For those who don't know the model. You can read this pdf. I want to find what is the probability that 2 nodes are each others neighbors when the algorithm converges (i.e. when all nodes are happy).
Here's the model in a gist. You have a grid (say 10x10). You have nodes of two kind (red and green) 45 each. So we have 10 empty spaces. We randomly place the nodes on the grid. Now we scan through this grid (Exact order does not matter according to Schelling). Each node wants a specific percentage of people of same kind in its Moore neighborhood (say b = 50% for each red and green). We calculate the happiness of each node (a = Number of neighbors of same kind/Number of neighbors of different kind). If a node is unhappy (a < b) it moves to an empty cell where it knows it will be happy. This movement can change the dynamics of old as well as new neighborhood. Algorithm converges when all nodes are happy.
PS - I am looking for links for any mathematical analysis of the Schelling's model. I had posted this question on stackoverflow and was advised to post it here.
