The European Physical Journal B

, Volume 73, Issue 4, pp 605–615

Differentiating information transfer and causal effect

Authors

    • CSIRO Information and Communications Technology Centre
    • School of Information Technologies, The University of Sydney
  • M. Prokopenko
    • CSIRO Information and Communications Technology Centre
    • Max Planck Institute for Mathematics in the Sciences
Interdisciplinary Physics

DOI: 10.1140/epjb/e2010-00034-5

Cite this article as:
Lizier, J. & Prokopenko, M. Eur. Phys. J. B (2010) 73: 605. doi:10.1140/epjb/e2010-00034-5

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.

Copyright information

© EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2010