“Whatever else they simulate” writes Dr. Steven E. Jones (who also happens to be our professor for the Digital Humanities course), “video games necessarily simulate the relation between human and computer.” Dr. Jones contends that games provide a vital negotiation model between human and computer in our everted world.
It seems to me that games can provide the opportunity for computers to learn about us and us about them, and I mean in the good way, not the creepy Sci-Fi way that ends up with people getting murdered and roving tribes of refugees scavenging parts to assemble the Great EMP which will allow humanity to survive. This learning can take the form of targeted marketing on social media platforms, or programs adapting to how we use them, and even in learning our preferences.
I was faced with this in the baseball sim that I’m currently playing and using as part of the Baseball VFW Project. In this particular sim, you can serve as both the GM and field manager of your team. There are AIs for everything in this game—running your minor league teams or your whole organization, a bench coach to help you with in-game strategy, an assistant GM to help you with trades and other organizational stuff, and a Scouting Director who helps you evaluate talent. I found one of them figuring me out.
One of the sims I started was a post-expansion draft in 1961. I’m now in the off-season before 1983 begins. One of the things the Scouting Director does during the annual first-year player draft (the kids coming out of high school and college) is suggest who you might want to pick next. The first ten rounds or so of the draft are pretty interesting, but then the players aren’t all that great, and you’re just picking over the bones of players who are never making it to the bigs anyway. At any point in the draft, you can just put it on auto and the AI will complete the draft for you.
About 10 or 12 seasons ago, I noticed something. Many of the players who the AI was suggesting for me were on the list I had come up with for myself before the drafts began. I would go down the list, look for particular attributes, and reference the list during the draft. As more time passed, the AI stopped suggesting just the best player still available, and started suggesting the ones that were most like the kind of player I like in the organization (focusing on their batting eye, speed, and defensive ability, as opposed to other attributes like power or stamina). It’s not anywhere close to 100%, but the ratio has improved too significantly to be random. The game knows what I like, even if its internal evaluation is different.
Conversely, I’m learning about what the AI values. There have been times when I’ve drafted players who the Scouting Director recommended, just to see what would happen. Like with all sports talent evaluation, it’s an inexact science, but I’ve come to learn why my particular Scouting Director (each has a coded personality) thinks highly of some players and not others. Its particular kind of thinking has led me to choose players I might not otherwise have, and many of them have done reasonably well. Our minor league organization has been the #1 ranked for more than a decade and we’ve built quite a dynasty; we’ve won 4 of the last 5 World Series.
Winning the Series means popping the champagne, so this week’s cocktail integrates a sparkler.
The Bubbling Smash
The Bubbling Smash is a take on a classic fruit smash. The only ingredients you need are
1.5 oz. good bourbon
½ an orange
Sparkling wine, such as prosecco or champagne
½ tsp raw sugar
In a cocktail shaker (never the glass), muddle the pulp of the orange with a bit of crushed ice and the sugar. Fill half the shaker with crushed ice and pour in the bourbon. Shake well, and strain into a martini glass. Fill the glass the rest of the way with the sparkler. Garnish with an orange peel.