“For example, in the case of the alert and bored students. People are moving from alert to bored. But if I think in terms of probabilities, that probability is staying fixed. That probability is staying fixed at 5/9. People are moving around, but the probability’s staying fixed. That’s why this is sometimes called a statistical equilibrium, ’cause the statistic p, the probability of someone being alert, is the thing that doesn’t change. Okay, pretty involved, right? What we did is, we wrote down the Markov transition matrix. And we showed how using that matrix, we could solve for an equilibrium. And we saw, at least in the simple example of alert and bored students, that the process went to an equilibrium, and it was fairly straightforward to solve for. What we want to do next is we want to do [a] slightly more sophisticated model that involves multiple states instead of just two, involves three states, and we’ll see how that process also converges to an equilibrium. ” – Transcript from Scott Page Coursera
Source:
Scott Page Model Thinking MOOC Course
