Extreme physical information

From HandWiki

Extreme physical information (EPI) is a principle in information theory, first described and formulated in 1998[1] by B. Roy Frieden, Emeritus Professor of Optical Sciences at the University of Arizona. The principle states that the precipitation of scientific laws can be derived through Fisher information, taking the form of differential equations and probability distribution functions.

Introduction

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,[2][3] biology,[4] the biophysics of cancer growth,[5]chemistry,[5] and economics.[6] 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.

EPI principle

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.[clarification needed] Denoting the Fisher information in the data[clarification needed] as [math]\displaystyle{ \mathcal{I} }[/math], and that in the source as [math]\displaystyle{ \mathcal{J} }[/math], the EPI principle states that

[math]\displaystyle{ \mathcal{I} - \mathcal{J} = \mathrm {Extremum} }[/math]

Since the data are generally imperfect versions of the source, the extremum for most situations is a minimum.[why?] Thus there is a comforting tendency for any observation to describe its source faithfully.[why?] The EPI principle may be solved for the unknown system amplitudes via the usual Euler-Lagrange equations of variational calculus.

Books

  • 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

Recent papers using EPI

Ecological Modeling 174, 25-35, 2004 - CW 2003
doi:10.1016/j.ecolmodel.2003.12.045
Subj: monitoring of the environment for species diversity
  • Yolles. M.I. - "Knowledge Cybernetics: A New Metaphor for Social Collectives", 2005
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
http://spiedl.aip.org/getpdf/servlet/GetPDFServlet?filetype=pdf&id=PSISDG005421000001000048000001&idtype=cvips&prog=normal[yes|permanent dead link|dead link}}]
  • Ménard, Michel; Eboueya, Michel (2002). "Extreme physical information and objective function in fuzzy clustering". Fuzzy Sets and Systems 128 (3): 285–303. doi:10.1016/s0165-0114(01)00071-9. 
  • Ménard, Michel. & Dardignac, Pierre-André. & Chibelushi, Claude C. - "Non-extensive thermostatistics and extreme physical information for fuzzy clustering (invited paper)", IJCC, 2 (4): 1-63, 2004 http://www.yangsky.us/ijcc/pdf/ijcc241.pdf

See also

Notes

  1. ↑ B. Roy Frieden, Physics from Fisher Information: A Unification , 1st Ed. Cambridge University Press, ISBN:0-521-63167-X, pp328, 1998
  2. ↑ Frieden, B.R.; Hughes (1994). "Spectral 1/f noise derived from extremized physical information". Phys. Rev. E 49 (4): 2644–2649. doi:10.1103/physreve.49.2644. PMID 9961526. Bibcode: 1994PhRvE..49.2644F. 
  3. ↑ Frieden, B.R.; Soffer (1995). "Lagrangians of physics and the game of Fisher-information transfer". Phys. Rev. E 52 (3): 2274–2286. doi:10.1103/physreve.52.2274. PMID 9963668. Bibcode: 1995PhRvE..52.2274F. 
  4. ↑ Frieden, B.R.; Plastino, A.; Soffer, B.H. (2001). "Population genetics from an information perspective". J. Theor. Biol. 208 (1): 49–64. doi:10.1006/jtbi.2000.2199. PMID 11162052. 
  5. ↑ 5.0 5.1 Frieden, B.R.; Gatenby, R.A. (2004). "Information dynamics in carcinogenesis and tumor growth". Mutat. Res. 568 (2): 259–273. doi:10.1016/j.mrfmmm.2004.04.018. PMID 15542113. 
  6. ↑ Hawkins, R.J.; Frieden, B.R.; D'Anna, J.L. (2005). "Ab initio yield curve dynamics". Phys. Lett. A 344 (5): 317–323. doi:10.1016/j.physleta.2005.06.079. Bibcode: 2005PhLA..344..317H. 

References

External links




Retrieved from "https://handwiki.org/wiki/index.php?title=Extreme_physical_information&oldid=91317"