Multiverse Decision Sciences — The Lessons of Elseworlds
Episode VI — Superhero Lessons In Analytics, Season 2
The CW’s annual crossover event is in the books. For better or worse, there are some lessons to be learned. Elseworlds, which featured a story line crossing CW’s Supergirl, Arrow, and Flash TV series, featured numerous instances of warped or changed perspectives and warped or flawed decision-making. Let’s break it down.
Superhero shows are always good for a little mind warping perspective. With the CW portfolio featuring multiple universes and numerous time travelers, it is doubly effective. For the crossover, they even inserted their own version of Freaky Friday — courtesy of a magic book.
Switching Barry and Oliver was an interesting plot twist. It is certainly not the sort of thing you can model in the real world. Proof of that slipped into the show where Grant Gustin (normally playing Barry Allen) was hard pressed to deliver on the fight sequences typical of Arrow. Later, it threw logic for a tumble when characters who supposedly retained their own consciousness suddenly had each others delusions… but we digress.
This sort of extreme shift in perspective would be an incredible tool for someone tasked with analytics. The ability to see things from a new point of view can lead to a great deal of discovery. On the other hand, it also decimates your benchmarks or at least it alters them. The series did a great job of drawing humor from those changes.
The series also offers another word of warning. In analytics, when perspective is altered and benchmarks are strained, young analysts often fall victim to warping their definitions as well. Is he Superman? Or do we have to call him Clark or Supergirl’s cousin? Wait, now there are two? Did someone say Bizzarro? It can all get very confusing. And if you want to be the hero, why dress in black exactly? Maybe he wanted to be the anti-hero? Those are cooler anyway!
In general, using varied perspectives is helpful to the process of decision-making. But the supporting cast of our two intrepid heroes weren’t given new perspectives — they were forced into altered ones. Against their new benchmarks, Arrow & Flash were behaving rather oddly. In analytics, we would call them outliers. Decisions built from outliers, especially when you are not aware they are outliers, can be problematic.
The Flash team resorted to throwing the duo in prison. I suppose that is one way of removing outliers. At least we now know the meta prisons have a toilet…
The Arrow team resorted to “bringing through the unknown super powered entity from another universe while all the highest powered heroes are away”. While it actually worked out, that decision made the earlier one look good. Many analysts have done similarly stupid things in a frustrated rush to get answers. Similar is very relative.
It is important to note that not only do outliers affect decisions. They can really mess up models. Of course, the duo was back to normal at the point where someone decided that running around the world in opposite directions at Mach 7 would stop time. Running at Mach 7 also leads to certain doom, by the way… unless it doesn’t. Maybe it was stopping time that caused certain doom? Determining causality is hard enough without using outliers!
Altering your perspective can give you real insight. It can set you up for future success and provide you with powerful new paths. In Elseworlds’ terms, Batwoman, Superman, and Green Lantern just to labor a final analogy.
But altered perspective runs risks. Be wary of changing benchmarks and definitions. Stay alert to outliers. Avoid basing new decisions on data that you don’t have the proper perspective on quite yet. It can ruin your models. Worse still, it may cause you to replay the story line from… well, every first Superman movie ever. Thanks for reading!
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