In this video, I'm going to go over the nitty gritty details of this model. This involves a little bit of math, so it's an optional video and if you feel comfortable with math go ahead and watch it, but if you don't, then you can skip it. without really losing too much insight into how the model works. Here's the details. We're going to let N be the number of strategies each person has, And M be the number of weeks for which the attendance is known. Let t be the current time which is, that is, the current Thursday that we're predicting the attendance for, and the previous weeks, therefore are: t minus one, t minus two, etc. And we're going to denote the attendance at time t by capital A of t. Each strategy is going to look like this. So a strategy that's going to predict the attendance for week t, is going to be a hundred times a sum, where the sum is some weight w, which is some constant, times the attendance at the previous week plus some other constant times the attendance at the week before that, etc., all the way up to the memory limit, plus some other constant. So this is a very general, sort of linear, combination of previous weeks, times a hundred. And these weights, w sub i, are in the interval minus one to one. Each person has N such strategies, and what makes the strategies different from one another are the weights, and some of these weights can be zero, which means we ignore the data from that particular week, and the strategies are different from person to person. Now one of these strategies is determined to be the current best one. We'll denote that by S star, and the decision that each person makes is as follows: If S star of t, that is the best strategy that we've determined, I haven't told you yet how that's been determined, but I'll tell you in a minute, so if S of t is greater than the overcrowding threshold, which is some value like 60, then don't go, otherwise go. This S star of t is different for each person, but everybody uses this rule, with their own S star of t to decide whether or not to go. OK Moving on, To start off with, everybody's N strategies are initialized with random weights. Everybody is given an initial history so that they can actually start making decisions. The previous M time steps is initialized at random, with some value between 0 and 99. So you can make some predictions on the first M time steps. And, here's how the best current strategy is determined. So at each time step, after each person makes a decision, they then learn the current attendance at that time step, then they determine which strategy would have been the best predictor. This strategy will be used by that person on the next round. OK so how they do that is as follows: Each person's going to determine, for each time step between now and M previous time steps, what current strategy would have had the least error. The error is the difference at each time step, between the prediction made by a strategy, and the actual attendance. So this strategy S, has a difference between what it predicted this time and the actual attendance and what it predicted last week minus the actual attendance and so on, all the way to the memory limit. The best current strategy is the strategy S star that has this lowest error over all the strategies. So that's how the best strategy is determined. And that's it. In the next video, we'll look at a NetLogo implementation of this model.