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The HUMANE typology and method – supporting the analysis and design of human-machine networks

The HUMANE typology and method – supporting the analysis and design of human-machine networks
The HUMANE typology and method – supporting the analysis and design of human-machine networks
This white paper presents the HUMANE typology and method, intended to support the analysis and design for human-machine networks (HMN). The typology serves to characterise HMNs on dimensions pertaining to the actors of the network, the relations between the actors, network extent and network structure. The method supports profiling HMNs along these dimensions, to analyse implications of the network characteristics, identify similar networks, and enable the transfer of design knowledge and experience in the form of design patterns. The application of the typology and method is exemplified a summary presentations of one of the HUMANE case studies (eVACUATE).
white paper
Engen, Vegard
5ab4f73a-6cb5-4a58-9d89-ebced3182962
Folstad, Asbjorn
bb1a064c-e005-436b-878c-6bd6e41c172e
Engen, Vegard
5ab4f73a-6cb5-4a58-9d89-ebced3182962
Folstad, Asbjorn
bb1a064c-e005-436b-878c-6bd6e41c172e

Engen, Vegard and Folstad, Asbjorn (eds.) (2017) The HUMANE typology and method – supporting the analysis and design of human-machine networks

Record type: Monograph (Project Report)

Abstract

This white paper presents the HUMANE typology and method, intended to support the analysis and design for human-machine networks (HMN). The typology serves to characterise HMNs on dimensions pertaining to the actors of the network, the relations between the actors, network extent and network structure. The method supports profiling HMNs along these dimensions, to analyse implications of the network characteristics, identify similar networks, and enable the transfer of design knowledge and experience in the form of design patterns. The application of the typology and method is exemplified a summary presentations of one of the HUMANE case studies (eVACUATE).

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More information

Accepted/In Press date: 1 June 2017
Keywords: white paper

Identifiers

Local EPrints ID: 412958
URI: http://eprints.soton.ac.uk/id/eprint/412958
PURE UUID: 0b9de1c0-672b-4483-8676-353c41ec6632

Catalogue record

Date deposited: 10 Aug 2017 16:30
Last modified: 10 Aug 2017 16:30

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Contributors

Editor: Vegard Engen
Editor: Asbjorn Folstad

University divisions

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