Hostname: page-component-5db58dd55d-8mwbx Total loading time: 0 Render date: 2026-05-26T01:47:18.297Z Has data issue: false hasContentIssue false
  • English
  • Français

Complexity in Science: Syntaxis Versus Semantics

Published online by Cambridge University Press:  07 November 2014

Fortunato Tito Arecchi
Get access Rights & Permissions [Opens in a new window]

Abstract

A definition of the objects of a science in terms of precise measuring operations M gives the objects a set-theoretical character whereby complexity, seen as a multiplicity of possible final outcomes, emerges. An adaptive strategy introduces a frequent readjustment of the M settings, which reduces that multiplicity. This way, an adaptive cognitive task can be seen as the extraction of a simple map out of a complex landscape.

Information

Type
Feature Article
Information
CNS Spectrums , Volume 3 , Issue 1: Chaos and Complexity in Neuroscience , January 1998 , pp. 30 - 40
Copyright
Copyright © Cambridge University Press 1998

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

1.Hopcroft, JE, Ullman, JD. Introduction to Automata Theory, Languages and Computation. Reading, MA: Addison-Wesley; 1979.Google Scholar
2.Kolmogorov, AN. Three approaches to the quantitative definition of information. Problems Inform Transmission. 1965;1:4–35.Google Scholar
3.Chaitin, GJ. On the length of programs for computing binary sequences. J Assoc Comp Math. 1966;13;547–582.Google Scholar
4.Grassberger, P, Schreiber, T, Schaffrath, C. Nonlinear time sequences analysis. Int J Bif Chaos. 1991;1:521–547.Google Scholar
5.Abarbanel, HDI, Brown, R, Sidorowich, JJ, Tsimring, LS. The analysis of observed chaotic data in physical systems. Rev Mod Phys. 1993;65:1331–1392.Google Scholar
6.Shaw, R. Strange attractors, chaotic behavior, and information flow. Z Naturforsch. 1981;36a:80–120.CrossRefGoogle Scholar
7.Pines, David, ed. Emerging syntheses in science. Santa Fe Inst Proc. 1987;1.Google Scholar
8.Zurek, WH, ed. Complexity, entropy and the physics of information. Santa Fe Inst Proc. 1990;8.Google Scholar
9.Casdagli, M, Eubank, S, eds. Nonlinear modeling and forecasting. Santa Fe Inst Proc. 1992;12.Google Scholar
10.Grassberger, P. Toward a quantitative theory of self-generated complexity. Int J Theor Phys. 1986;25:907–945.Google Scholar
11.Crutchfield, JP, Young, K. Inferring statistical complexity. Phys Rev Lett. 1989;63;105–108.CrossRefGoogle ScholarPubMed
12.D'Alessandro, G, Politi, A. Hierarchical approach to complexity with applications to dynamic systems. Phys Rev Lett. 1990;64;1609–1612.Google Scholar
13.Badii, R, Politi, A. Complexity: Hierarchical Structures and Scaling in Physics. Chapters 8 and 9. Cambridge, UK: Cambridge University Press; 1997.CrossRefGoogle Scholar
14.Simon, H. The architecture of complexity. Proc Amer Philos Soc. 1982;106:467–508.Google Scholar
15.Arecchi, FT, Basti, GF, Boccaletti, S, Perrone, A. Adaptive recognition of a chaotic dynamics. Europhys Lett. 1994;26:327–332.CrossRefGoogle Scholar
16.Boccaletti, S, Arecchi, FT. Adaptive control of chaos. Europhys Lett. 1995;31:127–132.Google Scholar
17.Crutchfield, JP. Semantics and thermodynamics. Santa Fe Inst Proc. 1992;12:317–359.Google Scholar
18.Tarski, A. The concept of truth in formalized languages. In: Logic, Semantics, Mathematics: Papers 1923–1938. Oxford, UK: Clarendon Press; 1956.Google Scholar
19.Krohn, W, Küppers, G, Nowotny, H. Selforganization Portrait of a Scientific Revolution. Dordrecht, The Netherlands: Kluwer Academic Publishers; 1990.Google Scholar
20.Arecchi, FT. A critical approach to complexity and self-organization. La Nuova Critica I–II. 1992;Quaderno 19–20:7–39.Google Scholar
21.Peirce, CS. Collected Papers. Vols. I–VI. Harshorne, C, Weiss, P, eds. pp. 1931–1935. Vols. VII and VIII. Burks AW, ed. Cambridge, MA: Harvard University Press; 1958.Google Scholar
22.Hanson, WR. Patterns of Discovery: An Inquiry into the Conceptual Foundations of Science. Cambridge, MA: Cambridge University Press; 1958.Google Scholar
23.Polanyii, M. Personal Knowledge: Towards a Post-Critical Philosophy. London, UK: Routledge & Kegan Paul; 1958.Google Scholar
24.Von Franz, ML. Psyche und Materie. Eranos: Einsiedeln (C.H.); 1988.Google Scholar
25.Zadeh, LA. Fuzzy Sets and Applications (selected papers). New York, NY: John Wiley & Sons; 1987.Google Scholar
26.Agazzi, E, ed. Temi e problemi di filosofia della fisica. Roma: Abete; 1974 (section 50).Google Scholar
27.Atmanspacher, H. Complexity and meaning as a bridge across the Cartesian cut. J Consciousness Studies. 1994;1:169–192.Google Scholar