Today my department had Sommer Gentry give a presentation on her work on liver transplants, an area I previously knew very little about. The basic idea is that because livers only last up to 8 hours after the donor dies, we can't simply allocate to whoever in the country needs it the most. Instead, historically there were 59 DSA (districts) and 11 regions (collections of districts). So within each DSA, an available liver is allocated to whoever needs it most.
This seems like a pretty good way to handle things until you know that liver availability does not necessarily match up with demand. Sommer's research has to do with coming up with collections of districts which will lead to a more equitable allocation across the US.
The big takeaway to me of her presentation was that transparency is key if you're actually hoping to have policy change because of your optimization results. She explained that some things which you would naturally want to have as an objective (distance organs travel for instance), end up as constraints when you want a model you can explain to anyone. This was particularly important since, as you can see in the video below, some entities are major losers in a more fair world. If the stakeholders can't find a problem with the process, just the outcome, then it will be a lot easier to achieve change!
I really enjoyed her presentation, and encourage you to watch the entire video (This seems to be a more mathematical version of the presentation I saw). The jump is set to her presenting a comparison of her solution vs. the current one in a way that visually shows the difference in fairness, which hits on transparency once again.
Feel free to post questions about the problem below!
This seems like a pretty good way to handle things until you know that liver availability does not necessarily match up with demand. Sommer's research has to do with coming up with collections of districts which will lead to a more equitable allocation across the US.
The big takeaway to me of her presentation was that transparency is key if you're actually hoping to have policy change because of your optimization results. She explained that some things which you would naturally want to have as an objective (distance organs travel for instance), end up as constraints when you want a model you can explain to anyone. This was particularly important since, as you can see in the video below, some entities are major losers in a more fair world. If the stakeholders can't find a problem with the process, just the outcome, then it will be a lot easier to achieve change!
I really enjoyed her presentation, and encourage you to watch the entire video (This seems to be a more mathematical version of the presentation I saw). The jump is set to her presenting a comparison of her solution vs. the current one in a way that visually shows the difference in fairness, which hits on transparency once again.
Feel free to post questions about the problem below!
