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Mario meets Pareto

mayerowitz.io

121–130 of 184 posts

Re: Mario meets Pareto

#122

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.

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.

Re: Mario meets Pareto

#123
Really cool analysis and visualizations! Although there’s an interesting wrinkle with Mario Kart 8 + 200cc - most players don’t want speed over a certain level because you’re too fast to control. So rather than trying to maximize absolute speed stat you may want to minimize the delta from your optimal speed stat

Re: Mario meets Pareto

#124

If I'm optimizing for 'weight' is it supposed to be heavier or lighter? At the moment it seems the more weight I put on 'weight' the heavier the build is.

Pretty sure weight mostly affects how badly you're knocked by other players when they hit you. So heavier is better (although it's often correlated with slow acceleration or poor handling). You want an EV... heavy as hell but with amazing acceleration.

Re: Mario meets Pareto

#125

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.

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.

Re: Mario meets Pareto

#126
> A portfolio with low risks and high returns? [...] Of course, if you already know the exact weights

Or! In this case, it reduces to a one-dimensional optimisation thanks to the structure of the problem.

What we're optimising in portfolio selection is not the return of a single investment, but of a lifetime of investments. And thanks to compounding, that is a function of both risk and return. So we can find the optimal allocation without making any tradeoff: https://two-wrongs.com/the-misunderstood-kelly-criterion.htm...

Re: Mario meets Pareto

#128

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.

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.

Re: Mario meets Pareto

#129
post #37

I always knew those little red tires were the best. Sadly, this misses the most important thing to me: style. And my love of Zelda. So I'm afraid I'll personally have to disregard all of this.

You're just optimizing for a different outcome. You could still apply this thinking, just with "Zelda adjacency" as the primary metric.
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