the Tishby-Wiseman-Saad framework

The Tishby-Wiseman-Saad framework is a mathematical approach to studying decision-making and information flow in systems that learn from data. It analyzes how an agent, like a machine learning model, balances acquiring new information (which can improve decisions) against the cost of processing that information. By quantifying information transfer and uncertainty, the framework helps optimize how systems learn efficiently, focusing on the most relevant data while minimizing unnecessary complexity. This approach is valuable for designing smarter algorithms that adapt well while conserving resources.