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Learn how to make efficient decisions when cognitive resources are limited in this 45-minute lecture by Xaq Pitkow from Carnegie Mellon University, presented at the Fields Institute. Explore the fundamental trade-offs between computational cost and decision quality, examining how biological and artificial systems can optimize their behavior when thinking itself comes at a premium. Discover theoretical frameworks for understanding resource-bounded rationality and investigate strategies for acting effectively under cognitive constraints. Gain insights into the mathematical principles underlying efficient information processing and decision-making in both neural and computational systems, with applications to understanding how brains balance accuracy against metabolic costs and how AI systems can be designed to make smart choices with limited computational budgets.
