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Modeling interaction as a complex system


van Berkel, N and Dennis, S and Zyphur, M and Li, J and Heathcote, A and Kostakos, V, Modeling interaction as a complex system, Human - Computer Interaction pp. 1-27. ISSN 0737-0024 (2020) [Refereed Article]

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© 2020 Taylor & Francis Group, LLC

DOI: doi:10.1080/07370024.2020.1715221


Researchers in Human-Computer Interaction typically rely on experiments to assess the causal effects of experimental conditions on variables of interest. Although this classic approach can be very useful, it offers little help in tackling questions of causality in the kind of data that are increasingly common in HCI – capturing user behavior ‘in the wild.’ To analyze such data, model-based regressions such as cross-lagged panel models or vector autoregressions can be used, but these require parametric assumptions about the structural form of effects among the variables. To overcome some of the limitations associated with experiments and model-based regressions, we adopt and extend ‘empirical dynamic modelling’ methods from ecology that lend themselves to conceptualizing multiple users’ behavior as complex nonlinear dynamical systems. Extending a method known as ‘convergent cross mapping’ or CCM, we show how to make causal inferences that do not rely on experimental manipulations or modelbased regressions and, by virtue of being non-parametric, can accommodate data emanating from complex nonlinear dynamical systems. By using this approach for multiple users, which we call ‘multiple convergent cross mapping’ or MCCM, researchers can achieve a better understanding of the interactions between users and technology – by distinguishing causality from correlation – in real-world settings.

Item Details

Item Type:Refereed Article
Keywords:ubicomp, mobile, performance, interaction, causality
Research Division:Psychology
Research Group:Cognitive and computational psychology
Research Field:Memory and attention
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in psychology
UTAS Author:Heathcote, A (Professor Andrew Heathcote)
ID Code:137937
Year Published:2020
Web of Science® Times Cited:2
Deposited By:Psychology
Deposited On:2020-03-16
Last Modified:2020-04-02

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