So now, let's dive back into the code. But we have this other idea. I just introduced this idea of playing for the max. That's when you're not looking to date, when you're looking for a long-term relationship; when you're not looking for gigs, when you're looking for a career. In those situations, we have this number, we put 100. I'm going to change the mean to max. It's another function in that log. You'll see it change color from black letters to purple letters. That means NetLogo knows what that word means. We're going to take the maximum of 100 draws from 1-10. The maximum of 100 draws from 79. What are we going to find? It's always better when you're playing for the max and you have 100 attempts than to take the risky bet. Well, that's 100 attempts. What about just for two attempts or just one attempt? I don't know. When I'm just taking one attempt, it seems still better even if I'm playing for the maximum. This is the maximum of one number. So better to take the safe bet. So you can play around with it, so you can keep plugging different numbers in. When you play around long enough, you'll find that the magic number is if you have more than nine dates, more than nine interviews, if you're willing to put in that big of a search for these numbers, what you're going to find is that that's when it tends to be better to take the risky bet. All right. How does this help you? Well, maybe you have a perfect experience. Maybe your safe bet isn't at seven, eight, or a nine. Maybe it's a five, a six, or a seven. All you have to do is change this one number from seven to six. Now you're playing a totally different game; you're modeling your own situation. So I could do the pencil-and-paper math to show you that the threshold is a. I could give you the formal solution or you can just play around with numbers and experience and get a sense of what comes out better for you and you'll find exactly how many times you have to try for the risky bet to be the best bet for you. Okay. Surfacing. We just saw that the numbers are totally different when I'm playing for the max, but suddenly, I should take the risky bet. Of course, that was when I set the slider to 100. When I set the slider to the one, I should maybe still play it safe. Well, what do you think? What's that number in the middle? What's the sweet spot? I don't have all the time in the world to go on job interviews, or to try every restaurant in Davis, or to go out with everybody, I can't spend all the time in the world, but I've got a little bit of time to look around. What do you think is that number? I don't think it's 50 because it's only 10 numbers. I think it must be bigger than three. Well, it turns out if you go into the pencil and paper you do the math, you want for this environment, for this system, for this model, any situation where you're going to go on more than eight tries, more than eight dates, more than eight interviews, more than eight restaurants and you're playing for the max, you want to take the risky choice. So breaking it out right here, when should you take the safe bet, when should you take the longshot, that's equivalent of saying that we've got your gig hunting through friends, gig hunting through Craigslist. That's scary. But if you'd have enough experiences on Craigslist and you're playing for the max, then you're bound to find the better job, the better experience from this risky distribution. Even though most experiences are worse, it's the risky environment that has that small tiny chance that just something better than you could have gone anywhere else. Now, so we face risky decisions, when should I go one direction, when should I go the other? Well, when you take away benefit from the life experience is the need of many tries, then you should be risk-neutral. You should take the safer bet. You shouldn't go out of your way and expose yourself through this. But, when your benefit is the maximum of any tries and you have more than a few tries, you should bet on the place that offers the highest maximum regardless of what its averages, regardless of how awful its worst experiences are. Now, this is a corollary. We've been talking of playing for the mean, playing for the max. There's also life decisions where you're playing for the mean. So squirrel suits are a fine example. Incredibly risky sports, incredibly risky endeavors. Things where you're going to have a great time most of the time. You'll have an amazing time most of the time, but there's a small probability that what you'll walk away from is the worst of all of your experiences. In the case where you're playing for the mean, where you're exposing yourself to tremendous amounts of risk for something that's usually very rewarding but sometimes awful, this is the time to be risk-averse, to avoid risks and to take the safer bet. Okay. So we just pulled this off. Somehow we did the strangest thing. We wrote a couple lines of code, we threw away all of those detail that's really important to the most important decisions in your life, and we came away with the opposite of what you'd expect, where you you might have approached the most important decisions of your life with a little bit of caution and only let yourself fly on risky behavior where the easier lower-stakes decisions, now we're walking away with a little bit the opposite conclusion that in cases where the experiences you're going to walk away from after many draws, after many attempts, are the best of all those draws. You should roll the dice. You should be risk-seeking. This is the core of modeling. We threw away a lot that's important about scary decisions. In the process, we distilled the essence of risk and we made all these very different seeming situations in very much under the same framework. In the process, we will find a general principle that makes us better at thinking. So you're going to keep doing this type of thinking to learn things about the way society works to continue to collect facts and frameworks for understanding human social systems that you can follow the exact same process to get tools for yourselves to approach problems in a more intelligent, more thoughtful, more reflective, more insightful manner. Thank you very much for your time. What do I say? You'll find me here on campus at UC Davis. Feel free to check out my classes, my birthday. Oh, really? Okay. Yeah. I put my contacts [inaudible]. You see that, right? Here. Okay. Yeah. That's right. Boom. Let's see. So this is a framework we're going to follow. We threw away all this detail to model thinking and to help you be a better reasoner. You've already been doing the exact same thing to help you understand human social systems is to make simple models. That's all I have for you. It's very good to talk with you. You can find me here at UC Davis. My contact information is here if you're interested in anything I'm doing. Thank you very much for your time and I hope you have a good time.
