[basseq]

That "drop out" concept just seems entirely wrong. Not only because its erroneously constricting the applicant pool, but because it strongly biases the earlier roles in the queue.

For example, consider two roles and two applicants, with fit scores as below:

               Role 1    Role 2
  -----        -----     -----
  Applicant A    96%       95%
  Applicant B    95%       50%

Ignoring the "drop out" bug, under the algorithm described the system would evaluate all candidates for Role 1, determine Applicant A is the best, then move on. At that point, Applicant B is the best candidate for Role 2... even though they're not a very good one. Overall, not a great outcome (73% avg.).

You'd think the algorithm would want to maximize outcomes across all roles: the more optimal "best fit" solution would be Applicant B in Role 1 and Applicant A in Role 2 (95% avg).

(I'm assuming the reality here is that Role B isn't available at time of evaluation, so there's no way to evaluate the universe without waiting, which may be sub-optimal.)

[mistrial9]

at first glance the algorithm seems to reward compliance ("take whatever is offered") and severely penalize any teacher who insists on some placement (by refusing the first placement you are knocked out of the applicants, maybe for a long time)