Open Paradoxes: Retrocausality

This chapter delves into the intriguing concept of retrocausality, challenging conventional linear causality by proposing that future events could influence the past. Explored in the context of quantum entanglement, delayed-choice experiments, Wheeler–Feynman Absorber Theory, and the transactional interpretation of quantum mechanics, retrocausality remains speculative. In the realm of machine learning, the chapter explores the implications of retrocausality for causal analysis, introducing the idea that effects can influence their causes. The integration of physical retrocausality, evolutionary epistemology, and alternative scientific ideas into artificial intelligence offers a novel approach to understanding and dealing with causality. This multifaceted approach enriches AI’s understanding, fostering adaptability, creativity, and a holistic perspective.