Facebook has at least experimented with using deep reinforcement learning to adjust its notifications according to https://arxiv.org/pdf/1811.00260.pdf . Depending on which exact features they used for the state space (i.e. if they are causally connected to preferences), the trained agent would at least theoretically have an incentive to change user’s preferences.
The fact that they use DQN rather than a bandit algorithm seems to suggest that what they are doing involves at least some short term planning, but the paper does not seem to analyze the experiments in much detail, so it is unclear whether they could have used a myopic bandit algorithm instead. Either way, seeing this made me update quite a bit towards being more concerned about the effect of recommender systems on preferences.
https://en.wikipedia.org/wiki/Technological_transitions might be relevant.
The Geels book cited in the article (Geels, F.W., 2005. Technological transitions and system innovations. Cheltenham: Edward Elgar Publishing.) has a bunch of interesting case studies I read a while ago and a (I think popular) framework for technological change, but I am not sure the framework is sufficiently precise to be very predictive (and thus empirically validatable).
I don’t have any particular sources on this, but the economic literature on the effects of regulation might be quite relevant. In particular, I do remember attending a lecture arguing that limited liability played an important role for innovation during the industrial revolution.