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• W2015893577 abstract "Motivated in part by the “rational inattention” framework of information-constrained decision-making by economic agents, we have recently introduced a general model for average-cost optimal control of Markov processes subject to mutual information constraints [1]. The optimal information-constrained control problem reduces to an infinite-dimensional convex program and admits a decomposition based on the Bellman error, which is the object of study in approximate dynamic programming. In this paper, we apply our general theory to an information-constrained variant of the scalar linear-quadratic-Gaussian (LQG) control problem. We give an upper bound on the optimal steady-state value of the quadratic performance objective and present explicit constructions of controllers that achieve this bound. We show that the obvious certainty-equivalent control policy is suboptimal when the information constraints are very severe, and exhibit another policy that performs better in this low-information regime. In the two extreme cases of no information (open-loop) and perfect information, these two policies coincide with the optimum." @default.
• W2015893577 created "2016-06-24" @default.
• W2015893577 creator A5031497586 @default.
• W2015893577 creator A5044844161 @default.
• W2015893577 date "2013-12-01" @default.
• W2015893577 modified "2023-09-26" @default.
• W2015893577 title "Rational inattention in scalar LQG control" @default.
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• W2015893577 doi "https://doi.org/10.1109/cdc.2013.6760793" @default.
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