Time travel, parallel worlds, and New Year resolutions

In a flashback, the main character (MC) dies in a car accident just as he is recognized as an ace in the pilot academy. 

The next moment, he wakes up as his 10-year-old self, together with his father, in an airplane that is being serviced by his estranged mother, who is the head flight attendant. 

He remembers that this was the doomed flight where he was the sole survivor and, just like Batman, was the reason behind his dream to become a pilot and prevent air accidents from creating orphans like himself. 

In the end, his reincarnation gives him a second chance to save his parents, meet his future mentor, and become a media sensation as the kid who saved hundreds with his retained knowledge as a pilot. 

The break during the holidays gave me an opportunity to watch movies and series suggested by social media algorithms. 

I managed to finally watch Chris Pratt in Tomorrow War and unashamedly binged on a few short Chinese drama videos. While unlikely to be nominated for the Oscars, the latter content served its purpose. A common theme of what I consumed was time travel.

Time travel is a popular trope in Asian content with plots ranging from second chances, hidden identity, revenge and saving the world. 

Time travel does not necessarily have to be technological, such as in Tomorrow War, where people in the future travel back in time to the present day to recruit soldiers to help fight a war with aliens (do not worry, no spoilers here).

In Chinese drama shorts, time travel can be through reincarnation or regression wherein you go back in time to a different era or to your past while retaining current memories and knowledge. The same advantage is also applicable in parallel worlds, where the main character does not go back in time but to a different world.

The reality, however, is that we cannot go back in time. Time moves forward and as much as we want to undo many decisions behind our current outcomes, we are bound by the laws of physics and must live with them. 

In economics, we acknowledge a similar limitation when we model existing economic data like GDP or inflation across a specific time period (this is also known as time-series data). 

Unlike cross-sectional data, like demographic data on genders, income, education, etc., time-series data does not have a population (the set of all data) on which a sample of the data can be tested. 

With no population for time-series data, we instead assume a concept called the data generating process (DGP), which is akin to all the time paths of the time-series data. 

The DGP is similar to the time stone in the Marvel universe that Doctor Strange uses to examine the millions of outcomes to find the one where they manage to defeat Thanos in Avengers Endgame. 

As we begin 2026, many will look at their current situation and draft new year resolutions. This is a useful exercise because even though time travel is not possible, we can still change the future. 

The best way to time-travel is to look at your current self from the perspective of your future self. In all the plots of these time travel movies, the objective is to change or transform. By the conclusion of Tomorrow War, Chris Pratt is no longer the high school science teacher that he was at the start. 

Admittedly, Chinese drama reincarnation shorts are unusual (for me) because the changes are absolute, i.e., no forgiveness or compromise despite lessons learned and remorse expressed. 

We also have a similar approach in quantitative economics, called backward induction, where you try to solve optimization problems from the end in a recursive approach. 

In other words, begin with the end state or position, make changes, take one step back, make more changes, take another step back, repeat until you get to the present. 

New Year’s resolutions should be the same. Ultimately, we want to change the future for the better, and a good start is making a list of changes that we need to do to get to our destination. This is how you can change outcomes without traveling through time.