Human capriciousness is indeed hard to model, so one solution is not do it. In timetabling applications, the algorithm provides a level of impartiality so even

Human capriciousness is indeed hard to model, so one solution is not do it. In timetabling applications, the algorithm provides a level of impartiality so even if some constituents don't get what they want, you can always blame the algorithm -- "it tried, but it had to satisfy other constraints". This removes some (but not all) of the politics behind timetabling. Influential persons may still try to wield their political power to bend the algorithm toward favoring them (e.g. in algorithmic terms, this could mean boosting their weight from 0.5 to 0.8 to beat out competition) but even in this compromised state, optimal timetabling is still better than doing it manually -- the optimization algorithm will at least try to maximize remaining preferences within the remainder degrees-of-freedom. Timetabling is an inherently political process anywhere (if one has ever done timetable for high school teachers....), but optimization algorithms do bring a level of impartiality to the process. Also, if folks aren't explicit about their preferences, or change their minds, it's no fault of the algorithm.