Learning sets and topologies

• Published in: Kybernetes Vol. 35; no. 10; pp. 1567 - 1578
• Main Authors: Alam Choudhury, Masudul, Zaman, S.I.
• Format: Journal Article
• Language: English
• Published: London Emerald Group Publishing Limited 01.12.2006
• Subjects: Causality; Cybernetics; Learning; Set theory; Social interaction; Social sciences; Topology; Learning; Set theory; Topology; Cybernetics
• ISSN: 0368-492X; 1758-7883
• DOI: 10.1108/03684920610688586

Purpose - This is an exploratory analytical paper. It aims to show how systemic learning is explained formally by evolutionary sets and topologies of ordinal values of knowledge-flows, the knowledge-induced socio-scientific variables and the relational mappings in terms of knowledge-flows and their induced socio-scientific variables.Design methodology approach - Mathematical theory of sets and topology is used to study the evolutionary impact of learning on social problems, whereby the impact can be transmitted into sets and topology for measurement.Findings - The properties are of interaction, integration and creative evolution of the knowledge-flows and their knowledge-induced socio-scientific variables and relations that are realized by circular causation interrelations. Such systemic learning emanating by circular causation relations is defined by measurable mappings over sets and topologies.Research limitations implications - The cybernetic nature of the paper points toward potential machine interface with cognitive measurement of learning values that are causally linked with social interaction.Originality value - This paper contributes a revolutionary way of measuring consciousness and learning parameters in the framework of evolutionary understanding of unity of knowledge in social systems. Evolutionary equilibrium implications of such learning are formalized by means of the method of sets and topology.