So, now we can take a step back and summarize what we've covered in this lecture. We really motivated the entire thing with, YouTube videos and looking at ways that we may be able to viralize YouTube videos, or trying to figure out why specific videos become viral, why others do not. And in doing so, we saw that there is definitely a gap between theory and practice. But we were also able to define exactly what we do mean when we say viral. Is that the video is eventually going to reach a very high total view count. It's going to have a short time before it starts to ramp up and a long time that it ramps up for at least. We also looked at a co-visitation account, which is how YouTube does recommendation for similar videos. To, trying to figure out which videos to recommend based upon which one is currently watched. As opposed to other, more sophisticated algorithms like Page Rank and Netflix style. A movie recommendation which would not be very reliable in this case. And we also looked at ways of quantifying popularity, or just different classes of popularity and what makes items or in this case videos popular. First case being that it could have some inherent or intrinsic value to you, independent upon what other people think. And the second being that there may be some network effects going on. Whether that's a positive or a network effect. You know, in the case of a fax machine. We saw a positive network effects being something that has more value, the more people that own it. And also we saw information cascade being that you kind of ignore your own instinct about something and just follow suit with it. So that brought us to the main portion of the lecture where we drilled down into what we actually mean by information cascade and we took a look at sequential decision making specifically. And in looking at sequential decision making we saw the difference between public and private signals. And private signal being what you believe internally and public action being what you're actually going to release and show to the public. We also saw how you may ignore your own private instinct in favor of public action if you see enough of those public actions being displayed, which can then trigger an information cascade. It's either correct or an incorrect cascade. We took a specific look at the number-guessing thought-experiment, which is a model from the early 1990s in which we saw that such cascades can be triggered by only having two public actions being displayed as the same. In which the objective, again, the objective behind that was to guess whether the value was zero or one, and everybody would go up to a blackboard, be shown a private signal by a moderator, and then try to make a guess. We also saw how easy it can be in certain circumstances to break a cascade. If for instance, it, it could take only a group of two people, that we saw, each having the same private signal. And then one of them leaking what their private signal is, in order to reverse, or to break the cascade. And finally, let's look at the themes we saw in this lecture. The first was a network effect, right? So we, we took a look specifically at a positive network effect in a case of Wikipedia or a fax machine. Being that the value of something is going to increase or the popularity is going to increase based upon the number of people that use it. And we also saw a negative network effect as well in terms of negative or incorrect information cascades. We also looked at positive feedback, and we saw how as opposed to negative feedback that here we're responding to some public action. So, as we see more of an action we're more likely to do it, which then increases the amount of people doing the action, which then increases the likelihood the next person will come and do it, and so forth. As opposed to negative feedback where we're trying to go the opposite direction of some error signal. Finally, we saw that crowds are not so wise, in these cases, that's right and when we have the wisdom of crowds, we need independence, and we need unbiased guesses, but here, when all you, all the information's released to the public, nothing's independent anymore, and the wisdom of the crowds entirely breaks down. Thanks, see you in the next lecture. [BLANK_AUDIO]
