Information-Theoretic Bounded Rationality for Learning and Decision-Making

Braun, D

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Information-Theoretic Bounded Rationality for Learning and Decision-Making

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Braun, D
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Research Group Sensorimotor Learning and Decision-Making, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Research Group Sensorimotor Learning and Decision-making, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Braun, D. (2016). Information-Theoretic Bounded Rationality for Learning and Decision-Making. Talk presented at NIPS 2016 Workshop on Imperfect Decision Makers: Admitting Real-World Rationality. Barcelona, Spain.

Cite as: https://hdl.handle.net/21.11116/0000-0000-7BEB-E

Abstract

We study an information-theoretic framework of bounded rational decision-making that trades off utility maximization against information-processing costs. We apply the basic principle of this framework to perception-action systems and show how the formation of abstractions and decision-making hierarchies depends on information-processing costs.