The Guguniverse

Predicting the past

A history timeline with a big hole in the middle

Retro-predictions were one of those ideas that appear seemingly out of nowhere and are so obvious, that it is surprising nobody thought about them before.

Traditional – or rather forward – predictions guess what might happen. The algorithm trains with billions of historized events, finding the common patterns between them and how events typically follow each other. It then uses those patterns to figure out what could happen after a particular situation.

Retro-predictions work similarly, but the algorithms train with sequences of events in reverse order. Instead of learning that someone leaves their home, then walks to the train station, then takes a train, the algorithm learns that someone takes a train after they walk to the station after they leave their home. Because the algorithm ignores the meaning of those actions, the order in which they occur is almost irrelevant; whether it knows that X happened before Y or that Y happened after X, the end result is the same. But training with events in forward order leads to forward-predictions, while learning in backward order leads to retro-predictions.

At first, the predictions from the algorithm were not very precise. Neither was it possible to use it everywhere. History had not been created with retro-predictions in mind, so its capacity to fill in holes in its information by looking backward was limited: It could barely see for a few seconds before any event, and the predictions were not especially good.

But even with those limitations, retro-predictions had a profound effect on people. They challenged the idea of time as an arrow flowing in one direction, where things in the future always depended on those from the past. If we could see events in the future as defining those in the past, what was the real difference between them both?

While philosophers spent their days discussing the essence of time, governments invested heavily in the new algorithms. In the West, by increasing their support for the History Company and the Human Data Corporation, which, at this point, had stopped fighting in favor of filling their coffers with public money. In the East, governments worked hard to ensure that predictions of the past could only show officially-sanctioned historical facts.

Among those interested in retro-predictions were law enforcement agencies, salivating at the idea of more algorithmic case-solving. They imagined a future where they could see the past, no matter how well someone buried it, and they set up labs to experiment with the new technology. Soon they came up with different ways to improve the predictions, for example, by combining them with their software for identifying individuals. This mash-up of predictive technologies increased their precision to uncanny levels.

While some welcomed the possibility of solving crimes without needing reliable witnesses or direct recordings, others grew restless. A group of civil rights organizations formed the Freedom to Forget coalition to prevent such a future from materializing. But the fear of an algorithmic police state drove thousands from the cities into low-population, low-historized areas.

As anthropogenic climate change made vast regions of Siberia habitable, groups of pioneers founded some of the first historized-free territories there. The settlements didn’t last long, as the permafrost’s melting released huge amounts of Anthrax into the air, and most settlers died after only a couple of years. Others managed to build permanent settlements in Northern Europe, especially in the fjords of Norway. Patagonia was another destination of choice, where it was possible to find locations hundreds of kilometers apart from existing settlements.

The privacy of these refugees, the first real proto-historics, was guaranteed for a while.