I'm asking a general question I think, but I couldn't find the answer myself. I have a system in which the next action depends on some variables, and those variables changes over time. At first I thought of modeling this as a markov chain, but I also need a variable set of probabilities to pass on each state.
Basically, I have 11 states in which I can be, and my probability to translate from state to another depends on the choices of all the other "players".
At the beginning, I start in one state, and at every instant, I look if too many players had choose my state. If so, with some probability I'll switch to another state and so on, until I found my best state.
Is that possible to to with a Markov chain or do I have to look for something else?
In addition, I'd like also to see when my chain gets to an equilibrium, that is, when no players change their state anymore.