Also thank you to Darren and Fanny for being on my committee! You’ve always given great feedback and been very supportive of my work :)

Takeaway #2: designers should visualise multiple distributions as p-boxes if they want viewers to adopt a risk-averse decision-making strategy regardless of how the forecasts are distributed.

Takeaway #1: designers should (probably) visualise multiple distributions using ensembles if they want viewers to base their decisions on the more frequent predictions (aka a probabilistic interpretation of the 2nd order of uncertainty)

If the forecasts are clustered and represented as ensembles, then the decision-making strategy that participants adopt is largely based on the cluster of forecasts.

We found that p-boxes lead to relatively more risk-averse decision-making where the lower bound of the set of forecasts is weighted more heavily. On the other hand, decisions made with ensembles are only going to be similarly risk-averse if the forecasts are not clustered.

We explore how the visual representations used can impact the decision-making strategies that a viewer adopts. We vary how the 2nd order uncertainty is visualised using either ensembles or p-boxes (which only shows the bounds of the distributions.

Decision-makers have access to multiple forecasts, often from different models which make different assumptions. Simply taking the average of all of these predictions might not always lead to the best decisions. We consider a range of possible strategies that may be used for decision-making.

Once, we did write the results section in RMarkdown (uploaded it to Github for collabs). When things looked good, compiled it to latex and then uploaded to overleaf