Multiple Realizability

Multiple realizability explores the possibility that the same functional outcome (e.g., a cognitive process or a specific behavior) can arise from diverse underlying physical implementations. Current research focuses on understanding the implications of this principle across various fields, including machine learning (deep learning models), reinforcement learning (algorithms under different realizability assumptions and data coverage), and causal inference (identifiability and achievability of causal models). This research is significant because it challenges traditional assumptions about the relationship between implementation and function, impacting the development of more robust and generalizable AI systems and providing a deeper understanding of complex systems in general.