[projectorlochsa]

Probabilistic graphical models.

That's the foundation. The way you set up your model is by nodes and edges that specify the flow of influence (directed or undirected). Then it seems that there are general methods for inference and learning on any kind of graph one might pose.

For simple graphs (and simple is something one might want when modelling) the methods should be fairly effective.

Unfortunately, the biggest book on the subject that I know (Koller & Friedman) isn't accessible. Koller's course is also not that accessible.