Rationally inattentive Markov decision processes over a finite horizon

Ehsan Shafieepoorfard, Maxim Raginsky

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The framework of Rationally Inattentive Markov Decision Processes (RIMDPs) is an extension of Partially Observable Markov Decision Processes (POMDP) to the case when the observation kernel that governs the information gathering process is also selected by the decision maker. At each time, an observation kernel is chosen subject to a constraint on the Shannon conditional mutual information between the history of states and the current observation given the history of past observations. This set-up naturally arises in the context of networked control systems, artificial intelligence, and economic decision-making by boundedly rational agents. We show that, under certain structural assumptions on the information pattern and on the optimal policy, Bellman's Principle of Optimality can be used to derive a general dynamic programming recursion for this problem that reduces to solving a sequence of conditional rate-distortion problems.

Original languageEnglish (US)
Title of host publicationConference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
EditorsMichael B. Matthews
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages621-627
Number of pages7
ISBN (Electronic)9781538618233
DOIs
StatePublished - Jul 2 2017
Event51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 - Pacific Grove, United States
Duration: Oct 29 2017Nov 1 2017

Publication series

NameConference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Volume2017-October

Other

Other51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Country/TerritoryUnited States
CityPacific Grove
Period10/29/1711/1/17

ASJC Scopus subject areas

  • Control and Optimization
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing
  • Biomedical Engineering
  • Instrumentation

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