Local Rule

Local rule research explores the design and analysis of systems that make decisions based on localized information, avoiding computationally expensive global computations. Current work focuses on developing and analyzing models that learn from sequential data using local rules, mitigating issues like catastrophic forgetting in neural networks and bias in machine learning applications for resource allocation. This research is significant for improving the efficiency and fairness of AI systems, particularly in applications like automated decision-making and resource allocation where global computations are impractical or undesirable.