Dynamic Change Arcs to Explore Model Forecasts

• Published in: Computer graphics forum Vol. 35; no. 3; pp. 311 - 320
• Main Authors: Jean, C. St, Ware, C., Gamble, R.
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.06.2016
• Subjects: Analysis; Categories and Subject Descriptors (according to ACM CCS); Computer based modeling; Computer graphics; Decision making; Decisions; Devices; Dynamical systems; Dynamics; Economics; Engineering; H.5.2 [Information Interfaces and Presentation]: User Interfaces-Interactions Styles; Linkages; Management; Mathematical models; Studies; Time series
• ISSN: 0167-7055; 1467-8659
• DOI: 10.1111/cgf.12907

In 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.