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Complexity versus Complex Systems: A New Approach to Scientific Discovery

  • F. Tito Arecchi

Abstract

Extraction of quantitative features from observations via measuring devices M means that the words of science are coded as numbers, and the syntaxis is a set of mathematical rules, thus all consequences should be worked in a purely deductive way. This characteristic of science displays two orders of drawbacks, namely, undecidability of deductive procedures, and intractability of complex situations. The way out of such a crisis consists in a frequent readjustment of M suggested by the observed events. This adaptive strategy differs from the adaptivity of a learning machine, which — inputted by a data stream — readjusts itself over a class of theoretical explanations in order to select the optimal choice. On the contrary, the scientist not only modifies the explanations for a fixed data set, but also explores different data sets by modifying M, that is, by selecting a different point of view. This M-adjustment is a pre-linguistic operation, not expressible by a formal language. Hence, the scientific endeavor can not be reduced to a machine task.

Keywords

Data Stream Adaptive Strategy Semantic Space Symbol Sequence Adaptive Procedure 
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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Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • F. Tito Arecchi
    • 1
  1. 1.Department of PhysicsUniversity of Firenze and Istituto Nazionale di OtticaFirenzeUK

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