A Semiotic Approach to Complex Systems

  • Harald Atmanspacher
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 209)


A key topic in the work of Burghard Rieger is the notion of meaning. To explore this notion, he and his collaborators developed a most sophisticated approach combining theoretical ideas and concepts of semiotics with empirical and numerical tools of computational linguistics (see [29] for a most recent comprehensive account). In the present contribution, relations of Rieger’s achievements to some issues of interest in the physics and philosophy of complex systems will be addressed.


Complexity Measure Reference Relation Shannon Information Syntactic Information Mental Domain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    H. Atlan. Intentional Self-organization in Nature and the Origin of Meaning. In C. Rossi and E. Tiezzi, editors, Ecological Physical Chemistry, pages 311-331. Elsevier, Amsterdam, 1991.Google Scholar
  2. [2]
    H. Atmanspacher. Complexity and Meaning as a Bridge Across the Cartesian Cut. Journal of Consciousness Studies, 1:168-181, 1994.Google Scholar
  3. [3]
    H. Atmanspacher. Cartesian cut, Heisenberg cut, and the concept of complexity. World Futures, 49:333-355, 1997.CrossRefGoogle Scholar
  4. [4]
    H. Atmanspacher and R. G. Jahn. Problems of Reproducibility in Complex Mind-matter Systems. Journal of Scientific Exploration, 17:243-270, 2003.Google Scholar
  5. [5]
    H. Atmanspacher, C. Räth, and G. Wiedenmann. Statistics and Metastatistics in the Concept of Complexity. Physica A, 234:819-829, 1997.CrossRefGoogle Scholar
  6. [6]
    A. Baillet. La Vie de M. Descartes. Daniel Horthemels, Paris, 1691.Google Scholar
  7. [7]
    J. Balatoni and A. Rényi. Remarks on Entropy. Publ. Math. Inst. Hung. Acad. Sci., 9:9-40, 1956.Google Scholar
  8. [8]
    Y. Bar Hillel and R. Carnap. Semantic Information. British Journal of the Philosophy of Science, 4:147-157, 1953.CrossRefMathSciNetGoogle Scholar
  9. [9]
    J. Barwise and J. Perry. Situations and Attitudes. MIT Press, Cambridge, 1983.Google Scholar
  10. [10]
    J. E. Bates and H. Shepard. Measuring Complexity Using Information Fluctuations. Physics Letters A, 172:416-425, 1993.CrossRefGoogle Scholar
  11. [11]
    J. Casti. The Simply Complex: Trendy Buzzword or Emerging New Science? Bulletin of the Santa Fe Institute, 7:10-13, 1992.Google Scholar
  12. [12]
    J. P. Crutchfield. Knowledge and Meaning . Chaos and Complexity. In L. Lam and V. Naroditsky, editors, Modeling Complex Phenomena, pages 66-101. Springer, Berlin, 1992.Google Scholar
  13. [13]
    J. P. Crutchfield and K. Young. Inferring Statistical Complexity. Physical Review Letters, 63:105-108, 1989.CrossRefMathSciNetGoogle Scholar
  14. [14]
    R. Descartes. Meditationes De Prima Philosophia. In C. Adam and P. Tannery, editors, Œuvres de Descartes. Cerf, Paris, 1897/1913.Google Scholar
  15. [15]
    D. P. Feldman and J. P. Crutchfield. Measures of Statistical Complexity: Why? Physics Letters A, 238:244-252, 1998.zbMATHCrossRefMathSciNetGoogle Scholar
  16. [16]
    D. Gernert. Measurement of Pragmatic Information. Cognitive Systems, 1:169-176, 1985.Google Scholar
  17. [17]
    P. Grassberger. Toward a Quantitative Theory of Self-generated Complexity. International Journal of Theoretical Physics, 25:907-938, 1986.zbMATHCrossRefMathSciNetGoogle Scholar
  18. [18]
    P. Grassberger. Problems in Quantifying Self-generated Complexity. Helv. Phys. Acta, 62:489-511, 1989.MathSciNetGoogle Scholar
  19. [19]
    H. Haken. Information and Self-Organization, chapter Sect. 1.6. Springer, Berlin, second enlarged edition, 2000.Google Scholar
  20. [20]
    T. C. Halsey, M. H. Jensen, L. P. Kadano., I. Procaccia, and B. I. Shraiman. Fractal Measures and their Singularities: The Characterization of Strange Sets. Physical Review A, 33:1141-1151, 1986.CrossRefMathSciNetzbMATHGoogle Scholar
  21. [21]
    A. N. Kolmogorov. A New Metric Invariant of Transitive Dynamical Systems and Automorphisms in Lebesgue Spaces. Doklady Akademii Nauk SSSR, 119:861-864, 1958. See also Ya. G. Sinai. On the notion of entropy of a dynamical system Doklady Akademii Nauk SSSR, 124:768, 1959.zbMATHMathSciNetGoogle Scholar
  22. [22]
    A. N. Kolmogorov. Three Approaches to the Quantitative Definition of Complexity. Problems in Information Transmission, 1:3-11, 1965.MathSciNetzbMATHGoogle Scholar
  23. [23]
    K. Kornwachs and W. von Lucadou. Pragmatic Information as a Nonclassical Concept to Describe Cognitive Processes. Cognitive Systems, 1:79-94, 1985.Google Scholar
  24. [24]
    P. T. Landsberg and J. S. Shiner. Disorder and Complexity in an Ideal Non-equilibrium Fermi Gas. Physics Letters A, 245:228-232, 1998.CrossRefGoogle Scholar
  25. [25]
    C. W. Morris. Foundations of the Theory of Signs. In O. Neurath, R. Carnap, and C. W. Morris, editors, International Encyclopedia of Unified Science, volume I/2, pages 77-137. University of Chicago Press, Chicago, 1955.Google Scholar
  26. [26]
    C. S. Peirce. The Collected Papers (1931-1935), volume 1-6 edited by C. Hartshorne and P. Weiss; volume 7-8 edited by A. W. Burks. Harvard University Press, Cambridge, 1958.Google Scholar
  27. [27]
    H. Primas. The Cartesian Cut, the Heisenberg Cut, and Disentangled Observers. In K. V. Laurikainen and C. Montonen, editors, Symposia on the Foundations of Modern Physics. Wolfgang Pauli as a Philosopher, pages 245-269. World Scientific, Singapore, 1993.Google Scholar
  28. [28]
    H. Primas. Emergence in Exact Natural Sciences. Acta Polytechnica Scandinavica, Ma 91:83-98, 1998.MathSciNetGoogle Scholar
  29. [29]
    B. B. Rieger. Semiotic Cognitive Information Processing: Learning to Understand Discourse. A Systemic Model of Meaning Constitution. In R. Kühn, R. Menzel, W. Menzel, U. Ratsch, M. M. Richter, and I. O. Stamatescu, editors, Perspectives on Adaptivity and Learning, pages 347-403. Springer, Berlin, 2002.Google Scholar
  30. [30]
    B. B. Rieger and C. Thiopoulos. Situations, Topoi and Dispositions. In J. Retti and K. Leidlmair, editors, KI-Informatik-Fachberichte, volume 208, pages 365-375. Springer, Berlin, 1989.Google Scholar
  31. [31]
    E. Scheibe. The Logical Analysis of Quantum Mechanics. Pergamon, Oxford, 1973.Google Scholar
  32. [32]
    C. E. Shannon and W. Weaver. The Mathematical Theory of Communication. University of Illinois Press, Urbana, 1949.zbMATHGoogle Scholar
  33. [33]
    J. K. Sheri. Charles Peirce's Guess at the Riddle. Indiana University Press, Bloomington, 1994.Google Scholar
  34. [34]
    G. Spencer Brown. Laws of Form, chapter 1. George Allen and Unwin, London, 1969.Google Scholar
  35. [35]
    M. L. von Franz. Der Traum des Descartes. In M. L. von Franz, editor, Träume, pages 137-224. Daimon, Zürich, 1985.Google Scholar
  36. [36]
    E. von Weizsäcker. Erstmaligkeit und Bestätigung als Komponenten der pragmatischen Information. In E. von Weizsäcker, editor, Ofiene Systeme, volume I, pages 83-113. Klett-Cotta, Stuttgart, 1974.Google Scholar
  37. [37]
    R. Wackerbauer, A. Witt, H. Atmanspacher, J. Kurths, and H. Scheingraber. A Comparative Classification of Complexity Measures. Chaos, Solitons, & Fractals, 4:133-173, 1994.zbMATHCrossRefMathSciNetGoogle Scholar
  38. [38]
    W. Weaver. Science and Complexity. American Scientist, 36:536-544, 1968.Google Scholar

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© Springer 2007

Authors and Affiliations

  • Harald Atmanspacher
    • 1
  1. 1.Institut für Grenzgebiete der Psychologie und PsychohygienePsychohygiene

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