Convergences in cognitive science, social network analysis, pattern recognition and machine intelligence as dynamic processes in non-Euclidean space

Joseph Woelfel ()
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Joseph Woelfel: University at Buffalo, State University of New York

Quality & Quantity: International Journal of Methodology, 2020, vol. 54, issue 1, No 19, 263-278

Abstract: Abstract Students of human cognitive and cultural processes, social networks, pattern recognition and machine intelligence often find that the coordinate systems resulting from commonly used measurement and analysis tools yield non-Euclidean configurations. Typically, researchers consider this unfortunate, and seek methods to return the spaces to Euclidean configurations. This article details all the known methods of such transformations, but presents evidence from multiple fields of inquiry that shows the non-Euclidean nature of the space is meaningful, and that all transformations to Euclidean form produce serious distortions to measured values. The article further presents methods for describing processes in the non-Euclidean spaces along with empirical examples of such uses.

Keywords: Machine intelligence; Neural network; Multidimensional scaling; Social network analysis; Galileo theory; Multidimensional space; Non-Euclidean space; Artificial intelligence; Inertial reference frame (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11135-019-00852-2

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