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  Bounded Rational Decision-Making in Changing Environments

Grau-Moya, J., & Braun, D. (2013). Bounded Rational Decision-Making in Changing Environments. In NIPS 2013 Workshop Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games (pp. 1-9).

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-001A-1261-1 Version Permalink: https://hdl.handle.net/21.11116/0000-0001-4A0B-1
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Grau-Moya, J1, 2, Author           
Braun, DA1, 2, Author           
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1Research Group Sensorimotor Learning and Decision-Making, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497809              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: A perfectly rational decision-maker chooses the best action with the highest utility gain from a set of possible actions. The optimality principles that describe such decision processes do not take into account the computational costs of finding the optimal action. Bounded rational decision-making addresses this problem by specifically trading off information-processing costs and expected utility. Interestingly, a similar trade-off between energy and entropy arises when describing changes in thermodynamic systems. This similarity has been recently used to describe bounded rational agents. Crucially, this framework assumes that the environment does not change while the decision-maker is computing the optimal policy. When this requirement is not fulfilled, the decision-maker will suffer inefficiencies in utility, that arise because the current policy is optimal for an environment in the past. Here we borrow concepts from non-equilibrium thermodynamics to quantify these inefficiencies and illustrate with simulations its relationship with computational resources.

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 Dates: 2013-12
 Publication Status: Issued
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 Identifiers: BibTex Citekey: GrauMoyaB2013
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Title: NIPS 2013 Workshop Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games
Place of Event: Lake Tahoe, NV, USA
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Title: NIPS 2013 Workshop Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1 - 9 Identifier: -