Models from complexity science

Karen Cham

Models from complexity science

Research output: Contribution to conference › Paper › peer-review

Abstract

In popular dialogues, describing a system as ‟complex” is often the point of resignation, inferring that the system cannot be sufficiently described, predicted nor managed. Scientific dialogues however, have explored the collective behaviours of complexity to formally define a complex system. This presentation will introduce generic principles from complex systems science, and unpack how and why they can be applied to user experience design.

Original language	English
Publication status	Published - 3 Nov 2017
Event	UX Brighton 2017 - Brighton, U.K. Duration: 3 Nov 2017 → 3 Nov 2017

Conference

Conference	UX Brighton 2017
Period	3/11/17 → 3/11/17

Keywords

user experience
design
complexity science
Computer science and informatics

Cite this

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title = "Models from complexity science",

abstract = "In popular dialogues, describing a system as ‟complex” is often the point of resignation, inferring that the system cannot be sufficiently described, predicted nor managed. Scientific dialogues however, have explored the collective behaviours of complexity to formally define a complex system. This presentation will introduce generic principles from complex systems science, and unpack how and why they can be applied to user experience design.",

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AB - In popular dialogues, describing a system as ‟complex” is often the point of resignation, inferring that the system cannot be sufficiently described, predicted nor managed. Scientific dialogues however, have explored the collective behaviours of complexity to formally define a complex system. This presentation will introduce generic principles from complex systems science, and unpack how and why they can be applied to user experience design.

KW - user experience

KW - design

KW - complexity science

KW - Computer science and informatics

M3 - Paper

T2 - UX Brighton 2017

Y2 - 3 November 2017 through 3 November 2017

ER -

Models from complexity science

Abstract

Conference

Keywords

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Cite this