Learning sets and topologies

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.