If all variables of a system was known, would stochastic modelling be redundant? Statistical parametric methods such as regression can be used to predict a particular dependent variable using a pre-defined set of covariates. For example, a linear regression model used to predict merchandise sales.
If all covariates that cause true value of the continuous dependent variable to change was known, more specifically if an event could be deterministically modelled, then would using parametric modelling techniques to represent such event be unnecessary since a deterministic model would always yield an accurate prediction for any observation?
Thus, in predictive modelling, should more time be devoted in collating and gathering as much relevant features to increase the accuracy of a model rather than trying to optimise a model based on a given pre-defined list of independent random variables?

It's a philosophical one. Are you ever able to observe all the causes? Say that you are modeling sales in a local grocery store. There are some obvious factors that would influence sales, but they do not explain all the variability. Say a severe snowstorm hits, so people are not able to commute to the store, and sales drop. There is an economic crisis, or pandemic, that makes people's behavior change. Maybe there was opened another store nearby and they have attractive prices this week to attract customers. Or maybe they are renovating the main road leading to the store, so your customers are preferring other stores to spend less time in traffic jams. To model sales in the local grocery store, would you collect all the data on all the economic, social, weather, political, medical, etc conditions related to all the possible customers of your store? I doubt and most likely you won't be able to collect all the data, even if you tried. You need statistical models because it is impossible to control for all the factors that would influence the phenomenon you are studying, and there is also a measurement error to account for.
