be a very useful tool in guiding the construction of quantum descriptions of such situations. Tests, extensions and speculative applications and implications, including for the encoding of thoughts in neural networks, are discussed. It is suggested that the recognition and incorporation of such mathematical structure into machine learning and artificial intelligence might lead to significant efficiency and generality gains in addition to ensuring probabilistic reasoning at a fundamental level.