Determinism does not imply predictability. Determinism is a property of a system’s boundary conditions; predictability is a relationship between a target system and a predicting system that carries a compressed internal model of the target system. This very setup implies a predictor with necessarily limited access, resolution, and computation (assuming that the “predictor” isn’t identical with the entire universe). Thus prediction, in this sense, can never be perfect or absolute, and in fact prediction error, “surprise”, or mismatch between model/expectation and measurement/perception is what enables a system to “learn”, i.e. update its model.
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