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Modeling interaction as a complex system

journal contribution
posted on 2023-05-20, 11:49 authored by van Berkel, N, Dennis, S, Zyphur, M, Li, J, Heathcote, A, Kostakos, V
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.

History

Publication title

Human - Computer Interaction

Pagination

1-27

ISSN

0737-0024

Department/School

School of Psychological Sciences

Publisher

Lawrence Erlbaum Assoc Inc

Place of publication

10 Industrial Ave, Mahwah, USA, Nj, 07430-2262

Rights statement

© 2020 Taylor & Francis Group, LLC

Repository Status

  • Restricted

Socio-economic Objectives

Expanding knowledge in psychology

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