Optimizing
In 1996, two behavioral ecologists (scientists who study the evolution of behavior), Robert MacArthur and Eric Pianka, published a paper on Optimal Foraging Theory (OFT). The point of the theory was to predict how an animal would behave when foraging. The model essentially predicts when an animal should move between patches of food to maximize its energy intake. In the decades since its introduction, the model has been built upon and studied in the lab and in the wild repeatedly.
This model was so fascinating to me as a young undergraduate that I went on to study behavioral ecology in birds during both my undergraduate and graduate degree. And the theory left a lasting question in my mind.
What is optimal?
I want to explore this question from a very different context. Possibly more relatable than birds from the 90’s: video games.
It’s a known fact that I enjoy the online competitive video game, League of Legends. A close friend and I can typically be found online in the evenings, playing a few games of League and chatting about social concepts, politics, our cats, mathematics, and computer science.
It is not uncommon to find other players offering unsolicited advice on how to play during a match and I find it “diet annoying”. Not annoying enough to quit playing, but annoying enough to make a sarcastic comment about it to my friend while we play. This unsolicited advice doesn’t feel useful to me and the underlying explanation lies in understanding what optimal is.
The problem with other people offering advice is that they assume they know what my goal or objective is and what the optimal strategy is. Many players in the US watch talented professional players (e.g. from the Korean team, T1) playing one way and determine that it’s the optimal strategy. The reality is, it’s the optimal strategy for that team and their goal, to win world championships. For full transparency, I am not a world champion gamer. I also think it’s boring to play the game one way. To never try anything different.
Let’s do some quick math to illustrate. In the game, each player picks a “champion” who has six “item slots”. These are kind of like weapons that help make your champion stronger. There are 71+ options for items.
That’s over 143 million possible combinations of items. That’s not even considering all the champion options (171 champions to choose from).
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Another example of this, if you like sports, comes from the movie Moneyball. The accuracy of the film itself has been challenged because The A’s had some powerhouse players that weren’t accounted for in the film and because several of the players were recruited using traditional scouting methods. However, that doesn’t detract from the point I’m about to make.
Billy Bean’s goal wasn’t just to build a winning team. It was to build a winning team on a budget. The optimal scouting strategy wasn’t to just find the hottest players in the league, he couldn’t afford them. Traditional scouting methods looking at batting average, home runs, and speed weren’t going to get his team to his goal. So, with the support of an analyst, Paul DePodesta, they changed their scouting strategy and looked for players with a high on-base percentage, who were more affordable because other teams weren’t looking at them.
The Oakland A’s went on to have a 20 game win streak and ended the season 103–59, an impressive record.
So what is optimal? It depends.
When we talk about an optimal strategy or “optimizing”, being explicitly clear about the goal/objective is essential. When we assume we know what’s “best” for someone else, we’re making a huge leap. Massive leap. Sometimes trying to jump the Grand Canyon. We’re going based on information we mostly have on ourselves. Our past experiences, our successes and failures, our knowledge of the world. But what we need is information and ultimately understanding of others.
Optimizing requires empathy.
In order to tell someone else what’s “best” for them you have to understand what they’re aiming for. You have to understand them. And arguably to understand someone, you have spent a lot of time with them, you have asked them questions, you have ultimately respected their agency to be their own unique person.
That includes acknowledging there are some aspects of their lives we may never understand. So can we truly offer advice on what’s best for them? Sometimes we must trust that they know themselves and what they need better than anyone else and listen to that.
And in order to know what’s best for yourself you have to understand what you want and filter the world through that lens. It helps to remember that all of the information put out about the best way to do anything isn’t specific to you.
In ongoing research of Optimal Foraging Theory, scientists expanded beyond optimizing maximum energy intake to survival. They realized that animals are also trying to avoid being eaten. Eating food even if a bigger animal is about to eat you isn’t what’s “best” or “optimal”. So now what’s optimal?
And the answer is still, it depends.
If you want to be a scientist, college might be your optimal strategy. If you want to gain muscle, weight training and a high protein diet might be your optimal strategy. If you’re trying not to get eaten by a bigger bird, grab a few quick berries and return to your tree hole.
There are so many variables in the universe. Way more than 143 million. There is no one optimal way to be, and that’s the fun part.
