Visualization Decision Making Laboratory

Multiple Forecast Visualizations (MFVs): Trade-offs in Trust and Performance in Multiple COVID-19 Forecast Visualizations

The prevalence of inadequate SARS-COV-2 (COVID-19) responses may indicate a lack of trust in forecasts and risk communication. However, no work has empirically tested how multiple forecast visualization choices impact trust and task-based performance. The three studies presented in this paper (N = 1299) examine how visualization choices impact trust in COVID-19 mortality forecasts and how they influence performance in a trend prediction task. These studies focus on line charts populated with real-time COVID-19 data that varied the number and color encoding of the forecasts and the presence of best/worst-case forecasts. The studies reveal that trust in COVID-19 forecast visualizations initially increases with the number of forecasts and then plateaus after 6-9 forecasts. However, participants were most trusting of visualizations that showed less visual information, including a 95% confidence interval, single forecast, and grayscale encoded forecasts. Participants maintained high trust in intervals labeled with 50% and 25% and did not proportionally scale their trust to the indicated interval size. Despite the high trust, the 95% CI condition was the most likely to evoke predictions that did not correspond with the actual COVID-19 trend. Qualitative analysis of participants’ strategies confirmed that many participants trusted both the simplistic visualizations and those with numerous forecasts. This work provides practical guides for how COVID-19 forecast visualizations influence trust, including recommendations for identifying the range where forecasts balance trade-offs between trust and task-based performance.

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BibTeX

@article{padilla2022multiple, title={Multiple Forecast Visualizations (MFVs): Trade-offs in Trust and Performance in Multiple COVID-19 Forecast Visualizations}, author={Padilla, Lace and Fygenson, Racquel and Castro, Spencer C and Bertini, Enrico}, journal={IEEE Transactions on Visualization and Computer Graphics}, year={2022}, publisher={IEEE} }

APA

Padilla, L., Fygenson, R., Castro, S. C., & Bertini, E. (2022). Multiple Forecast Visualizations (MFVs): Trade-offs in Trust and Performance in Multiple COVID-19 Forecast Visualizations. IEEE Transactions on Visualization and Computer Graphics.