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Binary Signaling under Subjective Priors and Costs as a Game
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
Bilkent Univ, Dept Elect & Elect Engn, TR-06800 Ankara, Turkey..
Queens Univ, Dept Math & Stat, Kingston, ON K7L 3N6, Canada..
2018 (English)In: 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2018, p. 1130-1135Conference paper, Published paper (Refereed)
Abstract [en]

Many decentralized and networked control problems involve decision makers which have either misaligned criteria or subjective priors. In the context of such a setup, in this paper we consider binary signaling problems in which the decision makers (the transmitter and the receiver) have subjective priors and/or misaligned objective functions. Depending on the commitment nature of the transmitter to his policies, we formulate the binary signaling problem as a Bayesian game under either Nash or Stackelberg equilibrium concepts and establish equilibrium solutions and their properties. In addition, the effects of subjective priors and costs on Nash and Stackelberg equilibria are analyzed. It is shown that there can be informative or non-informative equilibria in the binary signaling game under the Stackelberg assumption, but there always exists an equilibrium. However, apart from the informative and non-informative equilibria cases, under certain conditions, there does not exist a Nash equilibrium when the receiver is restricted to use deterministic policies. For the corresponding team setup, however, an equilibrium typically always exists and is always informative. Furthermore, we investigate the effects of small perturbations in priors and costs on equilibrium values around the team setup (with identical costs and priors), and show that the Stackelberg equilibrium behavior is not robust to small perturbations whereas the Nash equilibrium is.

Place, publisher, year, edition, pages
IEEE , 2018. p. 1130-1135
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-245016DOI: 10.1109/CDC.2018.8619612ISI: 000458114801014Scopus ID: 2-s2.0-85062193659ISBN: 978-1-5386-1395-5 (print)OAI: oai:DiVA.org:kth-245016DiVA, id: diva2:1293675
Conference
57th IEEE Conference on Decision and Control (CDC), DEC 17-19, 2018, Miami Beach, FL
Note

QC 20190305

Available from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-04-11Bibliographically approved

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Saritas, Serkan

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