Optimizing the Effectiveness of Organ Allocation 123 of points received for zero HLA mismatch is 400. The Eurotransplant policy also has greater emphasis on providing young children with a kidney match, giving children younger than 6 years an additional 1095 waiting-time points We implemented the Eurotransplant policy in our model to see if thatpolie could also benefit the u.s. but we found little difference. Utilizing Kidney Exchanges e A promising approach for kidney paired exchange is to run the maximal Home ing algorithm over the graph defined by the set of possible exchanges rer, this approach takes away from the autonomy of patients, because it requires them to wait for enough possible pairs to show up before performing the matching, and sometimes it may require them to take a less than perfect We sought to improve this supposedly"optimal solution"by implementing t paired donation in our model. According to each patient's phenotypes, we calculate the expected blood types of the persons parents and siblings, and make that the persons contri bution to the"donor pool. "In other words, the person brings to the transplant network an expected number r of potential donors. We then make the patient perform list paired donation with the topmost person in the current queue who is compatible in blood type to the donor accompanying the new patient. Ac- cording to our research, kidneys from live donors are about 21% better than cadaver kidneys in terms of success rate. Thus, it is in the cadaver- list persons best interest to undergo this exchange We find that for any value of r from 0. 2 to 2, list paired donation drastically decreases the length of the waitlist, by factors as large as 3, and makes the queue ize stabilize( figure 3) Patient Choices What should a patient do when presented with the opportunity for a kid ney? The decision is not clear-cut; for instance, if the patient is offered a poorly matched kidney now, but a well-matched kidney is likely to arrive in areason- able time, the patient should perhaps wait. We examine the this tradeoff. We assume that a patient who has already received a kidney transplant may not receive another in the future while this is not always true, it suffices for the purposes of our model, since we posit a choice between accepting a "lesser"kidney today and a better kidney later. (When a patient receives a second kidney transplant after the first organs failure, there is no reason to expect a better organ, since the patient cannot immediately return to the top of e cadaver kidney queue, and live donors are likely to be more reluctant previous failure.Optimizing the Effedtiveness of Organ Allocation 123 of points received for zero HLA mismatch is 400. The Eurotransplant policy also has greater emphasis on providing young dcildren with a kidney match, giving children younger than 6 years an additional 1095 waiting-time points. We implemented the Eurotransplant policy in our model to see if that policy could also benefit the U.S., but we found little difference. Utilizing Kidney Exchanges A promising approach for kidney paired exchange is to run the maximal matching algorithm over the graph defined by the set of possible exchanges. However, this approach takes away from the autonomy of patients, because it requires them to wait for enough possible pairs to show up before performing the matching, and sometimes it may require them to take a less than perfect matching. We sought to improve this supposedly "optimal solution" by implementing list paired donation in our model. According to each patient's phenotypes, we calculate the expected blood types of the person's parents and siblings, and make that the person's contri￾bution to the "donor pool." In other words, the person brings to the transplant network an expected number r of potential donors. We then make the patient perform list paired donation with the topmost person in the current queue who is compatible in blood type to the donor accompanying the new patient. Ac￾cording to our research, kidneys from live donors are about 21% better than cadaver kidneys in terms of success rate. Thus, it is in the cadaver-list person's best interest to undergo this exchange. We find that for any value of r from 0.2 to 2, list paired donation drastically decreases the length of the waitlist, by factors as large as 3, and makes the queue size stabilize (Figure 3). Patient Choices What should a patient do when presented with the oppor'tunity for a kid￾ney? Tfie decision is not clear-cut; for instance, if the patient is offered a poorly matched kidney now, but a well-matched kidney is likely to arrive in a reason￾able time, the patient should perhaps wait. We examine the this tradeoff. We assume that a patient who has already received a kidney transplant may not receive another in the future. While this is not always true, it suffices for the purposes of our model, since we posit a choice between accepting a "lesser" kidney today and. a better kidney later. (When a patient receives a second kidney transplant after the first organ's failure, there is no reason to expect a better organ, since the patient cannot immediately return to the top of the cadaver kidney queue, and live donors are likely to be more reluctant after a previous failure.)