From the transcript of the Devstream:
I'm actually in the middle of developing a design for a mobile football game and so I've had to deal
with some of these issues. I'm dealing with them right now and it's a difficult question, because on the one hand, simulation for process tends to allow a more open solution. What simulation for process is, you set up a series of rules to simulate the events, so in the case of a sports game you set up the various rules for how the players are gonna play, you set up the various rules for what the different variables are going to be and how they're going to interact with each other, and then you just step back and you let it run.
Believe it or not, this is actually in some ways an easier approach, because you know you're not responsible, you know you're not trying to dictate any one result. Yeah, you just put in the ingredients, you mix them up and what happens happens. Now that's the way that you would have seen the old statistics in the Madden football games, for example. You know when you do that, when you use simulation for process, you almost always have a situation where the results are not going to be realistic. The process is complicated and it is intrinsically inaccurate. It doesn't matter whether you're talking about an AI attempt to replicate human intelligence, whether you're talking about an attempt to replicate an infantry firefight, or whether you're dealing with something like a football or soccer game, in all of those cases you're dealing with multiple layers of abstraction, and every abstraction, every assigned variable is going to be different than the real world
Even if you build a very complicated model using very accurate statistics, the small errors, the small
differences, are going to multiply so that by the time that you get to the end result, you're not going to end up with very realistic numbers.