There’s this idea in sports that certain people are “ruining the game”. It’s those baseball people who favor home runs and defensive shifts. It’s the golfers who drive for show and dough.
And we can blame computers.
And us. We’re to blame too.
Computers compress time. I could have mailed this to you as a letter but that would take me buying paper (after a trip to the store of course) writing it…yada yada yada…and you walking to the mailbox. Computers compress all that.
Analytics is a type of compression. Rather than a lot of people and a lot of time to learn about the advantages of home runs or infield shifts in baseball or long drives in golf, a few people with computers thought it might work and ran the data.
This is an issue we will see more of: novel data making interesting predictions.
“We looked on Twitter for anyone who announced they were going to their first AA meeting and we followed what they tweeted after that. Did they stay sober for ninety days or did they go back to drinking? Did they complain about being hungover at work? Did they celebrate their sobriety? Then we took all the data we could model from their Twitter feeds to try to predict if they would be sober. Things like: who do you follow, do they talk about booze, are you over 21, how do you cope with stress? We can predict with 80% accuracy if someone will stay sober or not on the day they decide to go into treatment.” – Jen Golbeck, November 2020
This algorithm, Golbeck notes, is also pessimistic, it tends to say you won’t recover when you will. And it’s confounded by the sample: only certain people announce things on Twitter.
These algorithm approaches will grow in the decision making blend. Part-of-that means understanding the tools. We are time traveling, leaping to the future rather than walking there.