To know the future is to change the future

Earlier this week I was stuck in traffic and was thinking about time travel (don't ask) and the time travel paradox--basically, if you travel back in time and prevent your Grandfather from meeting your Grandmother, you would never exist because your parent would never be born. At the same time, there was a discussion on the radio about social issues and how to break negative outcome cycles (like dropping out of school). So, naturally, I wondered what effect having knowledge of a likely future outcome would have on that future. I know, geeky, dorky and confusing all at the same time...

The current state of Predictive Analytics and Big Data is that Data Scientists study and manipulate data to create models in order to test hypotheses. So, it stands to reason that the better a model becomes at predicting future behavior, the closer we get to seeing into the future; in other words, predictive models are akin to the arcane art of predicting the future. Thus, according to the time travel paradox, changing something as a consequence of this knowledge would necessarily change the future and therefore break the model or invalidate the prediction. 

Let's come back to social issues like high school drop out rates. If we create a model to predict high school dropout rates it will help us determine what segment of the teenage population is at risk of dropping out. Now, if we instruct social workers to monitor and educate at risk teens about dropping out which (ideally) causes rates to drop, we will have broken the model as it no longer reflects reality and therefore we would no longer see into the future.

This is a little naive, of course, because statistical models can be adjusted, and SHOULD be adjusted, in an iterative fashion. In fact, if we take this adjustment into account along with the availability of real-time data streams, we would expect the resulting predictions to evolve, which would mean that no matter what action we take based on our model, it would always accurate (within statistical parameters, of course).

Just don't try going back in time. I advise against it.