  Ranking models are typically designed to provide rankings that optimize some
measure of immediate utility to the users. As a result, they have been unable
to anticipate an increasing number of undesirable long-term consequences of
their proposed rankings, from fueling the spread of misinformation and
increasing polarization to degrading social discourse. Can we design ranking
models that understand the consequences of their proposed rankings and, more
importantly, are able to avoid the undesirable ones? In this paper, we first
introduce a joint representation of rankings and user dynamics using Markov
decision processes. Then, we show that this representation greatly simplifies
the construction of consequential ranking models that trade off the immediate
utility and the long-term welfare. In particular, we can obtain optimal
consequential rankings just by applying weighted sampling on the rankings
provided by models that maximize measures of immediate utility. However, in
practice, such a strategy may be inefficient and impractical, specially in high
dimensional scenarios. To overcome this, we introduce an efficient
gradient-based algorithm to learn parameterized consequential ranking models
that effectively approximate optimal ones. We showcase our methodology using
synthetic and real data gathered from Reddit and show that ranking models
derived using our methodology provide ranks that may mitigate the spread of
misinformation and improve the civility of online discussions.
