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Information flow in learning a coin-tossing game

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/65395

Title: Information flow in learning a coin-tossing game
Authors: Sato, Yuzuru Browse this author →KAKEN DB
Ay, Nihat Browse this author
Keywords: game dynamics
information theory
dynamical systems
Issue Date: 1-Apr-2016
Publisher: 電子情報通信学会
Journal Title: IEEE Nonlinear Theory and its Applications
Volume: 7
Issue: 2
Start Page: 118
End Page: 125
Publisher DOI: 10.1587/nolta.7.118
Abstract: Information flow in adaptively interacting stochastic processes is studied. We give an extended form of game dynamics for interactingMarkovian processes and compute a measure of causal information flow, which is different from the transfer entropy. In the game theoretic situation, causal information flow can show oscillatory behavior through reward-maximizing adaptation of two players. The adaptive dynamics for the coin-tossing game is exemplified and the causal information flow therein is investigated.
Rights: Copyright © 2016 The Institute of Electronics, Information and Communication Engineers
Relation: http://search.ieice.org/
Type: article
URI: http://hdl.handle.net/2115/65395
Appears in Collections:電子科学研究所 (Research Institute for Electronic Science) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 佐藤 譲

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