Dynamic Change Arcs to Explore Model Forecasts

dc.contributor.authorSt. Jean, Carmenen_US
dc.contributor.authorWare, Colinen_US
dc.contributor.authorGamble, Roberten_US
dc.contributor.editorKwan-Liu Ma and Giuseppe Santucci and Jarke van Wijken_US
dc.date.accessioned2016-06-09T09:32:54Z
dc.date.available2016-06-09T09:32:54Z
dc.date.issued2016en_US
dc.description.abstractIn many planning applications, a computational model is used to make predictions about the effects of management or engineering decisions. To understand the implications of alternative scenarios, a user typically adjusts one or more of the input parameters, runs the model, and examines the outcomes using simple charts. For example, a time series showing changes in productivity or revenue might be generated. While this approach can be effective in showing the projected effects of changes to the model's input parameters, it fails to show the mechanisms that cause those changes. In order to promote understanding of model mechanics using a simple graphical device, we propose dynamic change arcs. Dynamic change arcs graphically reveal the internal model structure as cause and effect linkages. They are signed to show both positive and negative effects. We implemented this concept using a species interaction model developed for fisheries management based on a system of Lotka-Volterra equations. The model has 10 economically important fish species and incorporates both predation and competition between species. The model predicts that changing the catch of one species can sometimes result in changes in biomass of another species through multi-step causal chains. The dynamic change arcs make it possible to interpret the resulting complex causal chains and interaction effects. We carried out an experiment to evaluate three alternative forms of arcs for portraying causal connections in the model. The results show that all linkage representations enabled participants to reason better about complex chains of causality than not showing linkages. However, none of them were significantly better than the others.en_US
dc.description.number3en_US
dc.description.sectionheadersTime Series Data and Sequencesen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume35en_US
dc.identifier.doi10.1111/cgf.12907en_US
dc.identifier.issn1467-8659en_US
dc.identifier.pages311-320en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12907en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectH.5.2 [Information Interfaces and Presentation]en_US
dc.subjectUser Interfacesen_US
dc.subjectInteractions Stylesen_US
dc.titleDynamic Change Arcs to Explore Model Forecastsen_US
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