Comment by wcrossbow
2 years ago
Nice article! The resulting Pareto front really highlights how hard game design is. You can get millions of possible combinations but the reality is that only a handful of them will ever happen in a competitive environment.
That doesn’t mean that the other combinations are worthless. Presumably there’s value in the cosmetics, plus the puzzle aspect of creating and optimizing the builds along the different dimensions. Surely there’s a meta-Pareto-front of the balance between usefulness of each combination in competitions and amount of fun it adds to the game!
Seconded.
As a game designer, you want to add silly and suboptimal things to your game. Don't fall into the trap of thinking that every decision must be perfectly balanced and equally weighty, because your game will turn out bland and textureless. And beyond the considerations of "optimal" play, putting suboptimal options into your game can serve as both a way of naturally selecting difficulty without having to implement ungainly difficulty sliders, and also accommodate fun/silly challenges for people who just want to mess around.
Keep in mind that the pareto front is not a two-dimensional line, but a surface in a some high dimensional vector space. In every game there are many, many aspects to min/max. As others pointed out even Mario Kart doesn't boil down to speed and acceleration.
In a sufficiently complex game every build is on the pareto front as it optimizes some specific cost function.
I was refering here to playing competitively, that is, playing with the only goal of winning. Of course, it is perfectly acceptable to play for style or to manage a podium with the worst configuration or anything you fancy [].
However, if your one and only goal is winning I suspect that the high dimensional vector space will end up not looking so high dimensional once you account for the correlations between the different features you use. This is already clear from the very strong correlation between speed and accelaration.
[]I myself have played MK64 a lot and sometimes the goal was simply to see the world burn, standing on a corner with a shell waiting for the what would've been the winner of the race. Fond memories.
> playing with the only goal of winning
Even if you're only optimizing for race times, in the case of Mario Kart, the choice of track will have a huge impact on the optimal kart selection. Tool-assisted speedruns pick different karts for different tracks.
The min/max cost functions make a lot of sense(easiest to visualize). It also can do a lot when you do multi-objective optimization. But I have always wondered how you go about evaluating other cost functions. I mean mathematically the concept is intuitive. i.e. just swap it for a quadratic or exponential, but which cost functions are useful in the real world?
Agreed! But it also depends on the goals for the game--- min-maxing isn't the only way to play and not everyone is super competitive.