Extreme physical information (EPI) is a principle, first described and formulated in 1998 by B. Roy Frieden, Emeritus Professor of Optical Sciences at the University of Arizona, that states, the precipitation of scientific laws can be derived through Fisher information, taking the form of differential equations and probability distribution functions.
Physicist John Archibald Wheeler stated that:
All things physical are information-theoretic in origin and this is a participatory universe... Observer participancy gives rise to information; and information gives rise to physics.
By using Fisher information, in particular its loss I - J incurred during observation, the EPI principle provides a new approach for deriving laws governing many aspects of nature and human society. EPI can be seen as an extension of information theory that encompasses much theoretical physics and chemistry. Examples include the Schrödinger wave equation and the Maxwell–Boltzmann distribution law. EPI has been used to derive a number of fundamental laws of physics, biology, the biophysics of cancer growth,chemistry, and economics. EPI can also be seen as a game against nature, first proposed by Charles Sanders Peirce. The approach does require prior knowledge of an appropriate invariance principle or data.
The EPI principle builds on the well known idea that the observation of a "source" phenomenon is never completely accurate. That is, information present in the source is inevitably lost when observing the source. The random errors in the observations are presumed to define the probability distribution function of the source phenomenon. That is, "the physics lies in the fluctuations." The information loss is postulated to be an extreme value. Denoting the Fisher information in the data as
I
, and that in the source as
J
, the EPI principle states that
I
−
J
=
E
x
t
r
e
m
u
m
Since the data are generally imperfect versions of the source, the extremum for most situations is a minimum. Thus there is a comforting tendency for any observation to describe its source faithfully. The EPI principle may be solved for the unknown system amplitudes via the usual Euler-Lagrange equations of variational calculus.
Frieden, B. Roy - Physics from Fisher Information: A Unification , 1st Ed. Cambridge University Press, ISBN 0-521-63167-X, pp328, 1998
Frieden, B. Roy - Science from Fisher Information: A Unification , 2nd Ed. Cambridge University Press, ISBN 0-521-00911-1, pp502, 2004
Frieden, B.R. & Gatenby, R.A. eds. - Exploratory Data Analysis Using Fisher Information, Springer-Verlag (in press), pp358, 2006
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Chimento, L.P.; Pennini, F.; Plastino, A. "Naudts-like duality and the Extreme Fisher information principle". Phys. Rev. E. 62: 7462–7465. doi:10.1103/physreve.62.7462.
Nagy, A (2003). "Fisher information in density functional theory". J. Chem. Phys. 119: 9401–9405. doi:10.1063/1.1615765.
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Mlynar, J. & Bertalot, L. - "Neutron spectra unfolding with minimum Fisher regularization" http://pos.sissa.it/archive/conferences/025/063/FNDA2006_063.pdf Subj: Diagnosis of plasma shape within the tokamak fusion machine using reconstructions based upon EPI.
Venkatesan, Ravi. - "Information encryption using a Fisher-Schrödinger Model", Presented at 6th International Conference on Complex Systems (ICCS) June, 2006 Boston, Massachusetts Full paper is in Frieden and Gatenby, 2006 http://necsi.edu/community/wiki/index.php/ICCS06/235 Subj: Encryption, secure transmission using EPI, in particular game aspect.
Fath B.D. & Cabezas, H. & CW Pawlowski - "Exergy and Fisher information as ecological indices",
Ecological Modeling 174, 25-35, 2004 - CW 2003
doi:10.1016/j.ecolmodel.2003.12.045
Subj: monitoring of the environment for species diversity
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http://isce.edu/ISCE_Group_Site/web-content/ISCE_Events/Christchurch_2005/Papers/Yolles.pdf
Subj: Information-based approaches to knowledge management.
Venkatesan, R.C. - "Invariant Extreme Physical Information and Fuzzy Clustering", Proc. SPIE Symposium on Defense & Security,
Intelligent Computing: Theory and Applications II, Priddy, K. L. ed, Volume 5421, pp. 48-57, Orlando, Florida, 2004
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Ménard, Michel; Eboueya, Michel. "Extreme physical information and objective function in fuzzy clustering" (PDF). Fuzzy Sets and Systems. 128 (3): 285–303. doi:10.1016/s0165-0114(01)00071-9.
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