Modeling interaction as a complex system

Abstract

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 behaviour 'in the wild'. To analyse 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 users' behaviour as a complex nonlinear dynamical system. Extending a method known as 'convergent cross mapping', we show how to make causal inferences that do not rely on experimental manipulations or model-based regressions and, by virtue of being non-parametric, can accommodate data emanating from complex nonlinear dynamical systems. By using this extended approach, 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 a wide variety of real-world settings.

Beyond linearity, stability, and equilibrium: The edm package for empirical dynamic modeling and convergent cross-mapping in Stata

“…To illustrate the use of the command, we also provide three template do-files as part of the supplementary materials for the article. These cover three common analysis types for time-series data, “multispatial” panel data (a shared dynamic system using the default multispatial approach), and “multiple EDM” panel data (distinct dynamic systems where each panel is analyzed separately; similar to van Berkel et al [Forthcoming]). These do-files automatically analyze data using the primary EDM tools of simplex projection, S-maps, and CCM but also produce various plots and automate additional postprocessing, including hypothesis tests of various types.…”

Multifractal Mice: Measuring Task Engagement and Readiness-to-hand via Hand Movement

“…This is a technique for separating out valid and spurious contributions to the singularity spectrum. Surrogate analysis can be applied to many non-linear analysis techniques, and has previously been applied in HCI as confirmatory evidence for order effects when inferring causality during interaction [9], and we describe its use in our own analysis below (3.3).…”

Emergent Interaction: Complexity, Dynamics, and Enaction inHCI

“…Altered goals and behaviours, in turn, affect the use of the interface, which in turn affects the user, and so forth. Examples of this can be seen in the relationship between user engagement and smartphone notifications, where notifications affect engagement, and engagement affect notifications [4], and between visual analytic-pipelines and decision-making processes [11]. Again, we see bidirectional and potentially non-linear, complex, interaction between elements [4].…”

“…Recent work in Human-Computer Interaction (HCI) has suggested that interaction is often well modelled as a complex system -a dynamic interaction among elements (humans and technologies) in which behaviours arise from interactions between these elements, and the co-evolution of their behaviours [4]. While the framing in terms of complex systems is relatively new, this focus on interactivity as a driver of human behaviour joins a well established tradition in HCI which has emphasised the "interactivity" in interaction.…”

“…Researchers in HCI have recently argued that interaction with technology forms just a complex dynamical system [4]. Here it is argued that a relationship like that between predator and prey occurs between human and technology.…”