So are you ready for Sugarscape 13-87? No, I'm just kidding. Let's keep it Lucky 13. It's going to be the last one I promise. Now we're going to start to throw different things together. They call this one E and Weaver who programmed this calls it life on the brink. But we do now, we keep our two resources, we keep our trade, and we add sexual reproduction. Now imagine I already said it's tough to multitask. It's a pretty hard life to multitask with two resources. Now imagine you also have to concentrate and focus on sexual reproduction on top of it. Wow, well, it might be easier for some of us than for others. But let's see how our agents of Sugarscape can deal with so many demands. All right. We set it up and we go, we start with our 400 agents and we see they're falling. Yes, they are reducing down here and we can see also. Wow! Now who they reduce quite a bit and we killed them. Wow, now we really killed them. That's actually, probably they ran out of sugar. That's what we see here. Okay, well that is life on the brink and we fell off the brink. So let's try to do that again and see if the random initial conditions can save us. This time can we make it? Yes, we're going down and we're going down and we're going down and we are pretty much again extinct. Let's try it again to see if I was set up and what is to be expected with sexual reproduction that we reduce the amount of agents at the beginning. Because we have these predator-prey Lotka-Volterra dynamics and if you overpopulate the population and you don't have enough resources, you can kill yourself. Oh now look at that. Now look at that, we bounced back and we're going up, we're going higher, and we're going higher. The prices will stabilize as there are more people. We have more trade going on. Let's increase that a little bit and see how high we can get. Yes, we probably will get. We see it slows down, probably will get into our cycles, our sexual reproduction Lotka-Volterra cycles. Let's see if we can put an inheritance mechanism in. Let's put the inheritance mechanism in and see if we can change the course of history. Just like a government we change the policy, we guide society into a different direction, and yes we caught it. They should re-elect us. They should really re-elect us. We really did this inheritance policy, really made it work and we increase our population. Fantastic. Fantastic. We also have since we have the trade we have kind of like the average, Joe, right? You can help the specialists. They're all black here and we can see it's going pretty well. Now in reality, we have other things as well. For example, we have pollution. Let's see, do we have pollution here? No, no pollution at all. Let's turn on the pollution. Remember that spice is a clean resource, sugar is the only one that's polluting. So over here we can see with pollution while it stops it stops our victory march a little bit and as we pollute our sugar region here. Yes, the pollution doesn't really help. You see also the price of sugars being destroyed, spices becoming more valuable because nobody wants to go and get some sugar, right? Actually yeah, spice is the clean resource higher-valued now. It's because the population adjusted to it. What else could we do, what else could we play with? Oh, seasonal settings. Let's see if we do some seasons here. We see with the seasons, now everybody is over here and your shortest seasons, now everybody's down here. Well, the seasons too have an effect. Maybe we are not quick enough to adjust for that. Our pollution maybe should stop that, a green energy law maybe that might help our society and we are still falling. Because maybe be adjusting to the seasonal change, climate change maybe. Wow! Now we have a season you have to adjust to it. We have to reallocate our population to different places and let's see. Oh yes, so we are now all down here and now we have to go all the up here. Price stabilized their population is- Anyways, I think that's all I wanted to show you. The idea is clearly not that you have to memorize all other what happens, but I wanted to show you it's just as crazy as reality what we can learn from different agent-based models.