Abhraneel Sarma

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 :)

Thank you @jessicahullman.bsky.social and @mjskay.com for being such wonderful mentors!!!

Looking back, one thing I really appreciated was all the times that you’ve challenged me to take on the more difficult problem, and that’s helped me improve my work immensely!

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.

More Forecasts, More (Decision) Problems: How Uncertainty Representations for Multiple Forecasts Impact Decision Making | Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems You will be notified whenever a record that you have chosen has been cited.

🚨Paper alert! 🚨
I'll be presenting my paper visualising multiple forecast distributions at #chi2025! (If you are in-person, the talk is on Wed 11:10am in G414+415)
If you are not interested in probability distributions, I will also be talking about 🦙 alpacas 🦙
dl.acm.org/doi/10.1145/...

This is a great point, and was one reason @abhsarma.bsky.social (with me + @jessicahullman.bsky.social) built a multiverse analysis tool that tries to help experts understand and evaluate the validity of subsets of a multiverse, rather than just shrugging: "enh, results vary" doi.org/10.1145/3613...

Can we make using SPSS (or other proprietary software) for analysis in papers be grounds for desk rejection?
(I am only slightly joking)

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