Monotonicity constraints in machine learning

In practical machine learning and data science tasks, an ML model is often used to quantify a global, semantically meaningful relationship between two or more values. For example, a hotel chain might want to use ML to optimize their pricing strategy and use a model to estimate the likelihood of a room being booked at a given price and day of the week. For a relationship like this the assumption is that, all other things being equal, a cheaper price is preferred by a user, so demand is higher at a lower price. However what might easily happen is that upon building the model, the data scientist discovers that the model is behaving unexpectedly: for example the model predicts that on Tuesdays, the clients would rather pay $110 than $100 for a room! The reason is that while there is an expected monotonic relationship between price and the likelihood of booking, the model is unable to (fully) capture it, due to noisiness of the data and confounds in it.