Differentiating information transfer and causal effect |
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Authors: | J T Lizier and M Prokopenko |
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Institution: | (1) Biomedical Engineering, SUNY Downstate Medical Center, 450 Clarkson Avenue, P.O. Box 31, Brooklyn, NY 11203-2098, USA;(2) Departments of Anatomy and Neurobiology, Virginia Commonwealth University, Richmond, VA 23284, USA;(3) Department of Physiology and Pharmacology, SUNY Downstate Medical Center, 450 Clarkson Avenue, P.O. Box 31, Brooklyn, NY 11203-2098, USA;(4) Center for Neural Science, New York University, 4 Washington Place, New York, NY 10003-6621, USA;(5) Department of Neurology, SUNY Downstate Medical Center, 450 Clarkson Avenue, P.O. Box 31, Brooklyn, NY 11203-2098, USA |
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Abstract: | The concepts of information transfer and causal effect have received much recent attention, yet often the two are not appropriately
distinguished and certain measures have been suggested to be suitable for both. We discuss two existing measures, transfer
entropy and information flow, which can be used separately to quantify information transfer and causal information flow respectively.
We apply these measures to cellular automata on a local scale in space and time, in order to explicitly contrast them and
emphasize the differences between information transfer and causality. We also describe the manner in which the measures are
complementary, including the conditions under which they in fact converge. We show that causal information flow is a primary
tool to describe the causal structure of a system, while information transfer can then be used to describe the emergent computation
on that causal structure. |
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