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Characterizing innovative processes in design spaces through measuring the information entropy of empirical data from protocol studies

Published online by Cambridge University Press:  30 January 2017

Jeff W.T. Kan  and
John S. Gero
Jeff W.T. Kan*
Affiliation:
Independent Scholar, Hong Kong
John S. Gero
Affiliation:
Department of Computer Science and School of Architecture, University of North Carolina, Charlotte, North Carolina, USA
*
Reprint requests to: Jeff W.T. Kan, Independent Scholar, Flat F, 34/F, Block One, Grand View Garden, Diamond Hill, Kowloon, Hong Kong. E-mail: [email protected]
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Abstract

This paper reports on a study characterizing design processes and the potential of design spaces through measuring the information entropy of empirical data derived from protocol studies. The sequential segments in a protocol analysis can be related to each other by examining their semantic content producing a design session's linkograph, which defines the design space for a design session. From a linkograph, it is possible to compute the probabilities of the connectivity of each segment for its forelinks and its backlinks, together with the probabilities of distance among links. A linkograph's entropy is a measure of the information in the design session. It is claimed that the entropy of the linkograph measures the potential of the design space being generated as the design proceeds chronologically. We present an approach to the automated construction of linkographs by connecting segments using the lexical database WordNet and measure its entropy. A case study of two design sessions with different characteristics was conducted, one considered more productive and creative, the other more pragmatic. Those segments with high entropy and those associated with high rates of change of entropy are analyzed. The creative session has a higher linkograph entropy. This result indicates the potential of using entropy to characterize a design process.

Keywords

Design ProcessesEntropyInformation AnalysisProtocol Studies
Type
Regular Articles
Information
AI EDAM , Volume 32 , Issue 1 , February 2018 , pp. 32 - 43
Copyright
Copyright © Cambridge University Press 2017 

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