The idea then was the following: We do not need "randomness" and probability theory in our models, once we realize that deterministic systems, even simple ones, can produce arbitrarily complex outcomes.
For example, this was all the rage in economics in the 90's. Surely, those are dynamical systems and besides having an actual proper reason for probabilistic reasoning, one wanted to at least consider that modeling deterministic systems without any randomness could fit reality.
I also make this point because in this instance, graph based discrete models often have a continuous equivalent and it depends on the case which of those offers more useful outcomes.
If I remember correctly, the hype about chaotic systems died down in part because while we could formulate substantively powerful foundations, it was extremely difficult to "get something useful out of it" and it all seemed to go more or less nowhere.