Elements of a cybernetic epistemology: elementary anticipatory systems

Author: Nechansky Helmut  

Publisher: Emerald Group Publishing Ltd

ISSN: 0368-492X

Source: Kybernetes: The International Journal of Systems & Cybernetics, Vol.42, Iss.2, 2013-02, pp. : 185-206

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Abstract

Purpose - The purpose of this paper is to analyze how elementary anticipation, understood as anticipation of the repetition of one known pattern, can emerge out of sequence learning and how it can contribute to the behavioral options of goal-oriented systems. Design/methodology/approach - A functional approach is used to develop the necessary cybernetic structures of a subsystem for sequence learning that can additionally provide standards of anticipated patterns for future pattern matching. Based on that it is analyzed, how a goal-oriented system can use the information about the actual occurrence of an anticipated pattern. Findings - A subsystem for elementary anticipation of single patterns builds on sequence learning and requires additionally a structure: first, to unequivocally identify the beginning of known sequences just from their first patterns; and second, to decide to use a latter pattern of such a sequence as standard for an anticipated pattern. Deciding to actually use such a pattern for anticipation requires an additional subsystem to switch between the feedback pattern recognition mode and feedforward. Then the occurrence of such an anticipated pattern allows immediate recognition and action. Practical implications - The paper shows a necessary evolution of cybernetic structures from pattern recognition via sequence learning to anticipation; and it shows, too, a necessary evolution in the cognitive development of individual systems. In the simple anticipatory structures analyzed here, only known patterns, that are part of a known sequence, can become anticipated patterns. Originality/value - The paper places elementary anticipation of single patterns in an evolutionary development based on pattern recognition and sequence learning. It provides the base to analyze more complex forms of anticipation.