Multiple Forecast Visualizations (MFVs): trade-offs in trust and performance in multiple COVID-19 forecast visualizations

@Article{Padilla2022MultipleForecastVisualizations,
  author   = {Padilla, Lace and Fygenson, Racquel and Castro, Spencer C. and Bertini, Enrico},
  journal  = {IEEE Transactions on Visualization and Computer Graphics},
  title    = {{Multiple Forecast Visualizations (MFVs)}: trade-offs in trust and performance in multiple {COVID-19} forecast visualizations},
  year     = {2022},
  note     = {VIS '22 Best Paper Award! Preprint at \url{https://psyarxiv.com/2sq7j}.},
  pages    = {12-22},
  volume   = {29},
  abstract = {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.},
  doi      = {10.1109/TVCG.2022.3209457},
  series   = {VIS/TVCG},
}